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make_run_order.py
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make_run_order.py
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import pandas as pd
import re
import numpy as np
raw_file_dest = "D:\\Data\\Test\\You Forgot"
group_prefixes = [
"K562_HCD30",
"HeLa_HCD30",
"HeLa_HCD20",
"K562_HCD20",
"Blank"
] #these goes into the raw file name
channel_prefixes = ["_CH1_","_CH2_","_CH3_"] #this too
group_methods = [
["channel_HCD30_1.meth", "channel_HCD30_2.meth","channel_HCD30_3.meth"],
["channel_HCD30_1.meth", "channel_HCD30_2.meth","channel_HCD30_3.meth"],
["channel_HCD20_1.meth", "channel_HCD20_2.meth","channel_HCD20_3.meth"],
["channel_HCD20_1.meth", "channel_HCD20_2.meth","channel_HCD20_3.meth"],
["channel_HCD30_1.meth", "channel_HCD30_2.meth","channel_HCD30_3.meth"],
]
INJ_VOLUME = 1 #this is not really used for us
group_sample_wells = [
["RA3", "RA4", "RA13"],
["RB2", "RB5", "RB11"],
"Hela_hcd20_wells.csv",
"K562_hcd20_wells.csv", #if you put your wells in a csv, with a column called "Position", it can read that in
"7@R5" #if always the same location put the number and location a string separated with an @
]
group_suffixes = [
"",
"",
"",
"",
"numerical"
] #numerical is a keyword that will use "_" and the rep number,
#use "" for no suffix
OUTPUT_FILENAME = "output.csv"
Block_over_time = True
Randomize = True
Block_between_channels = True
################################### Don't change anything beyond this point ##################################
# Define functions
#Block over time
def add_blocks(table):
num_samples = table.shape[0]
new_table = table.copy()
new_table["Block Width"] = 1/num_samples
new_table["Block"] = range(0,num_samples)
return new_table
#Randomize only
def add_randomizer(table):
num_samples = table.shape[0]
new_table = table.copy()
new_table["Block Width"] = 1/num_samples
new_table["Randomizer"] = [np.random.rand() for x in range(num_samples)]
return new_table
def format_tables(tables, randomize, block):
formatted_tables = []
for each_table in tables:
current_table = each_table.copy()
if block:
current_table = add_blocks(current_table)
if randomize:
current_table = add_randomizer(current_table)
formatted_tables.append(current_table)
return formatted_tables
#Reorder
def reorder_table(table):
#Both were done
if "Block" in table.columns and \
"Block Width" in table.columns and \
"Randomizer" in table.columns:
table["Order"] = table["Block"] * table["Block Width"] + \
table["Randomizer"] * table["Block Width"]
elif "Block Width" in table.columns and \
"Randomizer" in table.columns:
table["Order"] = table["Randomizer"] * table["Block Width"]
elif "Block Width" in table.columns and \
"Block" in table.columns:
table["Order"] = table["Block"] * table["Block Width"]
table = table.sort_values(by="Order").reset_index(drop = True)
return table
#remove extra columns
def tidy_table(table):
for each_extra_column in ["Order","Block","Block Width","Randomizer"]:
if each_extra_column in table.columns:
table = table.drop(each_extra_column, axis=1)
return table
#check you did it right
if len(group_prefixes) > len(group_sample_wells):
print("Missing sample wells")
exit()
elif len(group_prefixes) < len(group_sample_wells):
print("extra sample wells")
exit()
i = 0
for each_groups_methods in group_methods:
i = i + 1
if len(each_groups_methods) != len(channel_prefixes):
print("not enough methods for group {i}.")
exit()
if len(group_prefixes) != len(group_suffixes):
print('wrong number of suffixes\nit should match the number of prefixes, fill with "".')
tables = []
j = 0
for each_prefix in group_prefixes:
current_wells = group_sample_wells[j]
current_type = type(current_wells)
num_samples = len(current_wells)
if current_type == str and "." in current_wells: #file
current_data_frame = pd.read_csv(current_wells)
elif current_type == str and "@" in current_wells: #single well
num_samples = int(current_wells.split("@")[0])
current_wells = current_wells.split("@")[1]
current_data_frame = pd.DataFrame(data={"Position": [current_wells for x in range(num_samples)]})
elif current_type == list:
current_data_frame = pd.DataFrame(data={"Position": current_wells})
else:
print(current_wells)
well_container = re.sub(pattern="[0-9]+",repl="",string=current_data_frame["Position"].tolist()[0])
if len(well_container) == 1: #buffer
current_data_frame["Sample Type"] = "Blank"
if len(well_container) == 2: #wellplate
current_data_frame["Sample Type"] = "Unknown"
current_data_frame["File Name"] = each_prefix
current_data_frame["Path"] = raw_file_dest
current_data_frame["Inj Vol"] = INJ_VOLUME #injection volume
tables.append(current_data_frame)
j = j + 1
if Block_between_channels:
num_channels = len(channel_prefixes)
#split into channels
channels = []
for each_channel in range(num_channels):
channels.append([])
for each_table in tables:
num_rows = each_table.shape[0]
sliced_table = each_table.iloc[[x for x in range(num_rows) \
if x%num_channels == each_channel],:]
if sliced_table.shape[0] > 0:
channels[each_channel].append(sliced_table)
#format and combine each column
formatted_channels = []
for each_channel in channels:
formatted_tables = format_tables(each_channel, Randomize, Block_over_time)
combine_table = pd.DataFrame()
for each_table in formatted_tables:
combine_table = pd.concat([combine_table,each_table])
formatted_channels.append(combine_table)
#redistribute
current_distribution = [x.shape[0] for x in formatted_channels]
samples_per_channel = int(sum(current_distribution)/num_channels)
leftover_samples = sum(current_distribution)%num_channels
desired_distribution = [samples_per_channel for x in range(num_channels)]
for x in range(leftover_samples):
desired_distribution[x] += 1
for x in range(len(formatted_channels) -1):
final_index = desired_distribution[x]
formatted_channels[x+1] = pd.concat([formatted_channels[x+1],
formatted_channels[x].iloc[final_index:,:]])
formatted_channels[x] = formatted_channels[x].iloc[0:final_index,:]
#reorder
sorted_channels = []
for each_channel in formatted_channels:
sorted_channels.append(reorder_table(each_channel))
#intercalate samples from each column
final_table = pd.DataFrame()
for each_set in range(samples_per_channel):
for each_channel in sorted_channels:
current_table = each_channel.iloc[[each_set],:]
final_table = pd.concat([final_table, current_table])
if desired_distribution[0] > samples_per_channel:
for each_channel in sorted_channels:
if each_channel.shape[0] > samples_per_channel:
current_table = each_channel.iloc[[-1],:]
final_table = pd.concat([final_table, current_table])
final_table = final_table.reset_index(drop=True)
#rename filename and add method
for index, row in final_table.iterrows():
old_filename = row["File Name"]
current_channel = index%num_channels
current_suffix_index = group_prefixes.index(old_filename)
current_suffix = group_suffixes[current_suffix_index]
#see if suffix is sample number
if current_suffix == "numerical" and "Block" in row.keys():
current_suffix = "_" + str(row["Block"] + 1)
new_filename = old_filename\
+ channel_prefixes[current_channel] \
+ row["Position"]\
+ current_suffix
final_table.at[index,"File Name"] = new_filename
final_table.at[index,"Instrument Method"] = group_methods[current_suffix_index][current_channel]
else:
#format tables
formatted_tables = format_tables(tables, Randomize, Block_over_time)
#combine tables
combine_table = pd.DataFrame()
for each_table in formatted_tables:
combine_table = pd.concat([combine_table,each_table])
final_table = reorder_table(combine_table)
#rename filename and add method
for index, row in final_table.iterrows():
old_filename = row["File Name"]
current_channel = index%len(channel_prefixes)
current_suffix_index = group_prefixes.index(old_filename)
current_suffix = group_suffixes[current_suffix_index]
#see if suffix is sample number
if current_suffix == "numerical" and "Block" in row.keys():
current_suffix = "_" + str(row["Block"] + 1)
new_filename = old_filename\
+ channel_prefixes[current_channel] \
+ row["Position"]\
+ current_suffix
final_table.at[index,"File Name"] = new_filename
final_table.at[index,"Instrument Method"] = group_methods[current_suffix_index][current_channel]
#make column method and add channels
final_table = tidy_table(final_table)
#Export
# write the header
header = 'Bracket Type=4.\n'
with open(OUTPUT_FILENAME, 'w') as fp:
fp.write(header)
# write the rest
final_table.to_csv(OUTPUT_FILENAME, header=True, mode='a', index=False)