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Copy pathRexcel.py
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257 lines (225 loc) · 11.6 KB
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import pandas as pd
import xml.etree.ElementTree as ET
class Rextractor:
def __init__(self, file):
self.amplification_data = []
self.HRM_data = []
self.count_ct = []
self.temp_value = []
self.minxpoint = None
self.startx = None
self.stepx = None
self.master_ct = []
self.master_hrm = []
if file is not None:
self.data = file.read()
# Parse the XML content
self.Element = ET.fromstring(self.data)
def Amplification(self):
if self.Element[19].tag == "RawChannels":
for i, Raw_channel in enumerate(self.Element[19]):
if i == 0:
if Raw_channel.find("Name").text == "Cycling A.Green":
self.count_ct = [i for i in range(int(Raw_channel.find(
"StartX").text), int(Raw_channel.find("MinXPoints").text)+1)]
readings = Raw_channel.findall("Reading")
for i, reading in enumerate(readings):
reading_data = reading.text
reading_data = reading_data.split(" ")
reading_data = [float(value)
for value in reading_data]
self.amplification_data.append(reading_data)
for count in range(len(self.amplification_data)):
self.master_ct.append(self.count_ct)
elif self.Element[18].tag == "RawChannels":
for i, Raw_channel in enumerate(self.Element[18]):
if i == 0:
if Raw_channel.find("Name").text == "Cycling A.Green":
self.count_ct = [i for i in range(int(Raw_channel.find(
"StartX").text), int(Raw_channel.find("MinXPoints").text)+1)]
readings = Raw_channel.findall("Reading")
for i, reading in enumerate(readings):
reading_data = reading.text
reading_data = reading_data.split(" ")
reading_data = [float(value)
for value in reading_data]
self.amplification_data.append(reading_data)
for count in range(len(self.amplification_data)):
self.master_ct.append(self.count_ct)
return self.master_ct, self.amplification_data
def HRM(self):
if self.Element[19].tag == "RawChannels":
for i, Raw_channel in enumerate(self.Element[19]):
if i == 1:
if Raw_channel.find("Name").text == "HRM A.HRM":
self.minxpoint = Raw_channel.find("MinXPoints").text
self.startx = Raw_channel.find("StartX").text
self.stepx = Raw_channel.find("StepX").text
self.temp_value = [
int(self.startx) + i * float(self.stepx) for i in range(int(self.minxpoint)-1)]
readings = Raw_channel.findall("Reading")
for j, reading in enumerate(readings):
reading_data = reading.text
reading_data = reading_data.split(" ")
reading_data = [float(value)
for value in reading_data]
self.HRM_data.append(reading_data)
for count in range(len(self.HRM_data)):
self.master_hrm.append(self.temp_value)
elif self.Element[18].tag == "RawChannels":
for i, Raw_channel in enumerate(self.Element[18]):
if i == 1:
if Raw_channel.find("Name").text == "HRM A.HRM":
self.minxpoint = Raw_channel.find("MinXPoints").text
self.startx = Raw_channel.find("StartX").text
self.stepx = Raw_channel.find("StepX").text
self.temp_value = [
int(self.startx) + i * float(self.stepx) for i in range(int(self.minxpoint)-1)]
readings = Raw_channel.findall("Reading")
for j, reading in enumerate(readings):
reading_data = reading.text
reading_data = reading_data.split(" ")
reading_data = [float(value)
for value in reading_data]
self.HRM_data.append(reading_data)
for count in range(len(self.HRM_data)):
self.master_hrm.append(self.temp_value)
return self.master_hrm, self.HRM_data
def Sample_details(self):
if self.Element[16].tag == "Samples":
Ct = []
HRM = []
Page = self.Element[16].find("Page")
sample_id = Page.findall("Sample")
for i, Raw_channel in enumerate(self.Element[19]):
if Raw_channel.find("Name").text == "Cycling A.Green":
self.StartX = int(Raw_channel.find("StartX").text)
self.MinXPoints = int(Raw_channel.find("MinXPoints").text)
self.stepx = float(Raw_channel.find("StepX").text)
elif Raw_channel.find("Name").text == "HRM A.HRM":
self.StartX = int(Raw_channel.find("StartX").text)
self.MinXPoints = int(Raw_channel.find("MinXPoints").text)
self.stepx = float(Raw_channel.find("StepX").text)
for sample in sample_id:
id = sample.find("ID").text
Name = sample.find("Name").text
Text1 = []
Text2 = []
for count in range(int(self.Element[19].find("RawChannel").find("MinXPoints").text)):
Text1.append(f"{id}: {Name}")
for count in range(len([int(self.StartX) + i * float(self.stepx) for i in range(int(self.MinXPoints)-1)])):
Text2.append(f"{id}: {Name}")
Ct.append(Text1)
HRM.append(Text2)
del Text1
del Text2
elif self.Element[15].tag == "Samples":
Ct = []
HRM = []
Page = self.Element[15].find("Page")
sample_id = Page.findall("Sample")
for i, Raw_channel in enumerate(self.Element[18]):
if Raw_channel.find("Name").text == "Cycling A.Green":
self.StartX = int(Raw_channel.find("StartX").text)
self.MinXPoints = int(Raw_channel.find("MinXPoints").text)
self.stepx = float(Raw_channel.find("StepX").text)
elif Raw_channel.find("Name").text == "HRM A.HRM":
self.StartX = int(Raw_channel.find("StartX").text)
self.MinXPoints = int(Raw_channel.find("MinXPoints").text)
self.stepx = float(Raw_channel.find("StepX").text)
for sample in sample_id:
id = sample.find("ID").text
Name = sample.find("Name").text
Text1 = []
Text2 = []
for count in range(int(self.Element[18].find("RawChannel").find("MinXPoints").text)):
Text1.append(f"{id}: {Name}")
for count in range(len([int(self.StartX) + i * float(self.stepx) for i in range(int(self.MinXPoints)-1)])):
Text2.append(f"{id}: {Name}")
Ct.append(Text1)
HRM.append(Text2)
del Text1
del Text2
return Ct, HRM
def HRM_data(file):
try:
obj = Rextractor(file)
x_hrm, y_hrm = obj.HRM()
_, sample_data_hrm = obj.Sample_details()
dataframes = []
for i, (Text, x, y) in enumerate(zip(sample_data_hrm, x_hrm, y_hrm)):
df = pd.DataFrame({
f"Text{i}": Text,
f"X{i}": x,
f"Y{i}": y
})
if Text[0].split(" ")[1] != "None":
dataframes.append(df)
concatenated_df = pd.concat(dataframes, axis=1)
pathogens = ["HSV", "EV", "CMV", "VZV", "HI", "NM", "SP", "IC"]
transformed_data = []
targets = concatenated_df.iloc[0, :][0::3]
targets = [target.split(" ")[-1] for target in targets]
for i, target in enumerate(targets):
for j, pathogen in enumerate(pathogens):
if pathogen == target:
df = concatenated_df.iloc[:, [(3*i), (3*i)+1, (3*i)+2]]
transformed_data.append(df)
mep_df = pd.concat(transformed_data, axis=1)
seperate_dataframes = {}
text_columns = mep_df.iloc[0, :][0::3]
text_columns = [column.split(" ")[1] for column in text_columns]
unique_barcode = list(set(text_columns))
data_frames = []
for i, barcode in enumerate(unique_barcode):
for j, column in enumerate(text_columns):
if barcode == column:
df = mep_df.iloc[:, [(3*j), (3*j)+1, (3*j)+2]]
data_frames.append(df)
barcode_df = pd.concat(data_frames, axis=1)
data_frames.clear()
seperate_dataframes[barcode] = barcode_df
return seperate_dataframes
except:
return None
def CT_Cycle(file):
try:
obj = Rextractor(file)
x_ct, y_ct = obj.Amplification()
sample_data_ct, _ = obj.Sample_details()
dataframes = []
for i, (Text, x, y) in enumerate(zip(sample_data_ct, x_ct, y_ct)):
df = pd.DataFrame({
f"Text{i}": Text,
f"X{i}": x,
f"Y{i}": y
})
if Text[0].split(" ")[1] != "None":
dataframes.append(df)
concatenated_df = pd.concat(dataframes, axis=1)
pathogens = ["HSV", "EV", "CMV", "VZV", "HI", "NM", "SP", "IC"]
transformed_data = []
targets = concatenated_df.iloc[0, :][0::3]
targets = [target.split(" ")[-1] for target in targets]
for i, target in enumerate(targets):
for j, pathogen in enumerate(pathogens):
if pathogen == target:
df = concatenated_df.iloc[:, [(3*i), (3*i)+1, (3*i)+2]]
transformed_data.append(df)
mep_df = pd.concat(transformed_data, axis=1)
seperate_dataframes = {}
text_columns = mep_df.iloc[0, :][0::3]
text_columns = [column.split(" ")[1] for column in text_columns]
unique_barcode = list(set(text_columns))
data_frames = []
for i, barcode in enumerate(unique_barcode):
for j, column in enumerate(text_columns):
if barcode == column:
df = mep_df.iloc[:, [(3*j), (3*j)+1, (3*j)+2]]
data_frames.append(df)
barcode_df = pd.concat(data_frames, axis=1)
data_frames.clear()
seperate_dataframes[barcode] = barcode_df
return seperate_dataframes
except:
return None