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This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,147 @@ FNAME = "5um distance test 120-170.xml" HEADER = """\ <?xml version="1.0" encoding="UTF-8" standalone="no"?> <IndirectPrinting> """ FOOTER = """\ <Attributes> <PrintName>CustomPrint</PrintName> <Originals>4</Originals> <Replicants>{Replicants}</Replicants> <maxInXDir>{maxInXDir}</maxInXDir> <maxInYDir>{maxInYDir}</maxInYDir> </Attributes> <integrity>Lnu0jwyG1z2wa9ReqqU3lHDT36vkrLmFErBTt00iBTMDHn916BMMCA..</integrity> </IndirectPrinting> """ SAMPLE = """\ <Sample> <SampleNumber>{SampleNumber}</SampleNumber> <SpotNumber>{SpotNumber}</SpotNumber> <SpotRow>{SpotRow}</SpotRow> <SpotCol>{SpotCol}</SpotCol> <SampleLabel>{SampleNumber}:{SpotNumber}</SampleLabel> <Depositions>1</Depositions> </Sample> """ import numpy as np data = dict(SampleNumber=1, SpotNumber=0, SpotRow=3, SpotCol=4) samples = "" cell_size = 5 # um dMIN = 120 dMAX = 170 XREPS = 3 xgrid = np.arange(dMIN / cell_size, (dMAX + cell_size) / cell_size).astype(int) maxInXDir = 0 maxInYDir = 0 SpotNumber = 0 for rep in range(XREPS): x_origin = int(rep * dMAX * 3 / cell_size) for row, N in enumerate(reversed(xgrid)): samples += SAMPLE.format( SampleNumber=1, SpotNumber=SpotNumber, SpotRow=row, SpotCol=x_origin, ) SpotNumber += 1 samples += SAMPLE.format( SampleNumber=1, SpotNumber=SpotNumber, SpotRow=row, SpotCol=x_origin + N, ) SpotNumber += 1 maxInXDir = max(maxInXDir, row) maxInYDir = max(maxInYDir, x_origin + N) with open(FNAME, "w") as output_file: output_file.write(HEADER) output_file.write(samples) output_file.write( FOOTER.format( Replicants=SpotNumber, maxInXDir=maxInXDir, maxInYDir=maxInYDir, ) ) print( f"""Spacing: x={cell_size}um, y={dMAX*3} Array size: W={maxInXDir*cell_size/1000}mm, H={dMAX*3*len(xgrid)}mm""" ) import xml.etree.ElementTree as ET import pandas as pd import matplotlib.pyplot as plt # Parse the XML file tree = ET.parse(FNAME) root = tree.getroot() # Extract data and store in a list of dictionaries samples = [] for sample in root.findall("Sample"): sample_data = { "SampleNumber": int(sample.find("SampleNumber").text), "SpotNumber": int(sample.find("SpotNumber").text), "SpotRow": int(sample.find("SpotRow").text), "SpotCol": int(sample.find("SpotCol").text), "SampleLabel": sample.find("SampleLabel").text, "Depositions": int(sample.find("Depositions").text), } samples.append(sample_data) # Convert to DataFrame df = pd.DataFrame(samples) # Print the DataFrame print(df) # Visualize spot positions on a grid plt.figure(figsize=(8, 6)) plt.scatter(df["SpotCol"], df["SpotRow"], s=100, c=df["SampleNumber"], marker="o") # Annotate spots with their labels for i, label in enumerate(df["SampleLabel"]): plt.annotate( label, (df["SpotCol"][i], df["SpotRow"][i]), textcoords="offset points", xytext=(0, 10), ha="center", ) plt.gca().invert_yaxis() # Invert y-axis to match typical grid layout plt.xlabel("SpotCol") plt.ylabel("SpotRow") plt.title("Spot Positions on Grid") # Set up major and minor ticks plt.gca().xaxis.set_major_locator(plt.MultipleLocator(10 * cell_size)) plt.gca().yaxis.set_major_locator(plt.MultipleLocator(10)) plt.gca().xaxis.set_minor_locator(plt.MultipleLocator(cell_size)) plt.gca().yaxis.set_minor_locator(plt.MultipleLocator(1)) # Show grid lines plt.grid(which="major", color="black", linestyle="-", linewidth=0.75) plt.grid(which="minor", color="gray", linestyle=":", linewidth=0.5) plt.grid(True) # plt.gca().set_aspect("equal", adjustable="box") plt.show() -
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This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,68 @@ HEADER = """\ <?xml version="1.0" encoding="UTF-8" standalone="no"?> <IndirectPrinting> """ FOOTER = """\ <Attributes> <PrintName>CustomPrint</PrintName> <Originals>4</Originals> <Replicants>{Replicants}</Replicants> <maxInXDir>{maxInXDir}</maxInXDir> <maxInYDir>{maxInYDir}</maxInYDir> </Attributes> <integrity>Lnu0jwyG1z2wa9ReqqU3lHDT36vkrLmFErBTt00iBTMDHn916BMMCA..</integrity> </IndirectPrinting> """ SAMPLE = """\ <Sample> <SampleNumber>{SampleNumber}</SampleNumber> <SpotNumber>{SpotNumber}</SpotNumber> <SpotRow>{SpotRow}</SpotRow> <SpotCol>{SpotCol}</SpotCol> <SampleLabel>{SampleNumber}:{SpotNumber}</SampleLabel> <Depositions>1</Depositions> </Sample> """ data = dict(SampleNumber=1, SpotNumber=0, SpotRow=3, SpotCol=4) samples = "" ROWS = 20 COLS = 5 cell_size = 80 # um spot2spot = 2 # cells fov2fov = 5 # cells maxInXDir = COLS * fov2fov maxInYDir = ROWS * fov2fov for SampleNumber in range(1, 5): SpotNumber = 0 i = (0 if SampleNumber <= 2 else 1) * spot2spot j = ((SampleNumber - 1) % 2) * spot2spot for SpotRow in range(i, i + maxInYDir, fov2fov): for SpotCol in range(j, j + maxInXDir, fov2fov): samples += SAMPLE.format( SampleNumber=SampleNumber, SpotNumber=SpotNumber, SpotRow=SpotRow, SpotCol=SpotCol, ) SpotNumber += 1 with open("out.xml", "w") as output_file: output_file.write(HEADER) output_file.write(samples) output_file.write( FOOTER.format( Replicants=ROWS * COLS, maxInXDir=maxInXDir - spot2spot, maxInYDir=maxInYDir - spot2spot, ) ) This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,42 @@ import xml.etree.ElementTree as ET import pandas as pd import matplotlib.pyplot as plt # Parse the XML file tree = ET.parse('4spots 160um 400um 6x20.xml') root = tree.getroot() # Extract data and store in a list of dictionaries samples = [] for sample in root.findall('Sample'): sample_data = { 'SampleNumber': int(sample.find('SampleNumber').text), 'SpotNumber': int(sample.find('SpotNumber').text), 'SpotRow': int(sample.find('SpotRow').text), 'SpotCol': int(sample.find('SpotCol').text), 'SampleLabel': sample.find('SampleLabel').text, 'Depositions': int(sample.find('Depositions').text) } samples.append(sample_data) # Convert to DataFrame df = pd.DataFrame(samples) # Print the DataFrame print(df) # Visualize spot positions on a grid plt.figure(figsize=(8, 6)) plt.scatter(df['SpotCol'], df['SpotRow'], s=100, c=df['SampleNumber'], marker='o') # Annotate spots with their labels for i, label in enumerate(df['SampleLabel']): plt.annotate(label, (df['SpotCol'][i], df['SpotRow'][i]), textcoords="offset points", xytext=(0,10), ha='center') plt.gca().invert_yaxis() # Invert y-axis to match typical grid layout plt.xlabel('SpotCol') plt.ylabel('SpotRow') plt.title('Spot Positions on Grid') plt.grid(True) plt.gca().set_aspect('equal', adjustable='box') plt.show()