update requirements.txt

.gitignore

@@ -162,3 +162,7 @@ cython_debug/
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
 
+.venv/
+.idea/
+.ipynb_checkpoints/
+Data/

Code/1-extract.py

@@ -0,0 +1,72 @@
+import openpyxl
+import csv
+import os.path
+import glob
+
+
+def read_value(patient_dir, atlas, data_type):
+    folder_name = os.path.basename(patient_dir)
+    csv_file_path = os.path.join(patient_dir, f"{data_type}_{atlas}_result", "result_atlas.csv")
+
+    result = [folder_name]
+    with open(csv_file_path, mode='r') as data_csv_file:
+        csv_reader = csv.DictReader(data_csv_file)
+        for row in csv_reader:
+            mean = float(row["meanValue"])
+            result.append(mean)
+
+    return result
+
+
+def read_header(patient_dir, atlas, data_type):
+    header = ['编号']
+
+    csv_file_path = os.path.join(patient_dir, f"{data_type}_{atlas}_result", "result_atlas.csv")
+
+    with open(csv_file_path, mode='r') as data_csv_file:
+        csv_reader = csv.DictReader(data_csv_file)
+        for row in csv_reader:
+            region_name = row["Chinese Name"]
+            header.append(region_name)
+
+    return header
+
+
+def write_to_work_sheet(work_sheet, patient_list, atlas, data_type):
+    if len(patient_list) == 0:
+        return
+
+    header = read_header(patient_list[0], atlas, data_type)
+    work_sheet.append(header)
+
+    for patient_dir in patient_list:
+        print(data_type, atlas, patient_dir)
+        patient_data = read_value(patient_dir, atlas, data_type)
+
+        work_sheet.append(patient_data)
+
+
+def main():
+    # 选择导出的数据类型，请尝试增加VOL类型，并修改read_value、read_header函数以支持读取体积
+    data_types = ['corr-CBF', 'ATT', 'ACBV', 'CBF1', 'CBF2', 'CBF3', 'CBF4', 'CBF5']
+    # 选择导出后的图谱
+    atlas_list = ['AnImage_WholeBrain', 'AnImage_BrainLobes', 'AnImage_AAL3']
+    # 选择输出的位置
+    output_file = r'..\Data\csv-extract.xlsx'
+    # 选择输入的数据
+    patient_list = glob.glob(r'..\Data\csv-data\*')
+    # 创建新的excel workbook
+    work_book = openpyxl.Workbook()
+    # 删除默认worksheet
+    work_book.remove(work_book.active)
+
+    for data_type in data_types:
+        for atlas in atlas_list:
+            work_sheet = work_book.create_sheet(title=f'{atlas}_{data_type}')
+            write_to_work_sheet(work_sheet, patient_list, atlas, data_type)
+
+    # 保存workbook到xlsx文件
+    work_book.save(output_file)
+
+
+main()

Code/10-registration.py

@@ -0,0 +1,53 @@
+import SimpleITK as sitk
+
+
+def command_iteration(method):
+    print(f"{method.GetOptimizerIteration()} = {method.GetMetricValue():.5f} {method.GetOptimizerPosition()}")
+
+
+def registration(fixed_image_path, moving_image_path, new_image_path):
+    fixed_image = sitk.ReadImage(fixed_image_path, sitk.sitkFloat32)
+    moving_image = sitk.ReadImage(moving_image_path, sitk.sitkFloat32)
+
+    registration_method = sitk.ImageRegistrationMethod()
+
+    registration_method.SetMetricAsJointHistogramMutualInformation(numberOfHistogramBins=50)
+    registration_method.SetMetricSamplingStrategy(registration_method.REGULAR)
+    registration_method.SetMetricSamplingPercentage(0.2, 42)
+    registration_method.SetGlobalDefaultNumberOfThreads(1)
+    registration_method.SetOptimizerScalesFromPhysicalShift()
+
+    registration_method.SetInterpolator(sitk.sitkLinear)
+    registration_method.AddCommand(sitk.sitkIterationEvent, lambda: command_iteration(registration_method))
+
+    registration_method.SetOptimizerAsRegularStepGradientDescent(minStep=0.0001,
+                                                                 learningRate=1.0,
+                                                                 numberOfIterations=400,
+                                                                 gradientMagnitudeTolerance=1e-8)
+
+    initial_transform = sitk.CenteredTransformInitializer(fixed_image, moving_image, sitk.Euler3DTransform())
+    registration_method.SetInitialTransform(initial_transform, inPlace=True)
+
+    # 进行配准
+    final_transform_v4 = registration_method.Execute(fixed_image, moving_image)
+
+    # 打印结束条件
+    stop_condition = registration_method.GetOptimizerStopConditionDescription()
+    print(stop_condition)
+
+    # 重采样新的图像
+    moving_resampled = sitk.Resample(moving_image, fixed_image, final_transform_v4, sitk.sitkLinear, 0.0,
+                                     moving_image.GetPixelID())
+
+    sitk.WriteImage(moving_resampled, new_image_path)
+
+
+def main():
+    fixed_image_path = r"..\Data\registration\t1.nii.gz"
+    moving_image_path = r"..\Data\registration\t2 flair.nii.gz"
+    new_image_path = r"..\Data\registration\t2 flair_resampled.nii.gz"
+
+    registration(fixed_image_path, moving_image_path, new_image_path)
+
+
+main()

Code/2-simple-stat.py

@@ -0,0 +1,93 @@
+import openpyxl
+import csv
+import os.path
+import glob
+
+
+def read_value(patient_dir, atlas, data_type):
+    folder_name = os.path.basename(patient_dir)
+    csv_file_path = os.path.join(patient_dir, f"{data_type}_{atlas}_result", "result_atlas.csv")
+
+    result = [folder_name]
+    with open(csv_file_path, mode='r') as data_csv_file:
+        csv_reader = csv.DictReader(data_csv_file)
+        for row in csv_reader:
+            mean = float(row["meanValue"])
+            result.append(mean)
+
+    return result
+
+
+def read_header(patient_dir, atlas, data_type):
+    header = ['编号']
+
+    csv_file_path = os.path.join(patient_dir, f"{data_type}_{atlas}_result", "result_atlas.csv")
+
+    with open(csv_file_path, mode='r') as data_csv_file:
+        csv_reader = csv.DictReader(data_csv_file)
+        for row in csv_reader:
+            region_name = row["Chinese Name"]
+            header.append(region_name)
+
+    return header
+
+
+def generate_stats_row(name, region_size, function, patient_size):
+    result = [name]
+
+    for i in range(region_size):
+        letter = openpyxl.utils.cell.get_column_letter(i + 2)
+        result.append(f'={function}({letter}2:{letter}{patient_size + 1})')
+
+    return result
+
+
+def write_to_work_sheet(work_sheet, patient_list, atlas, data_type):
+    if len(patient_list) == 0:
+        return
+
+    header = read_header(patient_list[0], atlas, data_type)
+
+    work_sheet.append(header)
+
+    for patient_dir in patient_list:
+        print(data_type, patient_dir)
+        patient_data = read_value(patient_dir, atlas, data_type)
+
+        work_sheet.append(patient_data)
+
+    region_size = len(header) - 1
+    work_sheet.append([])
+    work_sheet.append([])
+    work_sheet.append(generate_stats_row('均值', region_size, 'AVERAGE', len(patient_list)))
+    work_sheet.append(generate_stats_row('标准差', region_size, 'STDEV', len(patient_list)))
+    work_sheet.append(generate_stats_row('最小值', region_size, 'MIN', len(patient_list)))
+    # 请尝试在空格的位置添加 5% 25% 50% 75% 95% 分位数，使用excel的PERCENTILE函数
+    work_sheet.append([])
+    work_sheet.append([])
+    work_sheet.append([])
+    work_sheet.append([])
+    work_sheet.append([])
+    work_sheet.append(generate_stats_row('最大值', region_size, 'MAX', len(patient_list)))
+
+
+def main():
+    data_types = ['corr-CBF', 'ATT', 'ACBV', 'CBF1', 'CBF2', 'CBF3', 'CBF4', 'CBF5']
+    atlas_list = ['AnImage_WholeBrain', 'AnImage_BrainLobes', 'AnImage_AAL3']
+
+    output_file = r'..\Data\csv-simple-stat.xlsx'
+
+    patient_list = glob.glob(r'..\Data\csv-data\*')
+
+    work_book = openpyxl.Workbook()
+    work_book.remove(work_book.active)
+
+    for data_type in data_types:
+        for atlas in atlas_list:
+            work_sheet = work_book.create_sheet(title=f'{atlas}_{data_type}')
+            write_to_work_sheet(work_sheet, patient_list, atlas, data_type)
+
+    work_book.save(output_file)
+
+
+main()

Code/3-group.py

@@ -0,0 +1,104 @@
+import openpyxl
+import csv
+import os.path
+
+
+def read_value(patient_dir, atlas, data_type):
+    folder_name = os.path.basename(patient_dir)
+    csv_file_path = os.path.join(patient_dir, f"{data_type}_{atlas}_result", "result_atlas.csv")
+
+    result = [folder_name]
+    with open(csv_file_path, mode='r') as data_csv_file:
+        csv_reader = csv.DictReader(data_csv_file)
+        for row in csv_reader:
+            mean = float(row["meanValue"])
+            result.append(mean)
+
+    return result
+
+
+def read_header(patient_dir, atlas, data_type):
+    header = ['编号']
+
+    csv_file_path = os.path.join(patient_dir, f"{data_type}_{atlas}_result", "result_atlas.csv")
+
+    with open(csv_file_path, mode='r') as data_csv_file:
+        csv_reader = csv.DictReader(data_csv_file)
+        for row in csv_reader:
+            region_name = row["Chinese Name"]
+            header.append(region_name)
+
+    return header
+
+
+def generate_stats_row(name, region_size, function, patient_size, extra_parameter):
+    result = [name]
+
+    for i in range(region_size):
+        letter = openpyxl.utils.cell.get_column_letter(i + 2)
+        result.append(f'={function}({letter}2:{letter}{patient_size + 1}{extra_parameter})')
+
+    return result
+
+
+def write_to_work_sheet(work_sheet, patient_list, atlas, data_type):
+    if len(patient_list) == 0:
+        return
+
+    header = read_header(patient_list[0], atlas, data_type)
+
+    work_sheet.append(header)
+
+    for patient_dir in patient_list:
+        print(data_type, patient_dir)
+        patient_data = read_value(patient_dir, atlas, data_type)
+
+        work_sheet.append(patient_data)
+
+    region_size = len(header) - 1
+    work_sheet.append([])
+    work_sheet.append([])
+    work_sheet.append(generate_stats_row('均值', region_size, 'AVERAGE', len(patient_list), ''))
+    work_sheet.append(generate_stats_row('标准差', region_size, 'STDEV', len(patient_list), ''))
+    work_sheet.append(generate_stats_row('最小值', region_size, 'MIN', len(patient_list), ''))
+    work_sheet.append(generate_stats_row('5%', region_size, 'PERCENTILE', len(patient_list), ',0.05'))
+    work_sheet.append(generate_stats_row('25%', region_size, 'PERCENTILE', len(patient_list), ',0.25'))
+    work_sheet.append(generate_stats_row('50%', region_size, 'PERCENTILE', len(patient_list), ',0.50'))
+    work_sheet.append(generate_stats_row('75%', region_size, 'PERCENTILE', len(patient_list), ',0.75'))
+    work_sheet.append(generate_stats_row('95%', region_size, 'PERCENTILE', len(patient_list), ',0.95'))
+    work_sheet.append(generate_stats_row('最大值', region_size, 'MAX', len(patient_list), ''))
+
+
+def main():
+    data_types = ['corr-CBF', "ATT"]
+    atlas_list = ['AnImage_WholeBrain', 'AnImage_BrainLobes', 'AnImage_AAL3']
+
+    output_file = r'..\Data\csv-group.xlsx'
+    group_file = r'..\Data\group.xlsx'
+    patient_root = r'..\Data\csv-data'
+
+    groups = {}
+    # 读取分组信息
+    group_workbook = openpyxl.load_workbook(group_file)
+    for sheet_name in group_workbook.sheetnames:
+        group = []
+        group_sheet = group_workbook[sheet_name]
+        for i in range(1, group_sheet.max_row + 1):
+            patient_id = group_sheet.cell(row=i, column=1).value
+            patient_path = os.path.join(patient_root, patient_id)
+            group.append(patient_path)
+        groups[sheet_name] = group
+
+    work_book = openpyxl.Workbook()
+    work_book.remove(work_book.active)
+
+    for group_key in groups.keys():
+        for data_type in data_types:
+            for atlas in atlas_list:
+                work_sheet = work_book.create_sheet(title=f'{group_key}_{atlas}_{data_type}')
+                write_to_work_sheet(work_sheet, groups[group_key], atlas, data_type)
+
+    work_book.save(output_file)
+
+
+main()

Code/4-ANOVA.py

@@ -0,0 +1,184 @@
+import openpyxl
+import csv
+import os.path
+
+
+def read_value(patient_dir, atlas, data_type):
+    folder_name = os.path.basename(patient_dir)
+    csv_file_path = os.path.join(patient_dir, f"{data_type}_{atlas}_result", "result_atlas.csv")
+
+    result = [folder_name]
+    with open(csv_file_path, mode='r') as data_csv_file:
+        csv_reader = csv.DictReader(data_csv_file)
+        for row in csv_reader:
+            mean = float(row["meanValue"])
+            result.append(mean)
+
+    return result
+
+
+def read_header(patient_dir, atlas, data_type):
+    header = ['编号']
+
+    csv_file_path = os.path.join(patient_dir, f"{data_type}_{atlas}_result", "result_atlas.csv")
+
+    with open(csv_file_path, mode='r') as data_csv_file:
+        csv_reader = csv.DictReader(data_csv_file)
+        for row in csv_reader:
+            region_name = row["Chinese Name"]
+            header.append(region_name)
+
+    return header
+
+
+def generate_stats_row(name, region_size, function, patient_size, extra_parameter):
+    result = [name]
+
+    for i in range(region_size):
+        letter = openpyxl.utils.cell.get_column_letter(i + 2)
+        result.append(f'={function}({letter}2:{letter}{patient_size + 1}{extra_parameter})')
+
+    return result
+
+
+def write_to_work_sheet(work_sheet, patient_list, atlas, data_type):
+    if len(patient_list) == 0:
+        return
+
+    header = read_header(patient_list[0], atlas, data_type)
+
+    work_sheet.append(header)
+
+    for patient_dir in patient_list:
+        print(data_type, patient_dir)
+        patient_data = read_value(patient_dir, atlas, data_type)
+
+        work_sheet.append(patient_data)
+
+    region_size = len(header) - 1
+    work_sheet.append([])
+    work_sheet.append([])
+    work_sheet.append(generate_stats_row('均值', region_size, 'AVERAGE', len(patient_list), ''))
+    work_sheet.append(generate_stats_row('标准差', region_size, 'STDEV', len(patient_list), ''))
+    work_sheet.append(generate_stats_row('最小值', region_size, 'MIN', len(patient_list), ''))
+    work_sheet.append(generate_stats_row('5%', region_size, 'PERCENTILE', len(patient_list), ',0.05'))
+    work_sheet.append(generate_stats_row('25%', region_size, 'PERCENTILE', len(patient_list), ',0.25'))
+    work_sheet.append(generate_stats_row('50%', region_size, 'PERCENTILE', len(patient_list), ',0.50'))
+    work_sheet.append(generate_stats_row('75%', region_size, 'PERCENTILE', len(patient_list), ',0.75'))
+    work_sheet.append(generate_stats_row('95%', region_size, 'PERCENTILE', len(patient_list), ',0.95'))
+    work_sheet.append(generate_stats_row('最大值', region_size, 'MAX', len(patient_list), ''))
+
+
+def write_sum(start_index, step, count, row_num):
+    result = f"={openpyxl.utils.cell.get_column_letter(start_index)}{row_num}"
+    for i in range(1, count):
+        letter = openpyxl.utils.cell.get_column_letter(start_index + i * step)
+        result += f'+{letter}{row_num}'
+    return result
+
+
+def write_anova(workbook, work_sheet, groups, atlas, data_type):
+    header_group = [""] * 14
+
+    for group_name in groups.keys():
+        header_group += [group_name] * 5
+    work_sheet.append(header_group)
+
+    header_cn = ['', '样本数量', '平均值', '标准差', '总平方和', '组间平方和', '组内平方和', '检验', '组间均方',
+                 '自由度', '组内均方', '自由度', '', '']
+    for _ in groups.keys():
+        header_cn += ['样本数量', '平均值', '标准差', '组间平方和', '组内平方和']
+    work_sheet.append(header_cn)
+
+    header_en = ['', 'N', 'AVG', 'STD', 'TSS', 'BSS', 'WSS', 'check', 'BMSS', 'df', 'WMSS', 'df', 'F', 'p']
+    for _ in groups.keys():
+        header_en += ['N', 'AVG', 'STD', 'BSS', 'WSS']
+    work_sheet.append(header_en)
+
+    sheet_dict = {}
+    all_group_count = 0
+    for group_name, group in groups.items():
+        sheet_dict[group_name] = f'{group_name}_{atlas}_{data_type}'
+        all_group_count += len(group)
+    all_group_sheet_name = f'ALL_{atlas}_{data_type}'
+
+    first_group_sheet = workbook[list(sheet_dict.values())[0]]
+    for column_num in range(2, first_group_sheet.max_column + 1):
+        column_letter = openpyxl.utils.cell.get_column_letter(column_num)
+        curr_row_number = column_num + 2
+        data_row = [first_group_sheet.cell(row=1, column=column_num).value]
+        data_row += [write_sum(15, 5, len(sheet_dict), curr_row_number)]
+        data_row += [f'=AVERAGE(\'{all_group_sheet_name}\'!{column_letter}2:{column_letter}{all_group_count + 1})']
+        data_row += [f'=STDEV(\'{all_group_sheet_name}\'!{column_letter}2:{column_letter}{all_group_count + 1})']
+        data_row += [f'=DEVSQ(\'{all_group_sheet_name}\'!{column_letter}2:{column_letter}{all_group_count + 1})']
+        data_row += [write_sum(18, 5, len(sheet_dict), curr_row_number)]
+        data_row += [write_sum(19, 5, len(sheet_dict), curr_row_number)]
+        data_row += [f'=E{curr_row_number}-F{curr_row_number}-G{curr_row_number}']
+        data_row += [f'=F{curr_row_number}/J{curr_row_number}']
+        data_row += [len(groups) - 1]
+        data_row += [f'=G{curr_row_number}/L{curr_row_number}']
+        data_row += [f'=B{curr_row_number}-J{curr_row_number}-1']
+        data_row += [f'=I{curr_row_number}/K{curr_row_number}']
+        data_row += [f'=FDIST(M{curr_row_number}, J{curr_row_number}, L{curr_row_number})']
+
+        for group_name, sheet_name in sheet_dict.items():
+            data_count = len(groups[group_name])
+            current_column_num = len(data_row) + 1
+            count_letter = openpyxl.utils.cell.get_column_letter(current_column_num)
+            avg_letter = openpyxl.utils.cell.get_column_letter(current_column_num + 1)
+            data_row += [f'=COUNTA(\'{sheet_name}\'!{column_letter}2:{column_letter}{data_count + 1})']
+            data_row += [f'=AVERAGE(\'{sheet_name}\'!{column_letter}2:{column_letter}{data_count + 1})']
+            data_row += [f'=STDEV(\'{sheet_name}\'!{column_letter}2:{column_letter}{data_count + 1})']
+            data_row += [f'={count_letter}{curr_row_number}*({avg_letter}{curr_row_number}-C{curr_row_number})^2']
+            data_row += [f'=DEVSQ(\'{sheet_name}\'!{column_letter}2:{column_letter}{data_count + 1})']
+
+        work_sheet.append(data_row)
+
+
+def main():
+    data_types = ['corr-CBF']
+    atlas_list = ['AnImage_AAL3']
+
+    output_file = r'..\Data\csv-ANOVA.xlsx'
+    group_file = r'..\Data\group.xlsx'
+    patient_root = r'..\Data\csv-data'
+
+    groups = {}
+
+    group_workbook = openpyxl.load_workbook(group_file)
+    for sheet_name in group_workbook.sheetnames:
+        group = []
+        group_sheet = group_workbook[sheet_name]
+        for i in range(1, group_sheet.max_row + 1):
+            patient_id = group_sheet.cell(row=i, column=1).value
+            patient_path = os.path.join(patient_root, patient_id)
+            group.append(patient_path)
+        groups[sheet_name] = group
+
+    all_group = []
+    for group in groups.values():
+        all_group += group
+
+    work_book = openpyxl.Workbook()
+    work_book.remove(work_book.active)
+
+    for data_type in data_types:
+        for atlas in atlas_list:
+            atlas_new_name = atlas.replace("AnImage_", "")
+            for group_key in groups.keys():
+                work_sheet = work_book.create_sheet(title=f'{group_key}_{atlas_new_name}_{data_type}')
+                write_to_work_sheet(work_sheet, groups[group_key], atlas, data_type)
+
+            work_sheet = work_book.create_sheet(title=f'ALL_{atlas_new_name}_{data_type}')
+            write_to_work_sheet(work_sheet, all_group, atlas, data_type)
+
+    for data_type in data_types:
+        for atlas in atlas_list:
+            atlas_new_name = atlas.replace("AnImage_", "")
+            work_sheet = work_book.create_sheet(title=f'ANOVA_{atlas_new_name}_{data_type}')
+            write_anova(work_book, work_sheet, groups, atlas_new_name, data_type)
+
+    work_book.save(output_file)
+
+
+main()

Code/5-ANOVA-SciPy.py

@@ -0,0 +1,150 @@
+import numpy as np
+import openpyxl
+import csv
+import os.path
+from scipy.stats import f_oneway
+
+
+def read_value(patient_dir, atlas, data_type):
+    folder_name = os.path.basename(patient_dir)
+    csv_file_path = os.path.join(patient_dir, f"{data_type}_{atlas}_result", "result_atlas.csv")
+
+    result = [folder_name]
+    with open(csv_file_path, mode='r') as data_csv_file:
+        csv_reader = csv.DictReader(data_csv_file)
+        for row in csv_reader:
+            mean = float(row["meanValue"])
+            result.append(mean)
+
+    return result
+
+
+def read_header(patient_dir, atlas, data_type):
+    header = ['编号']
+
+    csv_file_path = os.path.join(patient_dir, f"{data_type}_{atlas}_result", "result_atlas.csv")
+
+    with open(csv_file_path, mode='r') as data_csv_file:
+        csv_reader = csv.DictReader(data_csv_file)
+        for row in csv_reader:
+            region_name = row["Chinese Name"]
+            header.append(region_name)
+
+    return header
+
+
+def generate_stats_row(name, region_size, function, patient_size, extra_parameter):
+    result = [name]
+
+    for i in range(region_size):
+        letter = openpyxl.utils.cell.get_column_letter(i + 2)
+        result.append(f'={function}({letter}2:{letter}{patient_size + 1}{extra_parameter})')
+
+    return result
+
+
+def write_to_work_sheet(work_sheet, patient_list, atlas, data_type):
+    if len(patient_list) == 0:
+        return
+
+    header = read_header(patient_list[0], atlas, data_type)
+
+    work_sheet.append(header)
+
+    for patient_dir in patient_list:
+        print(data_type, patient_dir)
+        patient_data = read_value(patient_dir, atlas, data_type)
+
+        work_sheet.append(patient_data)
+
+    region_size = len(header) - 1
+    work_sheet.append([])
+    work_sheet.append([])
+    work_sheet.append(generate_stats_row('均值', region_size, 'AVERAGE', len(patient_list), ''))
+    work_sheet.append(generate_stats_row('标准差', region_size, 'STDEV', len(patient_list), ''))
+    work_sheet.append(generate_stats_row('最小值', region_size, 'MIN', len(patient_list), ''))
+    work_sheet.append(generate_stats_row('5%', region_size, 'PERCENTILE', len(patient_list), ',0.05'))
+    work_sheet.append(generate_stats_row('25%', region_size, 'PERCENTILE', len(patient_list), ',0.25'))
+    work_sheet.append(generate_stats_row('50%', region_size, 'PERCENTILE', len(patient_list), ',0.50'))
+    work_sheet.append(generate_stats_row('75%', region_size, 'PERCENTILE', len(patient_list), ',0.75'))
+    work_sheet.append(generate_stats_row('95%', region_size, 'PERCENTILE', len(patient_list), ',0.95'))
+    work_sheet.append(generate_stats_row('最大值', region_size, 'MAX', len(patient_list), ''))
+
+
+def write_anova(workbook, work_sheet, groups, atlas, data_type):
+    header = ['', 'F', 'p']
+    work_sheet.append(header)
+
+    sheet_dict = {}
+    for group_name, group in groups.items():
+        sheet_dict[group_name] = f'{group_name}_{atlas}_{data_type}'
+
+    first_group_sheet = workbook[list(sheet_dict.values())[0]]
+    for column_num in range(2, first_group_sheet.max_column + 1):
+        data_list = []
+        data_row = [first_group_sheet.cell(row=1, column=column_num).value]
+
+        for group_name, sheet_name in sheet_dict.items():
+            group_data = []
+            data_count = len(groups[group_name])
+            for i in range(2, data_count + 2):
+                data_value = workbook[sheet_name].cell(row=i, column=column_num).value
+                if not np.isnan(data_value):
+                    group_data.append(data_value)
+            data_list.append(group_data)
+
+        if len(data_list[0]) == 0:
+            continue
+
+        f_value, p_value = f_oneway(*data_list)
+        data_row += [f_value, p_value]
+        work_sheet.append(data_row)
+
+
+def main():
+    data_types = ['corr-CBF']
+    atlas_list = ['AnImage_AAL3']
+
+    output_file = r'..\Data\csv-ANOVA-SciPy.xlsx'
+    group_file = r'..\Data\group.xlsx'
+    patient_root = r'..\Data\csv-data'
+
+    groups = {}
+
+    group_workbook = openpyxl.load_workbook(group_file)
+    for sheet_name in group_workbook.sheetnames:
+        group = []
+        group_sheet = group_workbook[sheet_name]
+        for i in range(1, group_sheet.max_row + 1):
+            patient_id = group_sheet.cell(row=i, column=1).value
+            patient_path = os.path.join(patient_root, patient_id)
+            group.append(patient_path)
+        groups[sheet_name] = group
+
+    all_group = []
+    for group in groups.values():
+        all_group += group
+
+    work_book = openpyxl.Workbook()
+    work_book.remove(work_book.active)
+
+    for data_type in data_types:
+        for atlas in atlas_list:
+            atlas_new_name = atlas.replace("AnImage_", "")
+            for group_key in groups.keys():
+                work_sheet = work_book.create_sheet(title=f'{group_key}_{atlas_new_name}_{data_type}')
+                write_to_work_sheet(work_sheet, groups[group_key], atlas, data_type)
+
+            work_sheet = work_book.create_sheet(title=f'ALL_{atlas_new_name}_{data_type}')
+            write_to_work_sheet(work_sheet, all_group, atlas, data_type)
+
+    for data_type in data_types:
+        for atlas in atlas_list:
+            atlas_new_name = atlas.replace("AnImage_", "")
+            work_sheet = work_book.create_sheet(title=f'ANOVA_{atlas_new_name}_{data_type}')
+            write_anova(work_book, work_sheet, groups, atlas_new_name, data_type)
+
+    work_book.save(output_file)
+
+
+main()

Code/6-cbf-graph.py

@@ -0,0 +1,68 @@
+import numpy as np
+import openpyxl
+import os.path
+import matplotlib.pyplot as plt
+
+
+def draw_cbf_graph(region, data, save_path):
+    bar_x = np.array(['0.5', '1', '1.5', '2', '2.5', 'cCBF'])
+    bar_y = np.array(data)
+    color = ['#4CAF50', '#4CAF50', '#4CAF50', '#4CAF50', '#4CAF50', '#478FC1']
+
+    # Plotting the Graph
+    font_color = '#FFFFFF'
+    plt.rcParams['text.color'] = font_color
+    plt.rcParams['axes.labelcolor'] = font_color
+    plt.rcParams['xtick.color'] = font_color
+    plt.rcParams['ytick.color'] = font_color
+
+    plt.figure(facecolor='#000000')
+    ax = plt.axes()
+    ax.set_facecolor('#000000')
+    ax.spines['left'].set_color(font_color)
+    ax.spines['right'].set_color(font_color)
+    ax.spines['bottom'].set_color(font_color)
+    ax.spines['top'].set_color(font_color)
+
+    plt.rcParams["font.sans-serif"] = ["SimHei"]  # 设置字体
+    plt.bar(bar_x, bar_y, width=0.5, color=color)
+
+    for i in range(len(bar_x)):
+        plt.text(i - 0.2, bar_y[i] - 2, f"{bar_y[i]:.2f}")
+
+    plt.title(f"221例健康人西门子5延迟数据矫正前后{region}CBF对比")
+    plt.xlabel("PLD")
+    plt.ylabel("CBF")
+    plt.savefig(save_path, dpi=200)
+    plt.cla()
+    plt.close('all')
+
+
+def main():
+    data_types = ['CBF1', 'CBF2', 'CBF3', 'CBF4', 'CBF5', 'corr-CBF']
+    # 请尝试添加'AnImage_AAL3'图谱,并修复错误
+    atlas_list = ['AnImage_WholeBrain', 'AnImage_BrainLobes']
+    output_dir = r'..\Data\cbf-graph'
+    os.makedirs(output_dir, exist_ok=True)
+
+    input_file = r'..\Data\csv-simple-stat.xlsx'
+    workbook = openpyxl.load_workbook(input_file, data_only=True)
+
+    for atlas in atlas_list:
+        cbf_mean_dict = {}
+        for data_type in data_types:
+            worksheet = workbook[f'{atlas}_{data_type}']
+            for column_num in range(2, worksheet.max_column + 1):
+                name = worksheet.cell(row=1, column=column_num).value
+                mean = worksheet.cell(row=worksheet.max_row - 8, column=column_num).value
+                print(atlas, data_type, name, mean)
+
+                if name not in cbf_mean_dict:
+                    cbf_mean_dict[name] = []
+                cbf_mean_dict[name].append(mean)
+
+        for region, data in cbf_mean_dict.items():
+            draw_cbf_graph(region, data, os.path.join(output_dir, f'{region}.png'))
+
+
+main()

Code/7-box-graph.py

@@ -0,0 +1,50 @@
+import openpyxl
+import os.path
+import matplotlib.pyplot as plt
+
+
+def draw_box_graph(region, data, groups, data_type, save_path):
+    plt.rcParams["font.sans-serif"] = ["SimHei"]  # 设置字体
+    plt.boxplot(data,
+                patch_artist=True,  # 是否填色
+                labels=groups)
+
+    plt.title(f"各个分组{region}{data_type}对比")
+    plt.xlabel("分组")
+    plt.ylabel(data_type)
+    plt.savefig(save_path, dpi=200)
+    plt.cla()
+    plt.close('all')
+
+
+def main():
+    data_types = ['corr-CBF', "ATT"]
+    # 请尝试添加'AnImage_AAL3'图谱,并修复错误
+    atlas_list = ['AnImage_WholeBrain', 'AnImage_BrainLobes']
+    output_dir = r'..\Data\box-graph'
+    os.makedirs(output_dir, exist_ok=True)
+
+    input_file = r'..\Data\csv-group.xlsx'
+    workbook = openpyxl.load_workbook(input_file, data_only=True)
+    groups = ['A', 'B', 'C']
+
+    for data_type in data_types:
+        for atlas in atlas_list:
+            value_dict = {}
+            for group in groups:
+                worksheet = workbook[f'{group}_{atlas}_{data_type}']
+                for column_num in range(2, worksheet.max_column + 1):
+                    data = []
+                    name = worksheet.cell(row=1, column=column_num).value
+                    for row in range(2, worksheet.max_row - 10):
+                        data.append(worksheet.cell(row=row, column=column_num).value)
+
+                    if name not in value_dict:
+                        value_dict[name] = []
+                    value_dict[name].append(data)
+
+            for region, data in value_dict.items():
+                draw_box_graph(region, data, groups, data_type, os.path.join(output_dir, f'{region}{data_type}.png'))
+
+
+main()

Code/8-subplot.py

@@ -0,0 +1,55 @@
+import openpyxl
+import os.path
+import matplotlib.pyplot as plt
+
+
+def draw_box_graph(data_dict, groups, data_type, save_path):
+    plt.rcParams["font.sans-serif"] = ["SimHei"]  # 设置字体
+
+    grid_height = 2
+    grid_width = 4
+
+    _, ax = plt.subplots(grid_height, grid_width, figsize=(20, 15), sharey=True)
+    keys = list(data_dict.keys())
+    for x in range(grid_width):
+        for y in range(grid_height):
+            region_name = keys[x + y * grid_width]
+            ax[y][x].boxplot(data_dict[region_name], patch_artist=True, labels=groups)
+            ax[y][x].set_title(f"{region_name}{data_type}对比")
+            ax[y][x].set_ylabel(data_type)
+
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=200)
+    plt.cla()
+    plt.close('all')
+
+
+def main():
+    data_types = ['corr-CBF', "ATT"]
+    atlas_list = ['AnImage_BrainLobes']
+    output_dir = r'..\Data\subplot'
+    os.makedirs(output_dir, exist_ok=True)
+
+    input_file = r'..\Data\csv-group.xlsx'
+    workbook = openpyxl.load_workbook(input_file, data_only=True)
+    groups = ['A', 'B', 'C']
+
+    for data_type in data_types:
+        for atlas in atlas_list:
+            value_dict = {}
+            for group in groups:
+                worksheet = workbook[f'{group}_{atlas}_{data_type}']
+                for column_num in range(2, worksheet.max_column + 1):
+                    data = []
+                    name = worksheet.cell(row=1, column=column_num).value
+                    for row in range(2, worksheet.max_row - 10):
+                        data.append(worksheet.cell(row=row, column=column_num).value)
+
+                    if name not in value_dict:
+                        value_dict[name] = []
+                    value_dict[name].append(data)
+
+            draw_box_graph(value_dict, groups, data_type, os.path.join(output_dir, f'{atlas}_{data_type}.png'))
+
+
+main()

Code/9-polar-graph.py

@@ -0,0 +1,71 @@
+import numpy as np
+import openpyxl
+import os.path
+import matplotlib.pyplot as plt
+
+
+def draw_cbf_graph(cbf_mean_dict, data_type, save_path):
+    plt.rcParams["figure.figsize"] = [32, 18]
+    plt.rcParams["figure.autolayout"] = True
+    plt.rcParams["font.sans-serif"] = ["SimHei"]  # 设置字体
+    subplot = plt.subplot(1, 1, 1, projection='polar')
+
+    count = len(cbf_mean_dict)
+    theta = np.linspace(0.0, 2 * np.pi, count, endpoint=False)
+    data = np.array(list(cbf_mean_dict.values()))
+    width = 1.8 * np.pi / count
+    subplot.set_theta_zero_location('N')
+    subplot.set_theta_direction(-1)
+    subplot.xaxis.set_ticks(theta)
+    subplot.yaxis.set_ticks([])
+
+    bars = subplot.bar(x=theta, height=data, width=width)
+    for data_value, bar in zip(data, bars):
+        bar.set_facecolor(plt.cm.turbo(data_value / 60))
+        bar.set_alpha(0.8)
+    for theta_value, data_value in zip(theta, data):
+        margin = data.max() * 0.08
+        subplot.annotate(str(round(data_value, 2)), xy=(theta_value, data_value + margin))
+
+    subplot.set_xticklabels(list(cbf_mean_dict.keys()))
+    for label, angle in zip(subplot.get_xticklabels(), theta):
+        x, y = label.get_position()
+        lab = subplot.text(x, y - 0.1, label.get_text(), transform=label.get_transform(), ha=label.get_ha(),
+                           va=label.get_va())
+        rotation = - np.rad2deg(angle) + 90
+        if rotation < -90:
+            rotation += 180
+        lab.set_rotation(rotation)
+    subplot.set_xticklabels([])
+
+    plt.title(f"221例健康人西门子5延迟数据各脑区{data_type}")
+    plt.savefig(save_path, dpi=200)
+    plt.cla()
+    plt.close('all')
+
+
+def main():
+    # 请尝试添加att参数，并调整色阶范围使得CBF=40时为红色，60时为蓝色
+    data_types = ['corr-CBF']
+    atlas_list = ['AnImage_AAL3']
+    output_dir = r'..\Data\polar-graph'
+    os.makedirs(output_dir, exist_ok=True)
+
+    input_file = r'..\Data\csv-simple-stat.xlsx'
+    workbook = openpyxl.load_workbook(input_file, data_only=True)
+
+    for data_type in data_types:
+        for atlas in atlas_list:
+            cbf_mean_dict = {}
+            worksheet = workbook[f'{atlas}_{data_type}']
+            for column_num in range(2, worksheet.max_column + 1):
+                name = worksheet.cell(row=1, column=column_num).value
+                mean = worksheet.cell(row=worksheet.max_row - 8, column=column_num).value
+                print(atlas, data_type, name, mean)
+                if type(mean) is float:
+                    cbf_mean_dict[name] = mean
+
+            draw_cbf_graph(cbf_mean_dict, data_type, os.path.join(output_dir, f'{atlas}_{data_type}.png'))
+
+
+main()

Exercise/exercise-1.1.py

@@ -0,0 +1,13 @@
+
+# 计算 12879 * 36521 并打印
+# product = ???
+# print(product)
+
+
+# 计算 100 模 32 并打印
+# mod = ???
+# print(mod)
+
+# 打印Hello\ "World!"
+# msg = ???
+# print(msg)

Exercise/exercise-1.2.py

@@ -0,0 +1,8 @@
+import math
+
+# 请输出以下关于圆的周长和面积的计算，保留3位小数
+radius = 3
+pi = math.pi
+
+# 应当输出 当圆的的半径是3时，它的周长是18.850，他的面积是28.274
+print('当圆的的半径是时，它的周长是，他的面积是')

Exercise/exercise-2.1.py

@@ -0,0 +1,9 @@
+# 请尝试修改list以满足输出
+
+list_a = [0]
+
+list_a += ['Hello'] * 5
+list_a += [4, 5] * 3
+
+# 输出[0, 'Hello', 'Hello', 'Hello', 'Hello', 'Hello', 4, 5, 4, 5, 4, 5]
+print(list_a)

Exercise/exercise-2.2.py

@@ -0,0 +1,18 @@
+
+# 我们用如下数组存储一些患者的信息
+patient_info = [
+    ["Zhang San", 52, "M"],
+    ["Li Si", 19, "F"],
+    ["Wang Wu", 47, "F"],
+]
+
+# 检查后发现，张三的年龄和李四的年龄写反了，请将他们调整过来
+
+
+
+
+# 应当为 19
+print(patient_info[0][1])
+
+# 应当为 52
+print(patient_info[1][1])

Exercise/exercise-2.3.py

@@ -0,0 +1,19 @@
+age_dict = {
+  "Zhang San": 52,
+  "Li Si": 19,
+  "Wang Wu": 47
+}
+
+# 检查后发现，张三的年龄和李四的年龄写反了，请将他们调整过来。并添加Zhao Liu，年龄为29
+
+
+
+
+# 应当为 19
+print(age_dict['Zhang San'])
+
+# 应当为 52
+print(age_dict['Li Si'])
+
+# 应当为 29
+print(age_dict['Zhao Liu'])

Exercise/exercise-3.1.py

@@ -0,0 +1,13 @@
+# 请根据用户输入的年龄输出在学校的阶段：
+# 0-5 输出 学龄前
+# 6-11 输出 小学
+# 12-14 输出 初中
+# 15-17 输出 高中
+# 18-21 输出 大学
+# 22-59 输出 打工人
+# 60+ 输出 退休了
+
+print('请输入你的年龄：')
+age = int(input())
+if age < 6:
+    print('学龄前')

Exercise/exercise-4.1.py

@@ -0,0 +1,33 @@
+
+
+# 我们希望将患者按照信息，分别按照性别和年龄分成4个组别，分别存储他们的姓名
+patient_info = [
+    ["Zhang San", 52, "M"],
+    ["Li Si", 19, "F"],
+    ["Wang Wu", 47, "F"],
+    ["Zhao Liu", 29, "M"],
+]
+group_M_under_30 = []
+group_M_over_30 = []
+group_F_under_30 = []
+group_F_over_30 = []
+
+for patient in patient_info:
+    print('new patient:', patient)
+    # 补充代码，使用条件判断，将患者分为4组，分别存储他们的姓名即可
+
+
+
+
+
+# 应当为 ["Zhao Liu"]
+print(group_M_under_30)
+
+# 应当为 ["Zhang San"]
+print(group_M_over_30)
+
+# 应当为 ["Li Si"]
+print(group_F_under_30)
+
+# 应当为 ["Wang Wu"]
+print(group_F_over_30)

Exercise/exercise-4.2.py

@@ -0,0 +1,15 @@
+# 给定一组整数，计算他们的和并输出等式
+
+magic_number_list = [4, 8, 15, 16, 23, 42]
+
+sum = 0
+expression = ''
+for index in range(len(magic_number_list)):
+    print(index)
+
+
+
+
+
+# 应输出4 + 8 + 15 + 16 + 23 + 42 = 108
+print(expression)

Exercise/exercise-4.3.py

@@ -0,0 +1,14 @@
+import random
+
+# 我们来写一个猜数字的小游戏
+
+# 生成一个随机的1-100整数作为答案
+answer = random.randint(1, 100)
+game_over = False
+count = 0
+while not game_over:
+    print(f"你已经猜了{count}次，请输入下一次猜测")
+    count += 1
+    guess = int(input())
+    print(f"你的猜测是{guess}")
+    # 如果猜对了，输出“猜对了”并结束游戏。否则输出“猜大了”或者“猜小了”

Exercise/exercise-5.1.py

@@ -0,0 +1,10 @@
+# 我们来输出100以内的素数
+
+def is_prime(n):
+    # 检查n是否是素数，一个简易的办法是，遍历小于n的自然数，判断是否有自然数可以整除n
+    return False
+
+
+for i in range(100):
+    # 遍历100以内的自然数，检查他们是不是素数，并输出其中的素数
+    result = is_prime(i)

Exercise/exercise-5.2.py

@@ -0,0 +1,23 @@
+import random
+
+
+# 概率计算表明，23个人中有至少两人生日相同的概率高达50.7%，听起来有些反直觉，我们来通过计算估计这一概率。
+def contains_same_birthday(birthdays):
+    # 修改代码，判断birthdays这个list中是否有相同的元素
+    return True
+
+
+# 我们进行10000次测试
+test_count = 10000
+# 有至少两人生日相同的次数
+true_count = 0
+
+for i in range(test_count):
+    birth_days = []
+    for j in range(23):
+        birth_days.append(random.randint(1, 365))
+
+    # 统计满足条件的测试次数
+
+# 输出测试结果，保留4位小数
+print('23个人中有至少两人生日相同的概率是:')

Exercise/exercise-5.3.py

@@ -0,0 +1,8 @@
+
+
+# 哥德巴赫猜想（Goldbach's conjecture）是数论中存在最久的未解问题之一。这个猜想最早出现在1742年普鲁士数学家克里斯蒂安·哥德巴赫与瑞士数学家莱昂哈德·欧拉的通信中。用现代的数学语言，哥德巴赫猜想可以陈述为：
+# 任一大于2的偶数，都可表示成两个素数之和
+# 验证1000以内偶数是否满足这一猜想，并输出每一个结果，例如
+# 4 = 2 + 2
+# 6 = 3 + 3
+# 8 = 3 + 5

README.md

@@ -1,2 +1,3 @@
-# python_for_asl
+# ASL成像基于Python的定量数据处理基础
+
 

requirements.txt

@@ -0,0 +1,32 @@
+attr==0.3.2
+ConfigParser==7.2.0
+contextlib2==21.6.0
+cryptography==45.0.5
+Cython==3.1.2
+dl==0.1.0
+docutils==0.22
+HTMLParser==0.0.2
+ipython==7.34.0
+ipywidgets==8.1.7
+Jinja2==3.0.3
+jnius==1.1.0
+keyring==25.6.0
+lockfile==0.12.2
+matplotlib==3.3.4
+mock==5.2.0
+numpy==1.21.6
+openpyxl==3.1.3
+Pillow==9.5.0
+Pillow==11.3.0
+protobuf==3.14.0
+pyOpenSSL==25.1.0
+pywin32==308
+railroad==0.5.0
+scipy==1.5.4
+Sphinx==8.2.3
+thread==2.0.5
+tornado==6.2
+trove_classifiers==2025.5.9.12
+truststore==0.10.3
+urllib3_secure_extra==0.1.0
+xmlrpclib==1.0.1