commit f085d7c5fe6461abd21ee8ceb3e01c54f541d3f7
Author: zhanghuilai <zhanghuilai@iris-rs.cn>
Date:   Mon Jan 5 09:33:27 2026 +0800

    Initial commit: GasFlux project with core processing pipelines

diff --git a/.gitignore b/.gitignore
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--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,140 @@
+# repo stuff
+survey
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# VSCode
+.vscode/
+
+# Node modules for this repo
+package.json
+package-lock.json
+node_modules/
\ No newline at end of file
diff --git a/.idea/.gitignore b/.idea/.gitignore
new file mode 100644
index 0000000..35410ca
--- /dev/null
+++ b/.idea/.gitignore
@@ -0,0 +1,8 @@
+# 默认忽略的文件
+/shelf/
+/workspace.xml
+# 基于编辑器的 HTTP 客户端请求
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
diff --git a/.idea/gasflux-develop.iml b/.idea/gasflux-develop.iml
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index 0000000..4afe101
--- /dev/null
+++ b/.idea/gasflux-develop.iml
@@ -0,0 +1,23 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="gasflux2" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="PyDocumentationSettings">
+    <option name="format" value="PLAIN" />
+    <option name="myDocStringFormat" value="Plain" />
+  </component>
+  <component name="TemplatesService">
+    <option name="TEMPLATE_CONFIGURATION" value="Jinja2" />
+    <option name="TEMPLATE_FOLDERS">
+      <list>
+        <option value="$MODULE_DIR$/src/gasflux/templates" />
+      </list>
+    </option>
+  </component>
+  <component name="TestRunnerService">
+    <option name="PROJECT_TEST_RUNNER" value="py.test" />
+  </component>
+</module>
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml
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index 0000000..099dd97
--- /dev/null
+++ b/.idea/inspectionProfiles/Project_Default.xml
@@ -0,0 +1,35 @@
+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredPackages">
+        <value>
+          <list size="22">
+            <item index="0" class="java.lang.String" itemvalue="catboost" />
+            <item index="1" class="java.lang.String" itemvalue="opencv-python" />
+            <item index="2" class="java.lang.String" itemvalue="xgboost" />
+            <item index="3" class="java.lang.String" itemvalue="opencv-python-headless" />
+            <item index="4" class="java.lang.String" itemvalue="zstandard" />
+            <item index="5" class="java.lang.String" itemvalue="scikit-image" />
+            <item index="6" class="java.lang.String" itemvalue="scipy" />
+            <item index="7" class="java.lang.String" itemvalue="tensorflow_gpu" />
+            <item index="8" class="java.lang.String" itemvalue="h5py" />
+            <item index="9" class="java.lang.String" itemvalue="matplotlib" />
+            <item index="10" class="java.lang.String" itemvalue="numpy" />
+            <item index="11" class="java.lang.String" itemvalue="opencv_python" />
+            <item index="12" class="java.lang.String" itemvalue="Pillow" />
+            <item index="13" class="java.lang.String" itemvalue="tensorflow" />
+            <item index="14" class="java.lang.String" itemvalue="jupyter" />
+            <item index="15" class="java.lang.String" itemvalue="ipykernel" />
+            <item index="16" class="java.lang.String" itemvalue="pandas" />
+            <item index="17" class="java.lang.String" itemvalue="Werkzeug" />
+            <item index="18" class="java.lang.String" itemvalue="cellpose" />
+            <item index="19" class="java.lang.String" itemvalue="torchvision" />
+            <item index="20" class="java.lang.String" itemvalue="Flask" />
+            <item index="21" class="java.lang.String" itemvalue="fiona" />
+          </list>
+        </value>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
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index 0000000..105ce2d
--- /dev/null
+++ b/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
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index 0000000..e03360e
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="gasflux_env" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="gasflux2" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
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index 0000000..2b86e9d
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/gasflux-develop.iml" filepath="$PROJECT_DIR$/.idea/gasflux-develop.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
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--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,72 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.5.0
+    hooks:
+      - id: check-added-large-files # prevents giant files from being committed
+        args: ["--maxkb=1024"]
+      - id: check-ast # simply checks whether the files parse as valid python
+      - id: check-builtin-literals # requires literal syntax when initializing empty or zero python builtin types
+      - id: check-case-conflict # checks for files that would conflict in case-insensitive filesystems
+      - id: check-docstring-first # checks a common error of defining a docstring after code
+      - id: check-executables-have-shebangs # ensures that (non-binary) executables have a shebang
+      - id: pretty-format-json # pretty formats json files
+      - id: check-json # checks json files for syntax errors
+      - id: check-merge-conflict # prevents git merge conflicts from being committed (finds conflict markers)
+      - id: check-shebang-scripts-are-executable # ensures that (non-binary) files with a shebang are executable
+      - id: check-toml # checks toml files for syntax errors
+      - id: check-vcs-permalinks # ensures that links to vcs websites are permalinks
+      - id: check-yaml # checks yaml files for parseable syntax
+      - id: debug-statements # checks for debugger imports and py37+ `breakpoint()` calls in python source
+      #      - id: detect-aws-credentials  # detects *your* aws credentials from the aws cli credentials file
+      - id: detect-private-key # detects the presence of private keys
+      - id: end-of-file-fixer # ensures that a file is either empty, or ends with one newline
+        files: ^.*py$
+      - id: mixed-line-ending # replaces or checks mixed line ending
+        files: ^.*py$
+      - id: no-commit-to-branch
+        args: ["--branch", "main", "--branch", "develop"]
+      - id: requirements-txt-fixer # sorts entries in requirements.txt
+      - id: trailing-whitespace # trims trailing whitespace
+        files: ^.*py$
+
+  - repo: local
+    hooks:
+      - id: nbdev
+        name: nbdev clean
+        entry: nbdev_clean --clear_all
+        pass_filenames: false
+        language: python
+        files: .ipynb
+
+      - id: mypy
+        name: mypy
+        entry: mypy
+        language: python
+        files: ^.*py$
+
+      - id: vulture
+        name: vulture
+        entry: vulture
+        args: ["--min-confidence", "61"]
+        language: python
+        files: ^.*py$
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.3.5
+    hooks:
+      - id: ruff
+        types_or: [python, pyi, jupyter]
+        args: [--fix]
+      - id: ruff-format
+        types_or: [python, pyi, jupyter]
+# ci:
+#   autofix_commit_msg: |
+#     [pre-commit.ci] auto fixes from pre-commit.com hooks
+
+#     for more information, see https://pre-commit.ci
+#   autofix_prs: true
+#   autoupdate_branch: ""
+#   autoupdate_commit_msg: "[pre-commit.ci] pre-commit autoupdate"
+#   autoupdate_schedule: monthly
+#   skip: []
+#   submodules: false
diff --git a/3DVIEW_README.md b/3DVIEW_README.md
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+++ b/3DVIEW_README.md
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+# 3D轨迹可视化工具
+
+一个用于将GPS轨迹数据转换为可交互3D可视化的Python工具。
+
+## 功能特性
+
+- 🎯 **多格式支持**: 支持CSV和Excel文件 (.csv, .xlsx, .xls)
+- 📊 **3D交互式图表**: 基于Plotly的完全可交互3D可视化
+- 🎨 **智能着色**: 按高程自动着色，颜色映射到海拔高度
+- 🔧 **灵活配置**: 可自定义列索引、工作表等参数
+- 📱 **响应式设计**: 生成的HTML可在任何现代浏览器中查看
+
+## 数据格式要求
+
+### 文件格式
+- **CSV文件**: 逗号分隔值文件
+- **Excel文件**: .xlsx 或 .xls 格式
+
+### 数据列要求
+文件至少需要包含4列数据，按以下顺序排列：
+
+| 列索引 | 列名 | 数据类型 | 说明 |
+|--------|------|----------|------|
+| 0 | 经度 | 数值 | GPS经度坐标（原始格式） |
+| 1 | 纬度 | 数值 | GPS纬度坐标（原始格式） |
+| 2 | 高程 | 数值 | 飞行器高程（可选） |
+| 3 | 融合高程 | 数值 | 融合后的高程数据 |
+
+### 数据示例
+
+**CSV格式**:
+```csv
+1157855289,403491370,500110,435.789
+1157855290,403491371,500111,436.123
+1157855291,403491372,500112,437.456
+```
+
+**Excel格式**:
+- 第一行（如果有标题）将被忽略
+- 数据从第二行开始读取
+
+### 坐标转换
+- 经纬度数据会自动除以 10^7 进行单位转换
+- 这是因为原始GPS数据通常以放大整数形式存储
+
+## 安装依赖
+
+```bash
+pip install pandas plotly openpyxl
+```
+
+## 使用方法
+
+### 1. 命令行使用
+
+#### 基本用法
+```bash
+# 处理CSV文件
+python 3Dview.py data.csv
+
+# 处理Excel文件
+python 3Dview.py trajectory.xlsx
+
+# 指定输出文件
+python 3Dview.py data.csv -o my_visualization.html
+```
+
+#### 高级参数
+```bash
+# 自定义列索引
+python 3Dview.py data.xlsx --lon-col 1 --lat-col 2 --alt-col 4
+
+# 指定Excel工作表
+python 3Dview.py data.xlsx --sheet-name "Flight_Data"
+
+# 自定义标题
+python 3Dview.py data.csv --title "无人机飞行轨迹"
+
+# 组合使用
+python 3Dview.py trajectory.xlsx \
+    --lon-col 0 \
+    --lat-col 1 \
+    --alt-col 3 \
+    --sheet-name 0 \
+    --output flight_path.html \
+    --title "2024年飞行数据"
+```
+
+### 2. Python代码调用
+
+#### 基本调用
+```python
+from 3Dview import main
+
+# 处理CSV文件
+success = main('trajectory.csv')
+
+# 处理Excel文件
+success = main('flight_data.xlsx', sheet_name='Sheet1')
+```
+
+#### 完整参数调用
+```python
+from 3Dview import main
+
+success = main(
+    input_file='drone_trajectory.xlsx',
+    output_file='visualization.html',
+    lon_col=0,          # 经度列索引
+    lat_col=1,          # 纬度列索引
+    alt_col=3,          # 融合高程列索引
+    sheet_name=0,       # Excel工作表索引
+    title='无人机轨迹3D可视化'
+)
+
+if success:
+    print("可视化生成成功！")
+```
+
+## 输出结果
+
+### HTML文件内容
+- **3D散点图**: 每个数据点在三维空间中的位置
+- **颜色编码**: 点的颜色根据高程自动映射
+- **交互功能**: 支持旋转、缩放、平移等操作
+
+### 控制说明
+- **鼠标左键拖拽**: 旋转视角
+- **鼠标滚轮**: 缩放视图
+- **鼠标右键拖拽**: 平移视图
+- **双击**: 重置到初始视角
+- **悬停**: 显示数据点的详细信息
+
+## 参数说明
+
+### 命令行参数
+
+| 参数 | 简称 | 默认值 | 说明 |
+|------|------|--------|------|
+| `input_file` | - | 必需 | 输入文件路径 |
+| `--output` | `-o` | `3d_view.html` | 输出HTML文件路径 |
+| `--lon-col` | - | `0` | 经度列索引 |
+| `--lat-col` | - | `1` | 纬度列索引 |
+| `--alt-col` | - | `3` | 融合高程列索引 |
+| `--sheet-name` | - | `0` | Excel工作表名称或索引 |
+| `--delimiter` | - | `None` | CSV分隔符（自动检测） |
+| `--title` | - | `3D轨迹可视化` | 图表标题 |
+
+### 函数参数
+
+```python
+def main(
+    input_file,           # 输入文件路径
+    output_file='3d_view.html',     # 输出文件路径
+    lon_col=0,            # 经度列索引
+    lat_col=1,            # 纬度列索引
+    alt_col=3,            # 高程列索引
+    delimiter=None,       # CSV分隔符
+    title='3D轨迹可视化', # 图表标题
+    sheet_name=0          # Excel工作表
+):
+```
+
+## 故障排除
+
+### 常见问题
+
+#### 1. 文件读取错误
+```
+错误：输入文件不存在: xxx
+```
+**解决**: 检查文件路径是否正确，文件是否存在
+
+#### 2. 列数不足
+```
+数据文件至少需要4列，经度、纬度、高程、融合高程。当前只有X列。
+```
+**解决**: 检查数据文件格式，确保包含所需列
+
+#### 3. Excel工作表不存在
+```
+Worksheet 'Sheet1' not found
+```
+**解决**: 检查Excel文件中的工作表名称，或使用索引（如0）
+
+#### 4. 坐标转换问题
+如果坐标显示不正确，检查：
+- 经纬度数据格式是否正确
+- 单位转换是否合适（除以10^7）
+
+### 数据验证
+
+运行脚本时会显示以下统计信息：
+```
+成功加载数据：XXX 个数据点
+经度范围: XXX.XXXXXX - XXX.XXXXXX
+纬度范围: XXX.XXXXXX - XXX.XXXXXX
+高程范围: XXX.XX - XXX.XX 米
+```
+
+## 示例应用
+
+### 无人机飞行轨迹
+```python
+from 3Dview import main
+
+# 可视化无人机飞行路径
+main(
+    input_file='drone_flight.xlsx',
+    output_file='drone_path_3d.html',
+    title='无人机飞行轨迹3D可视化',
+    sheet_name='GPS_Data'
+)
+```
+
+### 地面车辆轨迹
+```python
+from 3Dview import main
+
+# 可视化车辆行驶轨迹
+main(
+    input_file='vehicle_gps.csv',
+    output_file='vehicle_path.html',
+    title='车辆轨迹可视化',
+    alt_col=2  # 如果没有高程数据，使用其他列
+)
+```
+
+## 技术细节
+
+### 依赖库
+- **pandas**: 数据处理
+- **plotly**: 3D可视化
+- **openpyxl**: Excel文件读取
+
+### 坐标系统
+- 输入: 原始GPS坐标（通常为度×10^-7）
+- 处理: 转换为标准经纬度坐标
+- 显示: WGS84地理坐标系
+
+### 性能考虑
+- 大文件处理: 建议分批处理超大数据集
+- 内存使用: 每个数据点占用约50-100字节内存
+- 渲染性能: 浏览器对大量数据点的渲染有限制
+
+## 许可证
+
+本工具遵循与主项目相同的许可证。
+
+---
+
+**版本**: 1.0.0
+**最后更新**: 2024年12月
diff --git a/3Dview.py b/3Dview.py
new file mode 100644
index 0000000..e87e42e
--- /dev/null
+++ b/3Dview.py
@@ -0,0 +1,370 @@
+#!/usr/bin/env python3
+"""
+3D可视化脚本 - 显示经纬度高程数据
+
+读取CSV文件中的经度、纬度、融合高程数据，
+生成可交互的三维点图，输出为HTML格式。
+
+数据格式要求：
+经度 纬度 高程 融合高程
+
+经纬度坐标需要除以10^-7进行单位转换。
+"""
+
+import pandas as pd
+import plotly.graph_objects as go
+import plotly.io as pio
+import argparse
+import sys
+import numpy as np
+from pathlib import Path
+
+def load_and_process_data(file_path, lon_col=0, lat_col=1, alt_col=3, delimiter=None, sheet_name=0):
+    """
+    加载并处理CSV或Excel数据
+
+    Args:
+        file_path: 文件路径 (支持.csv, .xlsx, .xls)
+        lon_col: 经度列索引 (默认0)
+        lat_col: 纬度列索引 (默认1)
+        alt_col: 高程列索引 (默认3，为融合高程)
+        delimiter: CSV分隔符，None表示自动检测
+        sheet_name: Excel工作表名称或索引 (默认0)
+
+    Returns:
+        pandas.DataFrame: 处理后的数据
+    """
+    try:
+        file_path_obj = Path(file_path)
+
+        # 根据文件扩展名选择读取方式
+        if file_path_obj.suffix.lower() in ['.xlsx', '.xls']:
+            # 读取Excel文件
+            print(f"检测到Excel文件: {file_path}")
+            df = pd.read_excel(file_path, sheet_name=sheet_name, header=None)
+        else:
+            # 读取CSV文件
+            print(f"检测到CSV文件: {file_path}")
+            df = pd.read_csv(file_path, delimiter=delimiter, header=None)
+
+        # 验证数据列数
+        if df.shape[1] < 4:
+            raise ValueError(f"数据文件至少需要4列，经度、纬度、高程、融合高程。当前只有{df.shape[1]}列。")
+
+        # 显示前几行数据以便调试
+        print(f"数据预览 (前3行):")
+        print(df.head(3))
+        print()
+
+        # 提取所需列
+        lon_raw = df.iloc[:, lon_col]  # 经度（原始格式）
+        lat_raw = df.iloc[:, lat_col]  # 纬度（原始格式）
+        altitude = df.iloc[:, alt_col]  # 融合高程
+
+        # 数据类型转换和清理
+        try:
+            lon_raw = pd.to_numeric(lon_raw, errors='coerce')
+            lat_raw = pd.to_numeric(lat_raw, errors='coerce')
+            altitude = pd.to_numeric(altitude, errors='coerce')
+        except Exception as e:
+            print(f"数据类型转换失败: {e}")
+            print("尝试手动转换...")
+
+            # 手动转换函数
+            def safe_convert(value):
+                try:
+                    return float(value)
+                except (ValueError, TypeError):
+                    return np.nan
+
+            lon_raw = lon_raw.apply(safe_convert)
+            lat_raw = lat_raw.apply(safe_convert)
+            altitude = altitude.apply(safe_convert)
+
+        # 检查转换结果
+        nan_count_lon = lon_raw.isna().sum()
+        nan_count_lat = lat_raw.isna().sum()
+        nan_count_alt = altitude.isna().sum()
+
+        if nan_count_lon > 0 or nan_count_lat > 0 or nan_count_alt > 0:
+            print(f"警告：发现无效数据点 - 经度: {nan_count_lon}, 纬度: {nan_count_lat}, 高程: {nan_count_alt}")
+            print("无效数据点将被移除")
+
+        # 移除NaN值
+        valid_mask = ~(lon_raw.isna() | lat_raw.isna() | altitude.isna())
+        lon_raw = lon_raw[valid_mask]
+        lat_raw = lat_raw[valid_mask]
+        altitude = altitude[valid_mask]
+
+        if len(lon_raw) == 0:
+            raise ValueError("没有有效的坐标数据")
+
+        # 经纬度单位转换 (除以10^7)
+        longitude = lon_raw / 1e7
+        latitude = lat_raw / 1e7
+
+        # 创建处理后的DataFrame
+        processed_df = pd.DataFrame({
+            'longitude': longitude,
+            'latitude': latitude,
+            'altitude': altitude
+        })
+
+        print(f"成功加载数据：{len(processed_df)} 个数据点")
+        print(f"经度范围: {longitude.min():.6f} - {longitude.max():.6f}")
+        print(f"纬度范围: {latitude.min():.6f} - {latitude.max():.6f}")
+        print(f"高程范围: {altitude.min():.2f} - {altitude.max():.2f} 米")
+
+        return processed_df
+
+    except Exception as e:
+        print(f"数据加载失败: {e}")
+        import traceback
+        traceback.print_exc()
+        sys.exit(1)
+
+def create_3d_plot(df, title="3D轨迹可视化", output_file="3d_view.html"):
+    """
+    创建三维散点图
+
+    Args:
+        df: 包含longitude, latitude, altitude列的DataFrame
+        title: 图表标题
+        output_file: 输出HTML文件路径
+    """
+
+    # 创建3D散点图
+    fig = go.Figure(data=[go.Scatter3d(
+        x=df['longitude'],
+        y=df['latitude'],
+        z=df['altitude'],
+        mode='markers',
+        marker=dict(
+            size=3,
+            color=df['altitude'],  # 颜色映射到高程
+            colorscale='Viridis',  # 颜色方案
+            colorbar=dict(title="高程 (m)"),
+            opacity=0.8
+        ),
+        hovertemplate=(
+            '经度: %{x:.6f}<br>' +
+            '纬度: %{y:.6f}<br>' +
+            '高程: %{z:.2f} m<br>' +
+            '<extra></extra>'
+        )
+    )])
+
+    # 设置布局
+    fig.update_layout(
+        title=title,
+        scene=dict(
+            xaxis_title='经度',
+            yaxis_title='纬度',
+            zaxis_title='高程 (m)',
+            xaxis=dict(showgrid=True, gridcolor='lightgray'),
+            yaxis=dict(showgrid=True, gridcolor='lightgray'),
+            zaxis=dict(showgrid=True, gridcolor='lightgray'),
+            # 设置视角
+            camera=dict(
+                eye=dict(x=1.5, y=1.5, z=1.5)  # 初始视角位置
+            )
+        ),
+        margin=dict(l=0, r=0, b=0, t=40),
+        paper_bgcolor='white',
+        plot_bgcolor='white'
+    )
+
+    # 添加交互说明
+    fig.add_annotation(
+        text="鼠标拖拽旋转视角，滚轮缩放，双击重置",
+        xref="paper", yref="paper",
+        x=0.02, y=0.98,
+        showarrow=False,
+        bgcolor="rgba(255,255,255,0.8)",
+        bordercolor="black",
+        borderwidth=1
+    )
+
+    # 保存为HTML文件
+    try:
+        pio.write_html(fig, file=output_file, auto_open=False)
+        print(f"3D可视化已保存到: {output_file}")
+        print("在浏览器中打开该文件即可查看可交互的3D图表")
+    except Exception as e:
+        print(f"保存HTML文件失败: {e}")
+        sys.exit(1)
+
+    return fig
+
+def main(input_file, output_file='3d_view.html', lon_col=2, lat_col=3, alt_col=5,
+         delimiter=None, title='3D轨迹可视化', sheet_name=0):
+    """
+    直接调用函数生成3D可视化
+
+    Args:
+        input_file: 输入文件路径 (支持.csv, .xlsx, .xls)
+        output_file: 输出HTML文件路径
+        lon_col: 经度列索引 (默认: 0)
+        lat_col: 纬度列索引 (默认: 1)
+        alt_col: 融合高程列索引 (默认: 3)
+        delimiter: CSV分隔符 (默认: 自动检测)
+        title: 图表标题 (默认: '3D轨迹可视化')
+        sheet_name: Excel工作表名称或索引 (默认: 0)
+    """
+
+    # 检查输入文件
+    input_path = Path(input_file)
+    if not input_path.exists():
+        print(f"错误：输入文件不存在: {input_path}")
+        return False
+
+    print(f"正在处理文件: {input_path}")
+    print(f"输出文件: {output_file}")
+    print("-" * 50)
+
+    try:
+        # 加载和处理数据
+        df = load_and_process_data(
+            file_path=input_path,
+            lon_col=lon_col,
+            lat_col=lat_col,
+            alt_col=alt_col,
+            delimiter=delimiter,
+            sheet_name=sheet_name
+        )
+
+        # 创建3D图表
+        fig = create_3d_plot(
+            df=df,
+            title=title,
+            output_file=output_file
+        )
+
+        print("-" * 50)
+        print("处理完成！")
+        print(f"\n✅ 3D可视化已保存到: {output_file}")
+        print("\n使用说明:")
+        print("- 在浏览器中打开生成的HTML文件")
+        print("- 鼠标左键拖拽：旋转视角")
+        print("- 鼠标滚轮：缩放视图")
+        print("- 双击：重置到初始视角")
+        print("- 右键拖拽：平移视图")
+
+        return True
+
+    except Exception as e:
+        print(f"❌ 处理失败: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+# 命令行接口（保持兼容性）
+def cli_main():
+    """命令行接口"""
+    parser = argparse.ArgumentParser(
+        description="3D轨迹可视化工具 - 将CSV数据转换为可交互的3D点图",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+使用示例:
+  # CSV文件
+  python 3Dview.py data.csv
+  python 3Dview.py data.csv -o custom_output.html
+  python 3Dview.py data.csv --lon-col 1 --lat-col 2 --alt-col 4
+
+  # Excel文件
+  python 3Dview.py trajectory.xlsx
+  python 3Dview.py data.xlsx --sheet-name "Sheet1"
+
+数据格式:
+  文件应包含至少4列：经度 纬度 高程 融合高程
+  支持格式: CSV (.csv), Excel (.xlsx, .xls)
+  经纬度将自动除以10^7进行单位转换
+
+直接调用示例:
+  from 3Dview import main
+  main('data.xlsx', sheet_name='Sheet1')  # Excel文件
+  main('data.csv', lon_col=0, lat_col=1, alt_col=3)  # CSV文件
+        """
+    )
+
+    parser.add_argument('input_file', help='输入CSV文件路径')
+    parser.add_argument('-o', '--output', default='3d_view.html',
+                       help='输出HTML文件路径 (默认: 3d_view.html)')
+    parser.add_argument('--lon-col', type=int, default=0,
+                       help='经度列索引 (默认: 0)')
+    parser.add_argument('--lat-col', type=int, default=1,
+                       help='纬度列索引 (默认: 1)')
+    parser.add_argument('--alt-col', type=int, default=3,
+                       help='融合高程列索引 (默认: 3)')
+    parser.add_argument('--delimiter', default=None,
+                       help='CSV分隔符 (默认: 自动检测)')
+    parser.add_argument('--title', default='3D轨迹可视化',
+                       help='图表标题 (默认: 3D轨迹可视化)')
+    parser.add_argument('--sheet-name', default=0,
+                       help='Excel工作表名称或索引 (默认: 0)')
+
+    args = parser.parse_args()
+
+    success = main(
+        input_file=args.input_file,
+        output_file=args.output,
+        lon_col=args.lon_col,
+        lat_col=args.lat_col,
+        alt_col=args.alt_col,
+        delimiter=args.delimiter,
+        title=args.title,
+        sheet_name=args.sheet_name
+    )
+
+    if not success:
+        sys.exit(1)
+
+# 示例用法
+if __name__ == "__main__":
+    # 检查是否有命令行参数
+    if len(sys.argv) > 1:
+        # 使用命令行接口
+        cli_main()
+    else:
+        # 直接调用示例
+        print("3D可视化工具使用示例")
+        print("=" * 50)
+
+        # 示例1: 处理Excel文件
+        print("\n示例1: 处理Excel数据文件")
+        excel_file = r"C:\Users\HL\Documents\xwechat_files\wxid_rdrswaol1qsr21_737e\msg\file\2025-12\08_51_15_间隔高度10m.xlsx"
+
+        if Path(excel_file).exists():
+            success = main(
+                input_file=excel_file,
+                output_file='./10m_3d_view.html',
+                title='无人机轨迹3D可视化示例'
+            )
+        else:
+            print(f"示例文件不存在: {excel_file}")
+            print("请提供正确的Excel文件路径")
+            success = False
+
+        if success:
+            print("\n示例代码:")
+            print("""
+# 直接调用示例
+from 3Dview import main
+
+# 处理Excel文件
+main('data.xlsx', sheet_name=0)  # 指定工作表
+
+# 处理CSV文件
+main('data.csv')
+
+# 自定义参数
+main(
+    input_file='trajectory.xlsx',
+    output_file='flight_path.html',
+    lon_col=0,      # 经度列索引
+    lat_col=1,      # 纬度列索引
+    alt_col=3,      # 融合高程列索引
+    sheet_name=0,   # Excel工作表
+    title='飞行轨迹可视化'
+)
+            """)
diff --git a/DATA_PROCESSOR_README.md b/DATA_PROCESSOR_README.md
new file mode 100644
index 0000000..566cfab
--- /dev/null
+++ b/DATA_PROCESSOR_README.md
@@ -0,0 +1,295 @@
+# 无人机数据处理工具
+
+将Excel格式的无人机测量数据转换为GasFlux标准输入格式的完整解决方案。
+
+## 📋 处理流程
+
+### 输入数据格式
+Excel文件必须包含以下列（顺序可以不同）：
+
+| 列名 | 数据类型 | 说明 |
+|------|----------|------|
+| `日期` | 字符串 | 日期 (YYYY-MM-DD 或 YYYY/MM/DD) |
+| `时间` | 字符串 | 时间 (HH:MM:SS 或 H:MM:SS) |
+| `经度` | 数值 | GPS经度坐标 (原始大数值) |
+| `纬度` | 数值 | GPS纬度坐标 (原始大数值) |
+| `高程` | 数值 | 飞行器高程 |
+| `融合高程` | 数值 | 融合后的海拔高度 |
+| `速度x` | 数值 | 东向速度分量 |
+| `速度y` | 数值 | 北向速度分量 |
+| `速度z` | 数值 | 垂直速度分量 |
+| `四元数_q0-q3` | 数值 | 姿态四元数 |
+| `pitch` | 数值 | 俯仰角 |
+| `roll` | 数值 | 横滚角 |
+| `yaw` | 数值 | 偏航角 |
+| `CH4` | 数值 | 甲烷浓度 |
+| `H2O` | 数值 | 水蒸气浓度 |
+| `修正风向` | 数值 | 修正后的风向 |
+| `修正风速` | 数值 | 修正后的风速 |
+| `原始风向` | 数值 | 原始风向 |
+| `原始风速` | 数值 | 原始风速 |
+| `风温` | 数值 | 风温 (温度) |
+
+## 🔄 处理步骤详解
+
+### 1. 列删除
+自动删除以下不需要的列：
+- `高程`, `速度x`, `速度y`, `速度z`
+- `四元数_q0`, `四元数_q1`, `四元数_q2`, `四元数_q3`
+- `pitch`, `roll`, `yaw`
+- `H2O`, `原始风向`, `原始风速`
+
+### 2. 时间修正
+根据文件名提取小时信息并修正时间列：
+
+**文件名示例**: `08_34_01_间隔高度5m.xlsx`
+- 提取: `08` (小时)
+- 时间转换: `0:34:01` → `08:34:01`
+
+### 3. 坐标转换
+经纬度坐标除以 `10^7` 进行单位转换：
+```
+原始经度: 1157855289 → 转换后: 115.7855289
+原始纬度: 403491370 → 转换后: 40.3491370
+```
+
+### 4. 气压计算（智能优化）
+使用 `qiya.py` 模块智能计算地面气压，支持两种计算模式：
+
+#### 🎯 三级智能计算策略
+- **高度变化小**（默认容差≤10米）：只计算一次平均气压，应用到所有行
+- **中等变化**：使用高度分档策略，每个档位计算一次气压
+- **高度变化大**：逐行计算气压，支持进度条显示
+
+#### 📏 高度分档优化
+- **分档间隔**：默认2米一个档位（可自定义）
+- **档位计算**：每个档位使用中间高度和平均位置计算一次气压
+- **减少API调用**：大幅降低重复计算，提高效率
+
+#### 📊 计算参数
+- **日期格式**: 转换为 `YYYY-MM-DD`（自动提取日期部分）
+- **时间格式**: 提取小时部分 `HH`
+- **海拔高度**: 使用 `融合高程` 列或档位中心高度
+- **API调用**: Open-Meteo 历史天气数据
+- **进度条**: 自动显示计算进度（需要安装tqdm）
+
+#### ⚙️ 自定义参数
+```python
+# 自定义参数
+df = calculate_pressure(
+    df,
+    max_samples=None,
+    height_tolerance=10.0,    # 高度变化容差
+    height_bin_size=2.0       # 高度分档间隔
+)
+```
+
+### 5. 高度调整
+对 `融合高程` 进行基准调整：
+```
+调整后高度 = 原始高度 - 最小高度
+```
+
+### 6. 时间戳融合
+将 `日期` 和 `时间` 列合并：
+```
+输入: 日期="2024-01-15", 时间="08:34:01"
+输出: timestamp="2024-01-15 08:34:01"
+```
+
+### 7. 字段重命名
+转换为GasFlux标准字段名：
+
+| 原始字段 | GasFlux字段 | 数据类型 |
+|----------|-------------|----------|
+| `日期+时间` | `timestamp` | datetime |
+| `经度` | `latitude` | float |
+| `纬度` | `longitude` | float |
+| `融合高程` | `height_ato` | float |
+| `修正风向` | `windir` | float |
+| `修正风速` | `windspeed` | float |
+| `风温` | `temperature` | float |
+| `气压计算结果` | `pressure` | float |
+| `CH4` | `ch4` | float |
+
+## 🚀 使用方法
+
+### 1. 交互式输入模式（推荐新用户）
+```bash
+# 直接运行脚本，会自动进入交互式模式
+python data_processor.py
+
+# 或者明确指定交互式模式
+python data_processor.py --interactive
+```
+
+在交互式模式下，程序会提示您：
+- 输入Excel文件路径
+- 指定输出CSV文件路径（可选）
+- 确认处理参数
+
+### 2. 命令行参数模式
+```bash
+# 基本用法
+python data_processor.py your_data.xlsx
+
+# 指定输出路径
+python data_processor.py input.xlsx -o output.csv
+
+# 查看帮助
+python data_processor.py --help
+```
+
+### 3. Python脚本直接调用（推荐）
+
+```python
+# 最简单的方式
+from src.gasflux.data_processor import process_file
+
+# 处理文件并返回DataFrame
+df = process_file('your_data.xlsx')
+
+# 或者指定输出文件名
+df = process_file('input.xlsx', 'output.csv')
+
+# 使用返回的DataFrame进行进一步分析
+print(df.head())
+print(df['ch4'].describe())
+```
+
+### 4. 完整使用脚本
+复制以下代码到新文件（如 `process_my_data.py`）：
+
+```python
+from src.gasflux.data_processor import process_file
+
+# 修改这里的输入文件名
+input_file = "08_34_01_间隔高度5m.xlsx"  # 您的Excel文件名
+output_file = "processed_data.csv"  # 输出文件名（可选）
+
+try:
+    df = process_file(input_file, output_file)
+    print("✅ 处理成功！")
+    print(f"数据形状: {df.shape}")
+    print(f"列名: {list(df.columns)}")
+    print(df.head())
+except Exception as e:
+    print(f"❌ 处理失败: {e}")
+```
+
+### Python代码调用
+
+```python
+from src.gasflux.data_processor import process_excel_file
+
+# 处理单个文件
+df = process_excel_file('path/to/data.xlsx')
+
+# 查看结果
+print(df.head())
+print(df.columns)
+```
+
+## 📊 输出格式
+
+处理后的数据完全符合GasFlux输入要求：
+
+```csv
+timestamp,latitude,longitude,height_ato,windspeed,windir,temperature,pressure,ch4
+2024-01-15 08:34:01,115.785529,40.349137,10.5,3.2,180.5,22.3,1013.25,2.15
+2024-01-15 08:34:02,115.785530,40.349138,10.8,3.1,181.2,22.4,1013.26,2.18
+...
+```
+
+## ⚙️ 配置选项
+
+### 文件名格式
+脚本根据文件名自动提取时间信息：
+- 格式: `{HH}_{MM}_{SS}_{其他信息}.xlsx`
+- 示例: `08_34_01_间隔高度5m.xlsx` → 小时=08
+
+### 气压计算
+- 默认只计算前20行数据（避免过多API调用）
+- 可以修改 `max_samples` 参数调整计算数量
+
+## 🔧 依赖要求
+
+```bash
+pip install pandas openpyxl requests
+```
+
+确保GasFlux的`qiya.py`模块可用。
+
+## ⚠️ 注意事项
+
+### 数据质量
+- 确保Excel文件格式正确
+- 检查日期时间格式
+- 验证经纬度坐标范围
+
+### API限制
+- Open-Meteo有每日请求限制
+- 批量处理时注意请求频率
+- 网络连接稳定很重要
+
+### 内存使用
+- 大文件处理时注意内存占用
+- 可以分批处理超大数据集
+
+## 🎯 应用场景
+
+### 无人机通量测量
+1. **数据采集**: 无人机飞行收集原始数据
+2. **数据处理**: 使用本脚本转换为标准格式
+3. **通量计算**: 输入GasFlux进行气体通量分析
+
+### 数据预处理工作流
+```
+原始Excel → 数据处理脚本 → GasFlux格式 → 通量计算 → 结果分析
+```
+
+## 📈 处理结果验证
+
+处理完成后检查：
+- ✅ 时间戳格式正确 (YYYY-MM-DD HH:MM:SS)
+- ✅ 经纬度坐标合理范围
+- ✅ 高度值从0开始 (已减去最小值)
+- ✅ 气压值在合理范围 (900-1100 hPa)
+- ✅ 所有必需字段都存在
+
+## 🛠️ 故障排除
+
+### 常见问题
+
+#### 1. 文件读取错误
+```
+确保Excel文件未损坏，列名与预期一致
+```
+
+#### 2. 时间格式错误
+```
+检查文件名格式，确包含小时信息 (HH_MM_SS...)
+```
+
+#### 3. 气压计算失败
+```
+检查网络连接，确认日期格式正确
+```
+
+#### 4. 坐标转换异常
+```
+验证经纬度数据是数值类型，不是字符串
+```
+
+## 📞 支持
+
+如遇问题，请检查：
+1. 输入数据格式是否正确
+2. 网络连接是否正常
+3. GasFlux依赖是否完整安装
+4. Python版本兼容性
+
+---
+
+**版本**: 1.0.0
+**最后更新**: 2024年12月
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..0ad25db
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,661 @@
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+  14. Revised Versions of this License.
+
+  The Free Software Foundation may publish revised and/or new versions of
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+will be similar in spirit to the present version, but may differ in detail to
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+  Each version is given a distinguishing version number.  If the
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+version or of any later version published by the Free Software
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+  Later license versions may give you additional or different
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+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
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+  17. Interpretation of Sections 15 and 16.
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+  If the disclaimer of warranty and limitation of liability provided
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+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
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+  To do so, attach the following notices to the program.  It is safest
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+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published
+    by the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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+  You should also get your employer (if you work as a programmer) or school,
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diff --git a/README.md b/README.md
new file mode 100644
index 0000000..80dc64b
--- /dev/null
+++ b/README.md
@@ -0,0 +1,75 @@
+
+[![License: AGPL v3](https://img.shields.io/badge/License-AGPL%20v3-blue.svg)](https://www.gnu.org/licenses/agpl-3.0)
+[![Tests](https://github.com/gasflux/gasflux/workflows/CI/badge.svg)](https://github.com/gasflux/gasflux/actions?query=workflow%3A%22CI%22)
+[![linting - Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v0.json)](https://github.com/charliermarsh/ruff)
+[![format - Black](https://img.shields.io/badge/format-black-000000.svg)](https://github.com/psf/black)
+[![types - mypy](https://img.shields.io/badge/types-mypy-blue.svg)](https://github.com/python/mypy)
+
+# GasFlux
+
+`pip install gasflux` \
+`gasflux --help`
+
+📖 **[详细用户指南](USER_GUIDE.md)** - 完整的功能说明和使用教程
+
+GasFlux is a tool for processing atmospheric gas concentration data and windspeeds into mass emissions fluxes, with principle applications to greenhouse gas measurement and vulcanology. Currently it works with in situ ("sniffing") data from UAVs and other aircraft, using mass balance as a paradigm and kriging as an interpolation strategy, but the intention is to expand this to other kinds of sampling and processing strategies, such as open-path and tracer methods.
+
+It is released under the AGPLv3 license as a free and open-source project - comments, pull requests, issues and co-development are warmly welcomed. Currently development is co-ordinated by Jamie McQuilkin ([@pipari](https://github.com/pipari)) at the UAV Greenhouse Gas group at the University of Manchester.
+
+## User Installation
+
+The package is available on PyPi and can be installed using `pip install gasflux`.
+
+## Usage
+
+The package interface is in active development. Currently it ingests a data csv file (or folder containing only data csv files) and a config file that dictates the parameters of the analysis.
+
+This is done through the syntax `gasflux process <input_file> --config <config_file>`.
+
+### The config file
+
+The default gasflux_config.yaml is located in the package source. It can be generated in a supplied directory using `gasflux generate-config <path>`. If a directory is supplied to `gasflux process` and a config is not also explicitly supplied, the package will look for one config file in that directory or its subdirectories and attempt to process all csv files in that directory and subdirectories. If multiple or no config is found, it will raise an error. If supplying a single csv file for processing (rather than a directory), only the parent directory will be searched for config files (not its subdirectories).
+
+Through it, variables can be passed to the [scikit-gstat](https://scikit-gstat.readthedocs.io/en/latest/) package used for kriging and the [pybaselines](https://pybaselines.readthedocs.io/en/latest/) package used for background correction.
+
+### The data file
+
+Input data files must be csv-type (i.e. readable by `pandas`) and have the following columns (all lower case):
+
+- `timestamp` (datetime)
+- `latitude` (float)
+- `longitude` (float)
+- `height_ato` (float) - height above takeoff
+- `windspeed` (float) - in m/s, as measured or inferred at each measurement point
+- `winddir` (float) - in standard 0-360 degree format, relative to the earth
+- `temperature` (float) in degrees celsius
+- `pressure` (float) - in hPa/mBar
+
+At least one gas concentration in ppm is also required. The column name should be the gas name, e.g. `co2`, `ch4`, `n2o`, etc.
+
+The gas should be entered in the gasflux_config.yaml file along with a range of concentrations in ppmv, e.g.:
+
+```
+gases:
+  ch4: [1.5, 500]
+  co2: [300, 5000]
+  c2h6: [-0.5, 10]
+```
+
+Ensuring input data are sufficient and correctly formatted is non-trivial and important, but is left to the user. Data sources vary enormously so it is difficult to generalise this part of the analysis - in many cases these will be a mix of flight logs, GPS, one or more anemometers, one or more gas sensors, a thermometer, hygrometer, barometer.
+
+Synchronisation and fusion of these data sources is important and should be given great attention - there are several ways to do this, including GPS logging on each sensor, recording everything on a single device, or NTP server synchronisation. Care should also be taken to avoid loss of data through resampling or interpolation.
+
+One way to convert encoded DJI logs is to use `djiparsetext`, a C++ library available on github [here](https://github.com/uav4geo/djiparsetxt) and documented [here](http://djilogs.live555.com/).
+
+## Development
+
+### Installation
+
+To install, clone the repository using e.g. `git clone` and use  `pip install -e .` to install it as an editable python package.
+
+It's highly recommended to use a virtual environment to manage dependencies. If you're not currently using one, [virtualenvwrapper](https://virtualenvwrapper.readthedocs.io/en/latest/install.html) is a good option.
+
+Then run `pip install -r requirements.txt` and `pip install -r dev-requirements.txt` to install the required dependencies.
+
+User requirements.txt is generated using `pigar generate` rather than `pip freeze`.
diff --git a/build_gasflux_exe.bat b/build_gasflux_exe.bat
new file mode 100644
index 0000000..d04a53c
--- /dev/null
+++ b/build_gasflux_exe.bat
@@ -0,0 +1,48 @@
+@echo off
+echo ========================================
+echo GasFlux RunExample EXE打包工具 (Windows)
+echo ========================================
+echo.
+
+cd /d "%~dp0"
+
+echo [1/4] 检查Python环境...
+python --version
+if errorlevel 1 (
+    echo 错误：未找到Python
+    pause
+    exit /b 1
+)
+
+echo [2/4] 升级pip...
+python -m pip install --upgrade pip
+
+echo [3/4] 安装PyInstaller...
+python -m pip install pyinstaller
+if errorlevel 1 (
+    echo 错误：PyInstaller安装失败
+    pause
+    exit /b 1
+)
+
+echo [4/4] 开始打包...
+python -m PyInstaller --clean gasflux_run_example.spec
+if errorlevel 1 (
+    echo 错误：打包失败
+    pause
+    exit /b 1
+)
+
+echo.
+echo ========================================
+echo 打包完成！
+echo ========================================
+echo.
+echo 可执行文件位置：dist\GasFlux_RunExample.exe
+echo.
+echo 使用方法：
+echo   GasFlux_RunExample.exe input.xlsx
+echo   GasFlux_RunExample.exe input.xlsx --output result.csv
+echo   GasFlux_RunExample.exe input.xlsx --no-gasflux
+echo.
+pause
diff --git a/build_gasflux_exe.sh b/build_gasflux_exe.sh
new file mode 100644
index 0000000..ce4472f
--- /dev/null
+++ b/build_gasflux_exe.sh
@@ -0,0 +1,48 @@
+#!/bin/bash
+
+echo "========================================"
+echo "GasFlux RunExample EXE打包工具 (Linux/Mac)"
+echo "========================================"
+echo
+
+cd "$(dirname "$0")"
+
+echo "[1/4] 检查Python环境..."
+if ! command -v python3 &> /dev/null; then
+    echo "错误：未找到Python3"
+    exit 1
+fi
+
+python3 --version
+
+echo "[2/4] 升级pip..."
+python3 -m pip install --upgrade pip
+
+echo "[3/4] 安装PyInstaller..."
+python3 -m pip install pyinstaller
+
+if [ $? -ne 0 ]; then
+    echo "错误：PyInstaller安装失败"
+    exit 1
+fi
+
+echo "[4/4] 开始打包..."
+python3 -m PyInstaller --clean gasflux_run_example.spec
+
+if [ $? -ne 0 ]; then
+    echo "错误：打包失败"
+    exit 1
+fi
+
+echo
+echo "========================================"
+echo "打包完成！"
+echo "========================================"
+echo
+echo "可执行文件位置：dist/GasFlux_RunExample"
+echo
+echo "使用方法："
+echo "  ./GasFlux_RunExample input.xlsx"
+echo "  ./GasFlux_RunExample input.xlsx --output result.csv"
+echo "  ./GasFlux_RunExample input.xlsx --no-gasflux"
+echo
diff --git a/examples/basic_usage/advanced_config.yaml b/examples/basic_usage/advanced_config.yaml
new file mode 100644
index 0000000..5ee39da
--- /dev/null
+++ b/examples/basic_usage/advanced_config.yaml
@@ -0,0 +1,52 @@
+output_dir: ./advanced_output
+
+required_cols:
+  latitude: [-90, 90]
+  longitude: [-180, 180]
+  height_ato: [-100, 500]
+  windspeed: [1, 15]
+  winddir: [0, 360]
+  temperature: [0, 30]
+  pressure: [980, 1030]
+
+gases:
+  ch4: [1.8, 5.0]
+  co2: [400, 500]
+
+
+strategies:
+  background: "algorithm"
+  sensor: "insitu"
+  spatial: "curtain"
+  interpolation: "kriging"
+
+algorithmic_baseline_settings:
+  algorithm: dietrich
+  dietrich:
+    poly_order: 4
+    smooth_half_window: 4
+
+semivariogram_settings:
+  model: gaussian
+  estimator: cressie
+  n_lags: 15
+  bin_func: even
+  fit_method: lm
+  maxlag: 80
+  tolerance: 15
+  azimuth: 0
+  bandwidth: 25
+
+ordinary_kriging_settings:
+  min_points: 5
+  max_points: 80
+  grid_resolution: 200
+  min_nodes: 10
+  y_min: 20
+  cut_ground: True
+
+filters:
+  course_filter:
+    azimuth_filter: 15
+    azimuth_window: 8
+    elevation_filter: 8
diff --git a/examples/basic_usage/data/08_34_01_间隔高度5m.xlsx b/examples/basic_usage/data/08_34_01_间隔高度5m.xlsx
new file mode 100644
index 0000000..693ae7d
Binary files /dev/null and b/examples/basic_usage/data/08_34_01_间隔高度5m.xlsx differ
diff --git a/examples/basic_usage/data/08_51_15_间隔高度10m.xlsx b/examples/basic_usage/data/08_51_15_间隔高度10m.xlsx
new file mode 100644
index 0000000..b71b273
Binary files /dev/null and b/examples/basic_usage/data/08_51_15_间隔高度10m.xlsx differ
diff --git a/examples/basic_usage/data/gasflux_config.yaml b/examples/basic_usage/data/gasflux_config.yaml
new file mode 100644
index 0000000..9f435a5
--- /dev/null
+++ b/examples/basic_usage/data/gasflux_config.yaml
@@ -0,0 +1,69 @@
+# GasFlux configuration file for basic usage example
+
+output_dir: ./10m
+
+# Required columns and their maximum valid ranges
+required_cols:
+  latitude: [-90, 90]
+  longitude: [-180, 180]
+  height_ato: [0, 50] # meters above takeoff
+  windspeed: [0, 50] # m/s
+  winddir: [0, 360] # degrees
+  temperature: [-50, 60] # degrees Celsius
+  pressure: [900, 1100] # hPa/mb
+
+# Optional gas columns and their maximum ppm concentration ranges.
+# Relative concentrations are used, so absolute offset can be incorrect as long as gain and linearity are correct.
+gases:
+  ch4: [2, 2.5]
+
+
+strategies:
+  background: "algorithm"    # Currently only algorithmic baseline correction (via pybaselines) is supported
+  sensor: "insitu"          # Currently only in-situ sensor data is supported
+  spatial: "spiral"         # Spatial processing mode: "curtain" and "spiral" are supported
+  interpolation: "kriging"  # Currently only kriging interpolation is supported
+
+# Baseline correction algorithm settings 
+algorithmic_baseline_settings:
+  algorithm: fastchrom
+  fastchrom: {
+    "half_window": 6, 
+    "threshold": "custom", #
+    "min_fwhm": ~,
+    "interp_half_window": 3, 
+    "smooth_half_window": 3, 
+    "weights": ~, 
+    "max_iter": 100, 
+    "min_length": 2}
+  fabc : {
+    "lam": 10000,  # The smoothing parameter. Larger values will create smoother baselines. Default is 1e6.
+    "scale": 10,  # The scale at which to calculate the continuous wavelet transform. Should be approximately equal to the index-based full-width-at-half-maximum of the peaks or features in the data.  Default is None, which will use half of the value from optimize_window(), which is not always a good value, but at least scales with the number of data points and gives a starting point for tuning the parameter.
+    "diff_order": 2}  # The order of the differential matrix. Must be greater than 0. Default is 2 (second order differential matrix). Typical values are 2 or 1.
+  dietrich : {
+    "poly_order": 5,
+    "smooth_half_window": 5,}
+  golotvin : {
+    "half_window": 2, 
+    "sections": 10}
+
+# Kriging settings - aggressively optimized for Spiral mode
+semivariogram_settings:
+  model: exponential       # Changed to exponential model for better circular data fitting
+  estimator: cressie        # Robust estimator for variogram calculation
+  n_lags: 50               # Further increased to 50 for better variogram resolution
+  bin_func: even           # Even binning function
+  fit_method: lm           # Least squares fitting method
+  ### Aggressively increased search ranges for circular/spiral data distribution
+  maxlag: 5000             # Dramatically increased to 5000m for comprehensive coverage
+  tolerance: 180           # Increased to 180° to allow full circular search
+  azimuth: 0               # Horizontal direction maintained
+  bandwidth: 300           # Further increased to 300m for maximum search bandwidth
+   # fit_sigma: linear # this should allow for a spatial uncertainty but currently producing bugs
+ordinary_kriging_settings:
+  min_points: 1            # Reduced to 1 to allow interpolation even with sparse data
+  max_points: 20           # Further reduced to 20 to minimize computational load
+  grid_resolution: 100     # Increased density to 100 for finer interpolation grid
+  min_nodes: 20            # Increased to 20 to ensure sufficient grid nodes
+  y_min: ~                 # Automatically determine minimum y value
+  cut_ground: False        # Keep ground cutting disabled
diff --git a/examples/basic_usage/data/testdata.csv b/examples/basic_usage/data/testdata.csv
new file mode 100644
index 0000000..3b15806
--- /dev/null
+++ b/examples/basic_usage/data/testdata.csv
@@ -0,0 +1,1001 @@
+timestamp,latitude,longitude,x,height_ato,windspeed,temperature,pressure,ch4,co2,c2h6,norm_x,norm_altitude_ato,index_x,index_y,winddir,geometry,utm_easting,utm_northing,hor_distance,vert_distance,course_azimuth,course_elevation
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diff --git a/examples/basic_usage/gasflux_config.yaml b/examples/basic_usage/gasflux_config.yaml
new file mode 100644
index 0000000..acd02f9
--- /dev/null
+++ b/examples/basic_usage/gasflux_config.yaml
@@ -0,0 +1,61 @@
+# GasFlux 基本使用示例配置文件
+# 这个配置文件演示了最基本的设置，用于处理简单的气体通量数据
+
+# 输出目录
+output_dir: ./output
+
+# 数据验证参数
+required_cols:
+  latitude: [-90, 90]        # 纬度范围
+  longitude: [-180, 180]     # 经度范围
+  height_ato: [0, 200]       # 相对起飞高度（米）
+  windspeed: [0, 20]         # 风速（m/s）
+  winddir: [0, 360]         # 风向（度）
+  temperature: [-50, 60]    # 温度（°C）
+  pressure: [900, 1100]     # 气压（hPa）
+
+# 气体配置：气体名称和浓度范围（ppmv）
+gases:
+  ch4: [1.5, 10.0]    # 甲烷
+  co2: [300, 500]    # 二氧化碳
+
+# 处理策略
+strategies:
+  background: "algorithm"    # 背景校正方法
+  sensor: "insitu"          # 传感器类型
+  spatial: "curtain"        # 空间处理模式
+  interpolation: "kriging"  # 插值方法
+
+# 背景校正算法设置
+algorithmic_baseline_settings:
+  algorithm: fastchrom      # 使用FastChrom算法
+  fastchrom:
+    half_window: 3          # 半窗口大小
+    threshold: "custom"     # 阈值方法
+    min_fwhm: ~
+    interp_half_window: 2
+    smooth_half_window: 2
+    weights: ~
+    max_iter: 50
+    min_length: 2
+
+# 克里金插值设置
+semivariogram_settings:
+  model: spherical          # 半变异函数模型
+  estimator: cressie        # 估计器
+  n_lags: 10               # 滞后期数
+  bin_func: even           # 分箱函数
+  fit_method: lm           # 拟合方法
+  maxlag: 50               # 最大滞后距离
+  tolerance: 10            # 方向容差
+  azimuth: 0               # 方位角
+  bandwidth: 15            # 带宽
+
+# 普通克里金设置
+ordinary_kriging_settings:
+  min_points: 3             # 最小邻点数
+  max_points: 50            # 最大邻点数
+  grid_resolution: 100      # 网格分辨率
+  min_nodes: 5              # 最小网格节点数
+  y_min: ~                  # 最小y值
+  cut_ground: False         # 是否切割地面
diff --git a/examples/basic_usage/result.csv b/examples/basic_usage/result.csv
new file mode 100644
index 0000000..1b85a86
--- /dev/null
+++ b/examples/basic_usage/result.csv
@@ -0,0 +1,820 @@
+timestamp,latitude,longitude,height_ato,windspeed,winddir,temperature,pressure,ch4,course_elevation,course_azimuth
+2016-02-12 08:34:01,40.349137,115.7855289,11.865000000000009,4.85383,126.121,18.1,957.6,2.28753,8.50882,74.9353
+2016-02-12 08:34:02,40.349137,115.7855289,11.865000000000009,3.5045,142.744,18.1,957.6,2.27667,8.50882,74.9353
+2016-02-12 08:34:02,40.349137,115.7855296,9.830000000000041,3.30667,145.626,18.1,957.4,2.28621,7.26866,75.2618
+2016-02-12 08:34:02,40.3491369,115.7855295,9.830000000000041,3.19669,147.2,18.1,957.4,2.29012,6.6954,75.3488
+2016-02-12 08:34:02,40.3491366,115.7855289,9.830000000000041,3.1958,146.33,18.1,957.4,2.28968,5.15029,75.4473
+2016-02-12 08:34:02,40.3491365,115.7855279,9.830000000000041,2.24203,142.349,18.1,957.4,2.27675,3.60147,75.8082
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+2016-02-12 08:47:04,40.3491715,115.784814,18.772000000000048,7.50092,106.624,16.2,957.4,2.25672,-5.29089,-78.658
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+2016-02-12 08:47:35,40.3491704,115.7844869,5.802000000000021,2.77827,78.7524,16.2,957.6,2.24481,0.782934,49.3694
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diff --git a/examples/basic_usage/testconfig.yaml b/examples/basic_usage/testconfig.yaml
new file mode 100644
index 0000000..ee23d5d
--- /dev/null
+++ b/examples/basic_usage/testconfig.yaml
@@ -0,0 +1,95 @@
+# testconfig.yaml
+
+horizontal_pixels: 500 # Width of the concentration map in pixels
+vertical_pixels: 100 # Height of the concentration map in pixels
+num_plumes: 10 # Number of Gaussian plumes
+groupiness: 0.5 # Groupiness of the plumes (0.0 to 1.0)
+spread: 0.1 # Spread of the plumes (0.0 to 1.0)
+wind_reference_height: 10 # Reference height for wind speed calculation (m)
+windspeed_avg: 5 # Average wind speed at 10m height (m/s)
+windspeed_rel_std: 0.2 # Relative standard deviation of wind speed (0.0 to inf), recommend 0.2-0.4
+surface_roughness: 0.1 # Surface roughness length (m)
+seed: 42 # Random seed for reproducibility
+simplex_octaves: 4 # Number of octaves for simplex noise (1 to inf, def 1)
+simplex_persistence: 0.7 # Persistence of simplex noise (0.0 to 1.0, def 0.5) - specifies the amplitude of each octave relative to the one below it
+simplex_lacunarity: 2.0 # Lacunarity of simplex noise (1.0 to inf, def 2.0) - specifies the frequency of each octave relative to the one below it
+winddir_avg: 0.0 # Average wind direction in degrees rel to plane (0 is CW)
+winddir_std: 10 # Standard deviation of wind direction in degrees
+timestamp: "2022-09-26 02:03:00"
+flight_time_seconds: 1000
+sample_frequency: 1
+start_coords:
+  - 54.87667
+  - 15.41
+transect_azimuth: 260 # the wind will start off 90 degrees CW to this azimuth, and is modified relative to that by the winddir_avg. 260 is a good value to test N problems
+sampling_altitude_ato_range:
+  - -10 # negative values should be fine
+  - 100
+sampling_horizontal_range:
+  - 50
+  - 950
+scene_altitude_range:
+  - -20
+  - 120
+scene_horizontal_range:
+  - 0
+  - 1000
+number_of_transects: 10
+gases:
+  ch4:
+    - 1.95
+    - 10.0
+  co2:
+    - 380.0
+    - 500.0
+  c2h6:
+    - 0.0
+    - 1.0
+temperature: 10.0
+pressure: 1000.0
+
+output_dir: ./gasflux_reports
+
+algorithmic_baseline_settings:
+  algorithm: fastchrom
+
+semivariogram_settings:
+  model: spherical
+  estimator: cressie
+  n_lags: 20
+  bin_func: even
+  fit_method: lm
+  maxlag: 100
+  #fit_sigma: linear
+  tolerance: 10
+  azimuth: 0
+  bandwidth: 20
+
+ordinary_kriging_settings:
+  min_points: 3
+  max_points: 100
+  grid_resolution: 500
+  min_nodes: 10
+  cut_ground: False
+  y_min: ~
+
+required_cols:
+  latitude: [-90, 90]
+  longitude: [-180, 180]
+  height_ato: [-100, 500]
+  windspeed: [0, 30]
+  winddir: [0, 360]
+  temperature: [-50, 60]
+  pressure: [900, 1100]
+
+filters:
+  course_filter:
+    azimuth_filter: 10
+    azimuth_window: 5
+    elevation_filter: 5
+
+strategies:
+  background: "algorithm"
+  sensor: "insitu"
+  spatial: "curtain"
+  interpolation: "kriging"
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..a61022a
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,120 @@
+[project]
+name = "gasflux"
+dynamic = ["version", "dependencies", "optional-dependencies"]
+description = "A package to calculate gas emissions fluxes from natural and anthropogenic sources"
+authors = [{ name = "Jamie McQuilkin", email = "jamie.mcquilkin@gmail.com" }]
+license = { text = "AGPL-3.0" }
+readme = "README.md"
+requires-python = ">=3.10"
+
+[project.scripts]
+gasflux = "gasflux.cli:main_cli"
+
+[project.urls]
+"Homepage" = "https://gasflux.github.io"
+"Bug Tracker" = "https://github.com/gasflux/gasflux/issues"
+
+[build-system]
+requires = ["setuptools>=69.0", "wheel", "setuptools-scm>=8.0"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools.dynamic]
+version = {attr = "gasflux.__version__"}
+dependencies = { file = ["requirements.txt"] }
+optional-dependencies = { dev = { file = ["dev-requirements.txt"] } }
+
+[tool.mypy]
+ignore_missing_imports = true
+check_untyped_defs = true
+
+[tool.ruff]
+line-length = 120
+target-version = "py311"
+
+[tool.ruff.lint]
+ignore = [
+    "G004",   # G004: Logging statement uses f-string
+    "PD901",  # PD901: Using "df" as a variable name is not recommended
+    "ISC001", # ISC001: Use of implicit string concatenation
+]
+
+
+select = [
+    "A",   # prevent using keywords that clobber python builtins
+    "B",   # bugbear: security warnings
+    "E",   # pycodestyle
+    "F",   # pyflakes
+    "ISC", # implicit string concatenation
+    "UP",  # alert you when better syntax is available in your python version
+    "RUF", # the ruff developer's own rules
+]
+
+[tool.ruff.lint.pydocstyle]
+convention = "google"
+
+[tool.semantic_release]
+assets = []
+commit_message = "{version}\n\nAutomatically generated by python-semantic-release"
+commit_parser = "angular"
+logging_use_named_masks = false
+major_on_zero = true
+allow_zero_version = true
+tag_format = "v{version}"
+version_variables = ["src/gasflux/__init__.py:__version__"]
+
+[tool.semantic_release.branches.main]
+match = "(main|master)"
+prerelease_token = "rc"
+prerelease = false
+
+[tool.semantic_release.changelog]
+# template_dir = "templates"
+changelog_file = "CHANGELOG.md"
+exclude_commit_patterns = ["ci", "test", "chore", "docs", "style", "refactor"]
+
+[tool.semantic_release.changelog.environment]
+block_start_string = "{%"
+block_end_string = "%}"
+variable_start_string = "{{"
+variable_end_string = "}}"
+comment_start_string = "{#"
+comment_end_string = "#}"
+trim_blocks = false
+lstrip_blocks = false
+newline_sequence = "\n"
+keep_trailing_newline = false
+extensions = []
+autoescape = true
+
+[tool.semantic_release.commit_author]
+env = "GIT_COMMIT_AUTHOR"
+default = "semantic-release <semantic-release>"
+
+[tool.semantic_release.commit_parser_options]
+allowed_tags = [
+    "build",
+    "chore",
+    "ci",
+    "docs",
+    "feat",
+    "fix",
+    "perf",
+    "style",
+    "refactor",
+    "test",
+]
+minor_tags = ["feat"]
+patch_tags = ["fix", "perf"]
+default_bump_level = 0
+
+[tool.semantic_release.remote]
+name = "origin"
+type = "github"
+ignore_token_for_push = false
+
+[tool.semantic_release.remote.token]
+env = "GH_TOKEN"
+
+[tool.semantic_release.publish]
+dist_glob_patterns = ["dist/*"]
+upload_to_vcs_release = true
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..fd52cb4
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,20 @@
+# Automatically generated by https://github.com/damnever/pigar.
+
+colorama==0.4.6
+geopandas==0.14.3
+geopy==2.4.1
+Jinja2==3.1.6
+joblib==1.3.2
+matplotlib==3.10.0
+molmass==2023.8.30
+noise==1.2.2
+numpy==2.1.3
+pandas==2.2.3
+plotly==5.20.0
+pybaselines==1.1.0
+pytest==8.1.1
+PyYAML==6.0.1
+scikit-gstat==1.0.19
+scikit-image==0.24.0
+scipy==1.15.1
+simplekml==1.3.6
diff --git a/src/__init__.py b/src/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/gasflux/__init__.py b/src/gasflux/__init__.py
new file mode 100644
index 0000000..da1d308
--- /dev/null
+++ b/src/gasflux/__init__.py
@@ -0,0 +1,29 @@
+"""Init file for gasflux package."""
+
+__version__ = "0.2.1"  # managed by semantic versioning
+
+from . import (
+    background,
+    cli,
+    gas,
+    interpolation,
+    ml,
+    plotting,
+    pre_processing,
+    processing,
+    processing_pipelines,
+    reporting,
+)
+
+__all__ = [
+    "background",
+    "cli",
+    "gas",
+    "interpolation",
+    "ml",
+    "plotting",
+    "pre_processing",
+    "processing",
+    "processing_pipelines",
+    "reporting",
+]
diff --git a/src/gasflux/background.py b/src/gasflux/background.py
new file mode 100644
index 0000000..519352a
--- /dev/null
+++ b/src/gasflux/background.py
@@ -0,0 +1,64 @@
+"""Baselining functions."""
+
+import numpy as np
+import pandas as pd
+import pybaselines as pybs
+from . import plotting
+
+# 自定义阈值函数，避免依赖scikit-image
+def custom_threshold(data):
+    """
+    简单的三角阈值算法实现，避免依赖scikit-image
+    使用直方图的三角法来确定阈值
+    """
+    if len(data) == 0:
+        return 0
+
+    # 计算直方图
+    hist, bin_edges = np.histogram(data, bins=256)
+    bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
+
+    # 找到最大峰值
+    max_idx = np.argmax(hist)
+
+    # 如果最大峰在边缘，使用中位数作为阈值
+    if max_idx == 0 or max_idx == len(hist) - 1:
+        return np.median(data)
+
+    # 使用简单的阈值策略：最大峰右侧的第一个局部最小值
+    # 这里简化为使用均值作为阈值
+    return np.mean(data)
+
+
+def algorithmic_baseline(
+    df: pd.DataFrame,
+    gas: str,
+    algorithmic_baseline_settings: dict,
+):
+    df = df.copy()
+    algorithm = algorithmic_baseline_settings["algorithm"]
+    settings = algorithmic_baseline_settings.get(algorithm, {}).copy()
+    if settings.get("threshold") == "custom":
+        settings["threshold"] = custom_threshold
+    if len(df) < 20:
+        raise ValueError("Dataframe must contain at least 20 rows for background correction.")
+    index = np.arange(len(df))
+    baseline_fitter = pybs.Baseline(index, check_finite=False)
+    fit = getattr(baseline_fitter, algorithm)
+    bkg, params = fit(df[gas], **settings)
+    bkg_points = params["mask"]
+    df[f"{gas}_normalised"] = df[gas] - bkg
+    df[f"{gas}_fit"] = bkg
+    background = (df[gas] - bkg)[bkg_points]
+    signal = (df[gas] - bkg)[~bkg_points]
+    df[f"{gas}_signal"] = np.invert(bkg_points)
+    fig = plotting.background_plotting(df, gas)
+    output_text = (
+        f"Baseline algorithm: {algorithm}\n"
+        f"Positive and negative 95% percentile of baseline: {np.percentile(background, 2.5):.2f} ppm, \
+            {np.percentile(background, 97.5):.2f} ppm\n"
+        f"Mean of baseline: {np.mean(background):.2f} ppm\n"
+        f"Minimum and maximum of baseline: {np.min(background):.2f} ppm, {np.max(background):.2f} ppm\n"
+        f"Signal points: {len(signal)}; background points: {len(background)}\n"
+    )
+    return df, fig, output_text
diff --git a/src/gasflux/cli.py b/src/gasflux/cli.py
new file mode 100644
index 0000000..f82fc7c
--- /dev/null
+++ b/src/gasflux/cli.py
@@ -0,0 +1,279 @@
+import argparse
+from pathlib import Path
+import shutil
+
+from colorama import init, Fore, Style
+from src.gasflux.processing_pipelines import process_main
+
+init()
+
+
+def find_config_file(path: Path, recursive: bool = True):
+    if recursive:
+        config_files = [file for file in path.rglob("*.yaml") if "gasflux_config" in file.read_text()]
+    else:
+        config_files = [file for file in path.glob("*.yaml") if "gasflux_config" in file.read_text()]
+    if len(config_files) == 1:
+        return config_files[0]
+    elif len(config_files) > 1:
+        raise ValueError(
+            "Multiple candidate config files found: {}".format(", ".join(str(file) for file in config_files))
+        )
+    else:
+        raise FileNotFoundError("No config file found in the supplied path or its child folders")
+
+
+def process_command(data_path: str, config_path: str, test: bool):
+    if test:
+        data_file = Path(__file__).parent / "testdata" / "testdata.csv"
+        config_file = Path(__file__).parent / "testdata" / "testconfig.yaml"
+        process_main(data_file, config_file)
+        return
+
+    dpath_obj = Path(data_path)
+
+    if dpath_obj.is_dir():
+        data_files = list(dpath_obj.rglob("*.csv"))
+        if not data_files:
+            raise FileNotFoundError(f"No CSV files found in directory: {data_path}")
+    elif dpath_obj.is_file():
+        data_files = [dpath_obj]
+    else:
+        raise FileNotFoundError(f"Invalid data path: {data_path}")
+
+    if config_path is None:
+        try:
+            config_file = find_config_file(dpath_obj if dpath_obj.is_dir() else dpath_obj.parent)
+        except FileNotFoundError:
+            response = (
+                input(
+                    "No configuration file found. Generate config file? \n"
+                    "You may have to modify it before processing. [y/n]: "
+                )
+                .strip()
+                .lower()
+            )
+
+            if response == "y":
+                config_file = (dpath_obj if dpath_obj.is_dir() else dpath_obj.parent) / "gasflux_config.yaml"
+                shutil.copy(Path(__file__).parent / "gasflux_config.yaml", config_file)
+                print(f"Config file copied to: {config_file}")
+            else:
+                print("No config file generated. Exiting.")
+                return
+    else:
+        config_file = Path(config_path)
+
+    for data_file in data_files:
+        process_main(data_file, config_file)
+
+
+def generate_config_command(config_destination: str, recursive: bool = False, template_path: str | None = None):
+    if template_path:
+        template = Path(template_path)
+        print(f"Using custom template: {template}")
+    else:
+        template = Path(__file__).parent / "gasflux_config.yaml"
+
+    if not template.exists():
+        raise FileNotFoundError(f"Template file not found: {template}")
+
+    destination_path = Path(config_destination)
+
+    if not destination_path.is_dir():
+        raise NotADirectoryError(f"Destination path is not a directory: {config_destination}")
+
+    overwrite_all = False  # Flag to determine if we should overwrite all files without prompting
+
+    if recursive:
+        for subdir in destination_path.rglob("*"):
+            if subdir.is_dir():
+                config_file = subdir / "gasflux_config.yaml"
+                if config_file.exists() and not overwrite_all:
+                    response = (
+                        input(f"Config file already exists at {config_file}. Overwrite? [y/n/a/c]: ").strip().lower()
+                    )
+                    if response == "a":
+                        overwrite_all = True
+                    elif response == "c":
+                        print("Operation canceled.")
+                        return
+                    elif response == "n":
+                        print(f"Skipping {config_file}")
+                        continue
+                shutil.copy(template, config_file)
+                print(f"Config file copied to: {config_file}")
+    else:
+        config_file = destination_path / "gasflux_config.yaml"
+        if config_file.exists() and not overwrite_all:
+            response = input(f"Config file already exists at {config_file}. Overwrite? [y/n/a/c]: ").strip().lower()
+            if response == "a":
+                overwrite_all = True
+            elif response == "c":
+                print("Operation canceled.")
+                return
+            elif response == "n":
+                print(f"Skipping {config_file}")
+                return
+        shutil.copy(template, config_file)
+        print(f"Config file copied to: {config_file}")
+
+
+def main_cli():
+    parser = argparse.ArgumentParser(
+        description="GasFlux Processing Pipeline",
+        formatter_class=argparse.RawTextHelpFormatter,
+        add_help=False,  # Disable the default help to use the custom help screen
+    )
+    subparsers = parser.add_subparsers(dest="command")
+
+    process_parser = subparsers.add_parser(
+        "process",
+        help="Process CSV data files",
+        description=(
+            "Process CSV data files located in a specified directory or a single file.\n"
+            "The program will search for a configuration file (gasflux_config.yaml) in the same directory as the data file(s).\n"  # noqa
+        ),
+        formatter_class=argparse.RawTextHelpFormatter,
+    )
+    process_parser.add_argument(
+        "data_path",
+        nargs="?",
+        default=None,
+        help=(
+            "Path to the data file or directory containing CSV files.\n"
+            "If a directory is provided, all CSV files within the directory will be processed."
+        ),
+    )
+    process_parser.add_argument(
+        "--config-path",
+        "-c",
+        default=None,
+        help=(
+            "Path to the configuration file (gasflux_config.yaml).\n"
+            "If not specified, the program will search for the configuration file in the same directory as the data file(s).\n"  # noqa
+            "If no configuration file is found, the default configuration will be used."
+        ),
+    )
+    process_parser.add_argument(
+        "--test",
+        action="store_true",
+        help="Use test data instead of the specified data file(s).",
+    )
+
+    config_parser = subparsers.add_parser(
+        "generate-config",
+        help="Generate a configuration file",
+        description=(
+            "Generate a configuration file (gasflux_config.yaml) in the specified directory.\n"
+            "If no directory is specified, the configuration file will be generated in the current directory."
+            "Use flag --recursive to generate config files in all subdirectories recursively. YOu might want to"
+            "do this if you want to have a separate config for each data file"
+        ),
+        formatter_class=argparse.RawTextHelpFormatter,
+    )
+    config_parser.add_argument(
+        "--recursive",
+        action="store_true",
+        help="Generate config files in all subdirectories recursively.",
+    )
+
+    config_parser.add_argument(
+        "--template",
+        default=None,
+        help="Path to a custom template configuration file.",
+    )
+
+    config_parser.add_argument(
+        "config_destination",
+        nargs="?",
+        default=None,
+        help="Destination directory for the generated configuration file.",
+    )
+
+    # Custom help option
+    parser.add_argument(
+        "-h",
+        "--help",
+        action="store_true",
+        help="Show this help message and exit",
+    )
+
+    args = parser.parse_args()
+
+    if args.command == "process":
+        process_command(args.data_path, args.config_path, args.test)
+    elif args.command == "generate-config":
+        generate_config_command(args.config_destination, args.recursive, args.template)
+    elif args.help:
+        display_help()
+    else:
+        display_help()
+
+
+def display_help():
+    help_text = f"""
+{Fore.CYAN}GasFlux Processing Pipeline{Style.RESET_ALL}
+{Fore.CYAN}==========================={Style.RESET_ALL}
+
+{Fore.YELLOW}Description:{Style.RESET_ALL}
+The GasFlux Processing Pipeline is a command-line tool for processing CSV data files and generating configuration files.
+
+{Fore.YELLOW}Usage:{Style.RESET_ALL}
+  gasflux [OPTIONS] COMMAND [ARGS]...
+
+{Fore.YELLOW}Commands:{Style.RESET_ALL}
+  {Fore.GREEN}process{Style.RESET_ALL}         Process CSV data files
+  {Fore.GREEN}generate-config{Style.RESET_ALL} Generate a default configuration file
+
+{Fore.YELLOW}Options:{Style.RESET_ALL}
+  {Fore.BLUE}-h, --help{Style.RESET_ALL}      Show this help message and exit
+  {Fore.BLUE}-v, --version{Style.RESET_ALL}   Show the version and exit
+
+{Fore.YELLOW}Commands Help:{Style.RESET_ALL}
+  {Fore.GREEN}process{Style.RESET_ALL}         Process CSV data files located in a specified directory or a single file.
+                  The program will search for a singular configuration file (gasflux_config.yaml) in the same directory
+                  - if a directory is supplied to [DATA_PATH] then child directories are searched too.
+
+                  {Fore.YELLOW}Usage:{Style.RESET_ALL} gasflux process [OPTIONS] [DATA_PATH]
+
+                  {Fore.YELLOW}Options:{Style.RESET_ALL}
+                    {Fore.BLUE}-c, --config-path PATH{Style.RESET_ALL}  Path to the configuration file (gasflux_config.yaml).
+                                            If not specified, the program will search for the configuration file
+                                            in the same directory as the data file(s).
+                                            If no configuration file is found, the default configuration will be used.
+                    {Fore.BLUE}--test{Style.RESET_ALL}                  Use test data instead of the specified data file(s).
+                    {Fore.BLUE}-h, --help{Style.RESET_ALL}              Show this help message and exit
+
+  {Fore.GREEN}generate-config{Style.RESET_ALL} Generate a default configuration file (gasflux_config.yaml) in the specified directory.
+                  If no directory is specified, the configuration file will be generated in the current directory.
+
+                  {Fore.YELLOW}Usage:{Style.RESET_ALL} gasflux generate-config [OPTIONS] [CONFIG_DESTINATION]
+
+                  {Fore.YELLOW}Options:{Style.RESET_ALL}
+                    {Fore.BLUE}-h, --help{Style.RESET_ALL}  Show this help message and exit
+
+{Fore.YELLOW}Examples:{Style.RESET_ALL}
+  Process a single CSV file:
+    $ gasflux process {Fore.MAGENTA}"/path/to/data.csv"{Style.RESET_ALL}
+
+  Process all CSV files in a directory:
+    $ gasflux process {Fore.MAGENTA}"/path/to/data/directory"{Style.RESET_ALL}
+
+  Process a single CSV file with a specific configuration file:
+    $ gasflux process {Fore.MAGENTA}"/path/to/data.csv"{Style.RESET_ALL} --config-path {Fore.MAGENTA}/path/to/config.yaml{Style.RESET_ALL}
+
+  Process test data:
+    $ gasflux process {Fore.BLUE}--test{Style.RESET_ALL}
+
+  Generate a default configuration file in the current directory:
+    $ gasflux generate-config {Fore.MAGENTA}.{Style.RESET_ALL}
+
+  Generate a default configuration file in a specific directory recursively using a custom template:
+    $ gasflux generate-config {Fore.MAGENTA}"/path/to/directory"{Style.RESET_ALL} {Fore.BLUE}--recursive{Style.RESET_ALL} {Fore.BLUE}--template{Style.RESET_ALL} {Fore.MAGENTA}"/path/to/template.yaml"{Style.RESET_ALL}
+"""  # noqa
+    print(help_text)
+
+
+if __name__ == "__main__":
+    main_cli()
diff --git a/src/gasflux/data_processor.py b/src/gasflux/data_processor.py
new file mode 100644
index 0000000..15e8c63
--- /dev/null
+++ b/src/gasflux/data_processor.py
@@ -0,0 +1,934 @@
+#!/usr/bin/env python3
+"""
+无人机数据处理脚本
+
+处理Excel格式的无人机测量数据，转换为GasFlux标准输入格式。
+
+支持两种使用方式：
+1. 命令行调用：python data_processor.py input.xlsx
+2. 直接调用：from data_processor import process_file; df = process_file('input.xlsx')
+
+处理步骤：
+1. 读取Excel文件
+2. 删除不需要的列
+3. 根据文件名修正时间格式
+4. 坐标转换（经纬度除以10^7）
+5. 计算气压（使用qiya.py）
+6. 高度调整（减去最小高度）
+7. 时间戳融合
+8. 字段重命名
+
+作者：GasFlux开发团队
+"""
+
+import pandas as pd
+import numpy as np
+from pathlib import Path
+import re
+from datetime import datetime
+import sys
+import os
+from collections import Counter
+
+try:
+    from tqdm import tqdm
+    HAS_TQDM = True
+except ImportError:
+    HAS_TQDM = False
+    print("⚠️  未安装tqdm库，将不显示进度条。如需进度条，请运行: pip install tqdm")
+
+
+def create_height_bins(heights, bin_size=2.0):
+    """
+    将高度数据按指定间隔分档
+
+    Args:
+        heights: 高度数据Series
+        bin_size: 每个档位的间隔（米）
+
+    Returns:
+        list: [(bin_min, bin_max, bin_center, count), ...] 每个档位的信息
+    """
+    if len(heights) == 0:
+        return []
+
+    height_min = heights.min()
+    height_max = heights.max()
+
+    # 计算需要的档位数量
+    range_size = height_max - height_min
+    if range_size == 0:
+        # 所有高度相同
+        return [(height_min, height_max, height_min, len(heights))]
+
+    num_bins = max(1, int(np.ceil(range_size / bin_size)))
+
+    bins = []
+    for i in range(num_bins):
+        bin_min = height_min + i * bin_size
+        bin_max = min(height_min + (i + 1) * bin_size, height_max)
+        bin_center = (bin_min + bin_max) / 2
+
+        # 统计这个档位有多少数据点
+        count = ((heights >= bin_min) & (heights < bin_max)).sum()
+        if i == num_bins - 1:  # 最后一个档位包含上限
+            count = ((heights >= bin_min) & (heights <= bin_max)).sum()
+
+        if count > 0:  # 只保留有数据的档位
+            bins.append((bin_min, bin_max, bin_center, count))
+
+    return bins
+
+# 导入qiya模块
+try:
+    from .qiya import get_pressure_at_location
+    print("✅ 成功导入qiya模块")
+except ImportError as e:
+    print(f"❌ 导入qiya模块失败: {e}")
+    print("请确保GasFlux包结构完整")
+    sys.exit(1)
+
+
+def load_excel_data(file_path):
+    """
+    读取Excel文件并进行初步处理
+
+    Args:
+        file_path: Excel文件路径
+
+    Returns:
+        pd.DataFrame: 读取的数据
+    """
+    try:
+        print(f"正在读取文件: {file_path}")
+
+        # 读取Excel文件
+        df = pd.read_excel(file_path)
+        print(f"✅ 成功读取数据：{len(df)} 行，{len(df.columns)} 列")
+        print(f"列名：{list(df.columns)}")
+
+        return df
+
+    except Exception as e:
+        print(f"❌ 读取文件失败: {e}")
+        sys.exit(1)
+
+
+def remove_columns(df, columns_to_remove):
+    """
+    删除指定的列
+
+    Args:
+        df: 输入DataFrame
+        columns_to_remove: 要删除的列名列表
+
+    Returns:
+        pd.DataFrame: 删除列后的DataFrame
+    """
+    print(f"\n删除列: {columns_to_remove}")
+
+    # 检查要删除的列是否存在
+    existing_columns = [col for col in columns_to_remove if col in df.columns]
+    missing_columns = [col for col in columns_to_remove if col not in df.columns]
+
+    if missing_columns:
+        print(f"⚠️  以下列不存在（跳过）: {missing_columns}")
+
+    if existing_columns:
+        df = df.drop(columns=existing_columns)
+        print(f"✅ 已删除 {len(existing_columns)} 列")
+
+    return df
+
+
+def extract_hour_from_filename(filename):
+    """
+    从文件名中提取小时信息
+
+    例如: "08_34_01_间隔高度5m.xlsx" -> "08"
+
+    Args:
+        filename: 文件名
+
+    Returns:
+        str: 小时字符串（两位数）
+    """
+    # 使用正则表达式匹配小时部分
+    match = re.match(r'(\d{2})_', filename)
+    if match:
+        return match.group(1)
+    else:
+        print(f"⚠️  无法从文件名 '{filename}' 中提取小时信息，使用默认值 '00'")
+        return "00"
+
+
+def fix_time_column(df, filename):
+    """
+    根据文件名修正时间列
+
+    Args:
+        df: 输入DataFrame
+        filename: 文件名
+
+    Returns:
+        pd.DataFrame: 修正后的DataFrame
+    """
+    print("修正时间格式...")
+
+    # 提取小时信息
+    hour_prefix = extract_hour_from_filename(filename)
+    print(f"从文件名提取的小时: {hour_prefix}")
+
+    # 检查时间列是否存在
+    if '时间' not in df.columns:
+        print("❌ 未找到 '时间' 列")
+        return df
+
+    # 修正时间格式
+    def fix_single_time(time_str):
+        if pd.isna(time_str):
+            return time_str
+
+        time_str = str(time_str).strip()
+
+        # 如果时间格式类似 "0:34:01" 或 "00:34:01"
+        if re.match(r'^\d{1,2}:\d{2}:\d{2}$', time_str):
+            parts = time_str.split(':')
+            original_hour = int(parts[0])
+            filename_hour = int(hour_prefix)
+
+            # 小时相加
+            new_hour = (filename_hour + original_hour) % 24  # 防止超过24小时
+
+            # 保持分钟和秒不变
+            new_time = f"{new_hour:02d}:{parts[1]}:{parts[2]}"
+            return new_time
+        else:
+            # 其他格式，使用默认时间
+            return f"{hour_prefix}:00:00"
+
+    # 应用时间修正
+    original_times = df['时间'].head(3).tolist()
+    df['时间'] = df['时间'].apply(fix_single_time)
+    corrected_times = df['时间'].head(3).tolist()
+
+    print(f"时间修正示例:")
+    for orig, corr in zip(original_times, corrected_times):
+        print(f"  {orig} → {corr}")
+
+    return df
+
+
+def convert_coordinates(df):
+    """
+    转换经纬度坐标（除以10^7）
+
+    Args:
+        df: 输入DataFrame
+
+    Returns:
+        pd.DataFrame: 转换后的DataFrame
+    """
+    print("转换经纬度坐标...")
+
+    if '经度' in df.columns:
+        original_lon = df['经度'].head(3).tolist()
+        df['经度'] = df['经度'] / 1e7
+        converted_lon = df['经度'].head(3).tolist()
+        print("经度转换示例:")
+        for orig, conv in zip(original_lon, converted_lon):
+            print(".6f")
+
+    if '纬度' in df.columns:
+        original_lat = df['纬度'].head(3).tolist()
+        df['纬度'] = df['纬度'] / 1e7
+        converted_lat = df['纬度'].head(3).tolist()
+        print("纬度转换示例:")
+        for orig, conv in zip(original_lat, converted_lat):
+            print(".6f")
+
+    return df
+
+
+def calculate_pressure(df, max_samples=None, height_tolerance=10.0, height_bin_size=2.0):
+    """
+    计算气压数据（高度分档优化版）
+
+    Args:
+        df: 输入DataFrame
+        max_samples: 最大采样数量（None表示计算所有行）
+        height_tolerance: 高度变化容差（米），如果所有高度都在此范围内，只计算一次
+        height_bin_size: 高度分档间隔（米），每个档位使用中间高度计算气压
+
+    Returns:
+        pd.DataFrame: 添加气压列的DataFrame
+    """
+    print("计算气压数据...")
+
+    # 检查必要列是否存在
+    required_cols = ['日期', '时间', '经度', '纬度', '融合高程']
+    missing_cols = [col for col in required_cols if col not in df.columns]
+
+    if missing_cols:
+        print(f"❌ 缺少必要列: {missing_cols}")
+        return df
+
+    # 检查高度变化范围
+    height_min = df['融合高程'].min()
+    height_max = df['融合高程'].max()
+    height_range = height_max - height_min
+
+    print(f"🏔️  高度范围: {height_min:.1f} - {height_max:.1f} 米 (变化: {height_range:.1f} 米)")
+    # 创建高度分档
+    height_bins = create_height_bins(df['融合高程'], height_bin_size)
+    print(f"📏 高度分档: {len(height_bins)} 个档位 (间隔: {height_bin_size:.1f} 米)")
+
+    for i, (bin_min, bin_max, bin_center, count) in enumerate(height_bins):
+        print(f"  档位{i+1}: {bin_min:.1f}-{bin_max:.1f}m (中心: {bin_center:.1f}m, 数据: {count}行)")
+    # 决定计算策略
+    if height_range <= height_tolerance:
+        # 高度变化小，只计算一次气压
+        print("🎯 高度变化小，将使用平均高度计算一次气压")
+        use_single_calculation = True
+        mean_height = df['融合高程'].mean()
+        print(f"📍 使用平均高度: {mean_height:.1f} 米")
+    elif len(height_bins) == 1:
+        # 只有一个高度档位，使用档位中心高度
+        print("📦 只有一个高度档位，使用档位中心高度")
+        use_single_calculation = True
+        mean_height = height_bins[0][2]  # bin_center
+        print(f"📍 使用档位中心高度: {mean_height:.1f} 米")
+    else:
+        # 高度变化大，使用分档计算
+        print("🏗️  使用高度分档策略，减少API调用")
+        use_single_calculation = False
+
+    # 确定要处理的行数
+    if max_samples is None or max_samples >= len(df):
+        # 计算所有行
+        sample_df = df.copy()
+        actual_samples = len(df)
+        if not use_single_calculation:
+            print(f"📊 将计算所有 {len(df)} 行的气压数据")
+    else:
+        # 限制采样数量
+        print(f"⚠️  数据量较大 ({len(df)} 行)，只对前 {max_samples} 行计算气压")
+        sample_df = df.head(max_samples).copy()
+        actual_samples = max_samples
+
+    pressures = []
+
+    if use_single_calculation:
+        # 只计算一次气压
+        try:
+            # 使用第一行的日期和时间作为代表
+            first_row = sample_df.iloc[0]
+
+            # 转换日期格式 - 只提取日期部分，移除任何时间信息
+            date_str = str(first_row['日期'])
+            if ' ' in date_str:
+                date_str = date_str.split(' ')[0]
+            elif 'T' in date_str:
+                date_str = date_str.split('T')[0]
+
+            if '/' in date_str:
+                date_str = date_str.replace('/', '-')
+
+            date_parts = date_str.split('-')
+            if len(date_parts) == 3 and len(date_parts[0]) == 4:
+                year, month, day = date_parts
+                formatted_date = f"{year}-{month.zfill(2)}-{day.zfill(2)}"
+            else:
+                raise ValueError(f"日期格式异常: {date_str}")
+
+            # 使用数据的代表性时间（整点小时，众数）
+            time_strings = []
+            for time_val in sample_df['时间']:
+                time_str = str(time_val).strip()
+                if ':' in time_str:
+                    # 确保是有效的 HH:MM 格式，然后取整点小时
+                    parts = time_str.split(':')
+                    if len(parts) >= 2:
+                        try:
+                            hour = int(parts[0])
+                            # 确保小时在有效范围内 (0-23)
+                            if 0 <= hour <= 23:
+                                time_strings.append(f"{hour:02d}:00")
+                        except ValueError:
+                            continue
+
+            if time_strings:
+                time_counts = Counter(time_strings)
+                formatted_time = time_counts.most_common(1)[0][0]
+                occurrence_count = time_counts.most_common(1)[0][1]
+                print(f"数据时间选择: {formatted_time} ({occurrence_count}/{len(time_strings)} 次，{occurrence_count/len(time_strings)*100:.1f}%)")
+            else:
+                formatted_time = "12:00"
+                print("无有效时间数据，使用默认中午12:00")
+
+            print("正在计算平均气压...")
+            pressure = get_pressure_at_location(
+                lat=sample_df['纬度'].mean(),
+                lon=sample_df['经度'].mean(),
+                altitude=mean_height,
+                date=formatted_date,
+                time=formatted_time
+            )
+
+            if pressure is not None:
+                pressures = [pressure] * len(sample_df)
+                print("✅ 平均气压计算成功，将应用到所有行")
+            else:
+                print("❌ 平均气压计算失败")
+                pressures = [None] * len(sample_df)
+
+        except Exception as e:
+            print(f"❌ 平均气压计算失败: {e}")
+            pressures = [None] * len(sample_df)
+
+    else:
+        # 使用高度分档策略
+        print("🏗️  开始分档计算气压...")
+
+        # 为每个高度档位计算气压
+        bin_pressures = {}  # bin_center -> pressure
+
+        # 设置进度条
+        iterator = height_bins
+        if HAS_TQDM:
+            iterator = tqdm(iterator, total=len(height_bins), desc="计算气压档位", unit="档")
+
+        for bin_min, bin_max, bin_center, count in iterator:
+            try:
+                # 使用第一行数据作为代表来获取日期和时间
+                # 找到这个档位中的一行数据
+                bin_rows = sample_df[(sample_df['融合高程'] >= bin_min) &
+                                   (sample_df['融合高程'] <= bin_max)]
+                if len(bin_rows) == 0:
+                    continue
+
+                first_row = bin_rows.iloc[0]
+
+                # 转换日期格式 - 只提取日期部分，移除任何时间信息
+                date_str = str(first_row['日期'])
+                if ' ' in date_str:
+                    date_str = date_str.split(' ')[0]
+                elif 'T' in date_str:
+                    date_str = date_str.split('T')[0]
+
+                if '/' in date_str:
+                    date_str = date_str.replace('/', '-')
+
+                date_parts = date_str.split('-')
+                if len(date_parts) == 3 and len(date_parts[0]) == 4:
+                    year, month, day = date_parts
+                    formatted_date = f"{year}-{month.zfill(2)}-{day.zfill(2)}"
+                else:
+                    print(f"⚠️  档位高度 {bin_center:.1f}m 日期格式异常: {date_str}")
+                    bin_pressures[bin_center] = None
+                    continue
+
+                # 使用该档位数据的代表性时间（整点小时）
+                time_strings = []
+                for time_val in bin_rows['时间']:
+                    time_str = str(time_val).strip()
+                    if ':' in time_str:
+                        # 确保是有效的 HH:MM 格式，然后取整点小时
+                        parts = time_str.split(':')
+                        if len(parts) >= 2:
+                            try:
+                                hour = int(parts[0])
+                                # 确保小时在有效范围内 (0-23)
+                                if 0 <= hour <= 23:
+                                    time_strings.append(f"{hour:02d}:00")
+                            except ValueError:
+                                continue
+
+                if time_strings:
+                    # 使用最常见的时间（众数）
+                    time_counts = Counter(time_strings)
+                    formatted_time = time_counts.most_common(1)[0][0]
+                    occurrence_count = time_counts.most_common(1)[0][1]
+                    print(f"    档位时间选择: {formatted_time} ({occurrence_count}/{len(time_strings)} 次，{occurrence_count/len(time_strings)*100:.1f}%)")
+                else:
+                    # 如果没有有效时间，使用中午12点
+                    formatted_time = "12:00"
+                    print("    无有效时间数据，使用默认中午12:00")
+
+                # 计算这个档位的气压（使用平均位置和档位中心高度）
+                avg_lat = bin_rows['纬度'].mean()
+                avg_lon = bin_rows['经度'].mean()
+
+                pressure = get_pressure_at_location(
+                    lat=avg_lat,
+                    lon=avg_lon,
+                    altitude=bin_center,
+                    date=formatted_date,
+                    time=formatted_time
+                )
+
+                bin_pressures[bin_center] = pressure
+
+                # 更新进度条
+                if HAS_TQDM:
+                    success_count = sum(1 for p in bin_pressures.values() if p is not None)
+                    iterator.set_description(f"计算档位 (成功: {success_count}/{len(bin_pressures)})")
+
+            except Exception as e:
+                print(f"❌ 计算高度档位 {bin_center:.1f}m 气压失败: {e}")
+                bin_pressures[bin_center] = None
+
+        # 为每一行分配对应档位的气压
+        pressures = []
+        for idx, row in sample_df.iterrows():
+            # 找到这个高度对应的档位
+            height = row['融合高程']
+            assigned_pressure = None
+
+            for bin_min, bin_max, bin_center, count in height_bins:
+                if bin_min <= height <= bin_max:
+                    assigned_pressure = bin_pressures.get(bin_center)
+                    break
+
+            pressures.append(assigned_pressure)
+
+        print(f"✅ 完成分档气压计算，共 {len(bin_pressures)} 个档位，{len([p for p in bin_pressures.values() if p is not None])} 个成功")
+
+    # 添加气压列
+    df['pressure'] = None  # 初始化
+    df.loc[sample_df.index, 'pressure'] = pressures
+
+    # 对于未计算的行，使用插值或平均值填充
+    if max_samples is not None and len(df) > max_samples:
+        # 只计算了部分行，用平均值填充其余行
+        valid_pressures_for_mean = [p for p in pressures if p is not None]
+        if valid_pressures_for_mean:
+            mean_pressure = sum(valid_pressures_for_mean) / len(valid_pressures_for_mean)
+            df['pressure'] = df['pressure'].fillna(mean_pressure)
+            print(f"使用平均气压填充其余 {len(df) - max_samples} 行: {mean_pressure:.1f} hPa")
+
+    # 统计信息
+    valid_pressures = [p for p in pressures if p is not None]
+    if valid_pressures:
+        avg_pressure = sum(valid_pressures) / len(valid_pressures)
+        print(f"成功计算 {len(valid_pressures)}/{actual_samples} 个气压值，平均值: {avg_pressure:.1f} hPa")
+    else:
+        print("⚠️  未能计算出任何气压值")
+    return df
+
+
+def adjust_altitude(df):
+    """
+    调整融合高程（减去最小值）
+
+    Args:
+        df: 输入DataFrame
+
+    Returns:
+        pd.DataFrame: 调整后的DataFrame
+    """
+    print("调整融合高程...")
+
+    if '融合高程' in df.columns:
+        min_altitude = df['融合高程'].min()
+        print(".2f")
+
+        original_alt = df['融合高程'].head(3).tolist()
+        df['融合高程'] = df['融合高程'] - min_altitude
+        adjusted_alt = df['融合高程'].head(3).tolist()
+
+        print("高度调整示例:")
+        for orig, adj in zip(original_alt, adjusted_alt):
+            print(".2f")
+
+    return df
+
+
+def merge_timestamp(df):
+    """
+    融合日期和时间列为时间戳
+
+    Args:
+        df: 输入DataFrame
+
+    Returns:
+        pd.DataFrame: 融合后的DataFrame
+    """
+    print("融合日期和时间...")
+
+    if '日期' in df.columns and '时间' in df.columns:
+        timestamps = []
+
+        for idx, row in df.iterrows():
+            try:
+                date_str = str(row['日期'])
+                time_str = str(row['时间'])
+
+                # 清理日期字符串 - 移除任何时间部分
+                date_str = date_str.strip()
+                if ' ' in date_str:
+                    date_str = date_str.split(' ')[0]  # 只取日期部分
+                if 'T' in date_str:
+                    date_str = date_str.split('T')[0]  # 处理ISO格式
+
+                # 标准化日期格式
+                if '/' in date_str:
+                    date_str = date_str.replace('/', '-')
+
+                # 确保日期格式正确
+                date_parts = date_str.split('-')
+                if len(date_parts) == 3:
+                    year, month, day = date_parts
+                    date_formatted = f"{year.zfill(4)}-{month.zfill(2)}-{day.zfill(2)}"
+                else:
+                    print(f"⚠️  日期格式异常: '{date_str}'，使用当前日期")
+                    date_formatted = datetime.now().strftime("%Y-%m-%d")
+
+                # 时间字符串已经是修正后的格式（如 "08:34:01"），直接使用
+                time_str = time_str.strip()
+                if ':' in time_str and len(time_str.split(':')) >= 2:
+                    time_formatted = time_str
+                else:
+                    print(f"⚠️  时间格式异常: '{time_str}'，使用默认时间")
+                    time_formatted = "12:00:00"
+
+                # 组合时间戳 - 直接连接日期和时间
+                timestamp = f"{date_formatted} {time_formatted}"
+                timestamps.append(timestamp)
+
+            except Exception as e:
+                print(f"❌ 处理第 {idx+1} 行时间戳失败: {e}")
+                timestamps.append(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+
+        df['timestamp'] = timestamps
+
+        print("时间戳融合示例:")
+        for i in range(min(3, len(timestamps))):
+            print(f"  {df.loc[i, '日期']} + {df.loc[i, '时间']} → {timestamps[i]}")
+
+    return df
+
+
+def rename_columns(df):
+    """
+    重命名字段为GasFlux标准格式
+
+    Args:
+        df: 输入DataFrame
+
+    Returns:
+        pd.DataFrame: 重命名后的DataFrame
+    """
+    print("重命名字段...")
+
+    # 定义字段映射
+    column_mapping = {
+        'timestamp': 'timestamp',      # 时间戳（已创建）
+        '经度': 'longitude',            # 经度 → latitude
+        '纬度': 'latitude',           # 纬度 → longitude
+        '融合高程': 'height_ato',      # 融合高程 → height_ato
+        '修正风向': 'winddir',         # 修正风向 → winddir
+        '修正风速': 'windspeed',       # 修正风速 → windspeed
+        '风温': 'temperature',         # 风温 → temperature
+        'pressure': 'pressure',        # 气压（已计算）
+        'CH4': 'ch4',                  # CH4保持不变
+        'pitch': 'course_elevation',   # pitch → course_elevation
+        'yaw': 'course_azimuth'        # yaw → course_azimuth
+    }
+
+    # 重命名存在的列
+    columns_to_rename = {}
+    for old_name, new_name in column_mapping.items():
+        if old_name in df.columns:
+            columns_to_rename[old_name] = new_name
+
+    if columns_to_rename:
+        df = df.rename(columns=columns_to_rename)
+        print("字段重命名:")
+        for old, new in columns_to_rename.items():
+            print(f"  {old} → {new}")
+
+    # 只保留GasFlux需要的列
+    required_columns = ['timestamp', 'latitude', 'longitude', 'height_ato', 'windspeed', 'winddir', 'temperature', 'pressure', 'ch4', 'course_elevation', 'course_azimuth']
+    existing_required_columns = [col for col in required_columns if col in df.columns]
+
+    if len(existing_required_columns) != len(required_columns):
+        missing = [col for col in required_columns if col not in df.columns]
+        print(f"⚠️  缺少必需列: {missing}")
+
+    # 移除不需要的列，只保留必需的列
+    df = df[existing_required_columns]
+    print(f"最终保留列: {existing_required_columns}")
+
+    return df
+
+
+def process_excel_file(file_path):
+    """
+    处理单个Excel文件的主函数
+
+    Args:
+        file_path: Excel文件路径
+    """
+    print(f"=== 开始处理文件: {file_path} ===\n")
+
+    # 获取文件名（用于时间修正）
+    filename = Path(file_path).name
+
+    # 1. 读取数据
+    df = load_excel_data(file_path)
+
+    # 2. 删除不需要的列
+    columns_to_remove = [
+        '高程', '速度x', '速度y', '速度z',
+        '四元数_q0', '四元数_q1', '四元数_q2', '四元数_q3',
+        'roll', 'H2O',  # 保留pitch和yaw，将重命名为course_elevation和course_azimuth
+        '原始风向', '原始风速'
+    ]
+    df = remove_columns(df, columns_to_remove)
+
+    # 3. 修正时间格式
+    df = fix_time_column(df, filename)
+
+    # 4. 坐标转换
+    df = convert_coordinates(df)
+
+    # 5. 计算气压
+    df = calculate_pressure(df, max_samples=None, height_tolerance=10.0, height_bin_size=2.0)  # 计算所有行，高度容差10米，分档2米
+
+    # 6. 高度调整
+    df = adjust_altitude(df)
+
+    # 7. 时间戳融合
+    df = merge_timestamp(df)
+
+    # 调试：检查当前列
+    print(f"时间戳融合后列名: {list(df.columns)}")
+    if 'timestamp' in df.columns:
+        print(f"timestamp列示例: {df['timestamp'].head(3).tolist()}")
+
+    # 8. 字段重命名
+    df = rename_columns(df)
+
+    # 保存处理结果
+    output_path = Path(file_path).with_suffix('.processed.csv')
+    df.to_csv(output_path, index=False)
+
+    print(f"\n✅ 处理完成！")
+    print(f"📁 输出文件: {output_path}")
+    print(f"📊 最终数据形状: {df.shape[0]} 行 × {df.shape[1]} 列")
+    print(f"📋 最终列名: {list(df.columns)}")
+
+    return df
+
+
+def process_file(input_file, output_file=None):
+    """
+    直接处理Excel文件的函数（不使用命令行参数）
+
+    Args:
+        input_file: 输入Excel文件路径（字符串或Path对象）
+        output_file: 输出CSV文件路径（可选，字符串或Path对象）
+
+    Returns:
+        pd.DataFrame: 处理后的DataFrame
+    """
+    # 转换为Path对象
+    input_path = Path(input_file)
+
+    # 检查输入文件
+    if not input_path.exists():
+        raise FileNotFoundError(f"输入文件不存在: {input_path}")
+
+    if input_path.suffix.lower() not in ['.xlsx', '.xls']:
+        raise ValueError(f"输入文件必须是Excel格式 (.xlsx 或 .xls)，当前文件: {input_path}")
+
+    # 处理文件
+    df = process_excel_file(str(input_path))
+
+    # 如果指定了输出路径，额外保存一份
+    if output_file:
+        output_path = Path(output_file)
+        df.to_csv(output_path, index=False)
+        print(f"📁 额外保存到: {output_path}")
+
+    return df
+
+
+def interactive_input():
+    """
+    交互式输入模式 - 手动输入参数
+
+    Returns:
+        tuple: (input_file, output_file) 文件路径元组
+    """
+    print("=== 手动输入模式 ===")
+    print("请按照提示输入参数...\n")
+
+    # 输入文件路径
+    while True:
+        input_file = input("请输入Excel文件路径 (例如: data.xlsx): ").strip()
+        if not input_file:
+            print("❌ 文件路径不能为空，请重新输入")
+            continue
+
+        input_path = Path(input_file)
+        if not input_path.exists():
+            print(f"❌ 文件不存在: {input_path}")
+            print("提示: 请确保文件路径正确，或者将文件放在当前目录下")
+            continue
+
+        if input_path.suffix.lower() not in ['.xlsx', '.xls']:
+            print(f"❌ 文件格式错误: {input_path.suffix}")
+            print("只支持 .xlsx 和 .xls 格式的Excel文件")
+            continue
+
+        break
+
+    # 输出文件路径（可选）
+    output_file = input("请输入输出CSV文件路径 (可选，直接回车使用默认): ").strip()
+    if not output_file:
+        output_file = None
+        print("使用默认输出文件名")
+
+    print(f"\n✅ 输入确认:")
+    print(f"   输入文件: {input_file}")
+    print(f"   输出文件: {output_file or '自动生成'}")
+
+    confirm = input("\n确认开始处理? (y/N): ").strip().lower()
+    if confirm not in ['y', 'yes', '是', '确认']:
+        print("❌ 用户取消操作")
+        return None, None
+
+    return input_file, output_file
+
+
+def main(input_file=None, output_file=None, interactive=False):
+    """
+    主函数 - 支持多种输入方式
+
+    Args:
+        input_file: 输入文件路径（直接调用时使用）
+        output_file: 输出文件路径（直接调用时使用）
+        interactive: 是否启用交互式输入模式
+    """
+    # 如果启用交互式输入
+    if interactive:
+        input_file, output_file = interactive_input()
+        if input_file is None:
+            return None
+
+    # 如果提供了直接参数，使用直接参数
+    if input_file is not None:
+        try:
+            return process_file(input_file, output_file)
+        except Exception as e:
+            print(f"❌ 处理失败: {e}")
+            raise
+
+    # 否则使用命令行参数
+    import argparse
+
+    parser = argparse.ArgumentParser(
+        description="无人机数据处理工具 - 将Excel数据转换为GasFlux格式",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+使用方式:
+
+1. 命令行模式:
+   python data_processor.py data.xlsx
+   python data_processor.py data.xlsx -o output.csv
+
+2. 交互式模式:
+   python data_processor.py --interactive
+
+3. 直接调用:
+   from data_processor import process_file
+   df = process_file('data.xlsx', 'output.csv')
+
+4. Python脚本调用:
+   from data_processor import main
+   df = main(input_file='data.xlsx', output_file='output.csv')
+
+处理步骤:
+1. 读取Excel文件
+2. 删除不需要的列
+3. 根据文件名修正时间格式
+4. 经纬度坐标转换 (除以10^7)
+5. 计算气压数据
+6. 高度调整 (减去最小值)
+7. 时间戳融合
+8. 字段重命名为GasFlux格式
+        """
+    )
+
+    parser.add_argument('input_file', nargs='?', help='输入的Excel文件路径')
+    parser.add_argument('-o', '--output', help='输出CSV文件路径（可选，默认自动生成）')
+    parser.add_argument('-i', '--interactive', action='store_true', help='启用交互式输入模式')
+
+    args = parser.parse_args()
+
+    # 如果启用交互式模式
+    if args.interactive:
+        input_file, output_file = interactive_input()
+        if input_file is None:
+            return None
+    else:
+        input_file = args.input_file
+        output_file = args.output
+
+        # 如果没有提供输入文件，显示帮助和使用示例
+        if not input_file:
+            parser.print_help()
+            print("\n" + "="*60)
+            print("📖 使用示例:")
+            print("="*60)
+            print("1. 命令行模式:")
+            print("   python data_processor.py your_file.xlsx")
+            print("   python data_processor.py data.xlsx -o output.csv")
+            print("")
+            print("2. Python脚本中直接调用:")
+            print("   from data_processor import process_file")
+            print("   df = process_file('input.xlsx')")
+            print("   df = process_file('input.xlsx', 'output.csv')")
+            print("")
+            print("3. 交互式模式:")
+            print("   python data_processor.py --interactive")
+            print("="*60)
+            return None
+
+    # 检查输入文件
+    input_path = Path(input_file)
+    if not input_path.exists():
+        print(f"❌ 错误：输入文件不存在: {input_path}")
+        sys.exit(1)
+
+    if input_path.suffix.lower() not in ['.xlsx', '.xls']:
+        print(f"❌ 错误：输入文件必须是Excel格式 (.xlsx 或 .xls)")
+        sys.exit(1)
+
+    # 处理文件
+    try:
+        df = process_excel_file(str(input_path))
+
+        # 如果指定了输出路径，额外保存一份
+        if output_file:
+            output_path = Path(output_file)
+            df.to_csv(output_path, index=False)
+            print(f"📁 额外保存到: {output_path}")
+
+        return df
+
+    except KeyboardInterrupt:
+        print("\n⚠️  用户中断处理")
+        sys.exit(1)
+    except Exception as e:
+        print(f"\n❌ 处理失败: {e}")
+        import traceback
+        traceback.print_exc()
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    # 当直接运行脚本时，使用命令行参数模式
+    main()
\ No newline at end of file
diff --git a/src/gasflux/gas.py b/src/gasflux/gas.py
new file mode 100644
index 0000000..4e5620f
--- /dev/null
+++ b/src/gasflux/gas.py
@@ -0,0 +1,58 @@
+"""functions related to gas transformations and calculations, e.g. density, point flux etc."""
+
+import molmass
+import pandas as pd
+
+gas_variables = {
+    "standard_pressure": 1013.25,  # hPa/mbar
+    "standard_temperature": 273.15,  # degrees K
+    "standard_molar_volume": 0.022413969545014,  # m3⋅mol-1
+}
+
+
+def mass(formula: str) -> float:
+    """Return the molar mass of a gas in g/mol."""
+    return molmass.Formula(formula.upper()).mass  # only accepts capital letters
+
+
+def gas_density(local_pressure: float, local_temperature_celsius: float, gas: str) -> float:  # millibars and celsius
+    """
+    Calculate the density of a gas in kg/m3 based on local pressure and temperature.
+
+    Parameters:
+    - local_pressure: The local pressure in hPa/mbar.
+    - local_temperature: The local temperature in degrees Celsius.
+    - gas: The chemical formula of the gas.
+
+    Returns:
+    - The density of the gas in kg/m3.
+
+    Assumes ideal gas behavior.
+    """
+    local_temperature_kelvin = local_temperature_celsius + gas_variables["standard_temperature"]
+    local_volume = (
+        gas_variables["standard_molar_volume"]
+        * (gas_variables["standard_pressure"] / local_pressure)
+        * ((local_temperature_kelvin + gas_variables["standard_temperature"]) / gas_variables["standard_temperature"])
+    )  # m3⋅mol-1
+    return mass(gas) / 1000 / local_volume
+
+
+def gas_flux_column(df: pd.DataFrame, gas: str, wind: str = "windspeed") -> pd.DataFrame:
+    """
+    Add columns to the DataFrame for the gas density, mass, and flux.
+
+    Parameters:
+    - df: The DataFrame.
+    - gas: The chemical formula of the gas.
+    - wind: The column name for the wind speed (NB - must be perpendicular to the plane)
+
+    Returns:
+    - The DataFrame with the added columns.
+    """
+    average_temp = df["temperature"].mean()  # celsius
+    average_pressure = df["pressure"].mean()  # hPa
+    gd = gas_density(local_pressure=average_pressure, local_temperature_celsius=average_temp, gas=gas)  # kg/m3
+    df[f"{gas}_kg_m3"] = gd * (df[f"{gas}_normalised"] * 1e-6)  # kg/m3
+    df[f"{gas}_kg_h_m2"] = df[f"{gas}_kg_m3"] * df[wind] * 60 * 60  # kg/h/m2
+    return df
diff --git a/src/gasflux/gasflux_config.yaml b/src/gasflux/gasflux_config.yaml
new file mode 100644
index 0000000..b3723a8
--- /dev/null
+++ b/src/gasflux/gasflux_config.yaml
@@ -0,0 +1,69 @@
+# gasflux_config.yaml
+
+output_dir: ~/gasflux_reports
+
+# required columns and maximum ranges
+required_cols:
+  latitude: [-90, 90]
+  longitude: [-180, 180]
+  height_ato: [-200, 500] # meters above takeoff
+  windspeed: [0, 50] # m/s
+  winddir: [0, 360] # degrees
+  temperature: [-50, 60] # degrees Celsius
+  pressure: [900, 1100] # hPa/mb
+
+# optional gas columns and maximum ppm ranges. Relative concentrations are used so offset can be wrong as long as gain and linearity are correct.
+gases:
+  ch4: [1.5, 500]
+  co2: [300, 5000]
+  c2h6: [-0.5, 10]
+
+strategies:
+  background: "algorithm" # currently only algorithm (via pybaselines) is supported
+  sensor: "insitu" # currently only insitu is supported
+  spatial: "curtain" # currently "curtain" and "spiral" are supported
+  interpolation: "kriging" # currently only kriging is supported
+
+# baseline settings. 
+algorithmic_baseline_settings:
+  algorithm: fastchrom
+  fastchrom: {
+    "half_window": 6, 
+    "threshold": "custom", #
+    "min_fwhm": ~,
+    "interp_half_window": 3, 
+    "smooth_half_window": 3, 
+    "weights": ~, 
+    "max_iter": 100, 
+    "min_length": 2}
+  fabc : {
+    "lam": 10000,  # The smoothing parameter. Larger values will create smoother baselines. Default is 1e6.
+    "scale": 10,  # The scale at which to calculate the continuous wavelet transform. Should be approximately equal to the index-based full-width-at-half-maximum of the peaks or features in the data.  Default is None, which will use half of the value from optimize_window(), which is not always a good value, but at least scales with the number of data points and gives a starting point for tuning the parameter.
+    "diff_order": 2}  # The order of the differential matrix. Must be greater than 0. Default is 2 (second order differential matrix). Typical values are 2 or 1.
+  dietrich : {
+    "poly_order": 5,
+    "smooth_half_window": 5,}
+  golotvin : {
+    "half_window": 2, 
+    "sections": 10}
+
+# kriging settings
+semivariogram_settings:
+  model: spherical
+  estimator: cressie
+  n_lags: 20
+  bin_func: even
+  fit_method: lm
+  ### Size of the search window; if the algorithm complains about not having enough neighbours, consider increasing these
+  maxlag: 100 # in meters. 
+  tolerance: 10 # in degrees
+  azimuth: 0 # in degrees (0 is right/horizontal)
+  bandwidth: 20 # in meters 
+   # fit_sigma: linear # this should allow for a spatial uncertainty but currently producing bugs
+ordinary_kriging_settings:
+  min_points: 3 
+  max_points: 100
+  grid_resolution: 500
+  min_nodes: 10
+  y_min: ~  # manual override for the minimum y value. Leave blank or ~ to use ymin.
+  cut_ground: True # cuts everything below the ground level in the krig (need height_agl to work at the moment)
diff --git a/src/gasflux/interpolation.py b/src/gasflux/interpolation.py
new file mode 100644
index 0000000..b4dba90
--- /dev/null
+++ b/src/gasflux/interpolation.py
@@ -0,0 +1,171 @@
+"""Functions related to kriging and other kinds of interpolation"""
+
+import numpy as np
+import pandas as pd
+import skgstat as skg
+from scipy import integrate
+
+from . import plotting
+
+
+def simpsonintegrate(array: np.ndarray, x_cell_size: float, y_cell_size: float) -> float:
+    """Function to obtain the volume of the krig in kgh⁻¹, i.e. the cut-fill volume
+    (negative volumes from background noise are subtracted)."""
+    grid = np.nan_to_num(array.copy(), copy=False, nan=0)
+    vol_rows = integrate.simpson(np.transpose(grid))  # this integrates along each row of the grid
+    vol_grid = integrate.simpson(vol_rows)  # this integrates the rows together
+    return vol_grid * x_cell_size * y_cell_size  # type: ignore
+
+
+def directional_gas_semivariogram(
+    df: pd.DataFrame, x: str, z: str, gas: str, semivariogram_filter: float | None = None, **semivariogram_settings
+):
+    """Function to calculate the directional semivariogram - typically horizontally - of a gas in a dataframe."""
+    if semivariogram_filter:
+        df = df[df[gas] > semivariogram_filter]
+    v = skg.DirectionalVariogram(
+        df[[x, z]].to_numpy(),
+        df[gas].to_numpy(),
+        **semivariogram_settings,
+    )
+    return v
+
+
+def ordinary_kriging(
+    df: pd.DataFrame,
+    x: str,
+    y: str,
+    gas: str,
+    ordinary_kriging_settings: dict,
+    semivariogram_filter: float | None = None,
+    **semivariogram_settings,
+):
+    """Function to calculate the ordinary kriging of a gas in a dataframe, after calculating a semivariogram."""
+    gasflux = f"{gas}_kg_h_m2"
+    skg.plotting.backend("plotly")  # type: ignore
+    cut_ground = ordinary_kriging_settings["cut_ground"]
+    semivariogram = directional_gas_semivariogram(df, x, y, gasflux, semivariogram_filter, **semivariogram_settings)
+    ok = skg.OrdinaryKriging(
+        semivariogram,
+        coordinates=df[[x, y]].to_numpy(),
+        values=df[gasflux].to_numpy(),
+        min_points=ordinary_kriging_settings["min_points"],
+        max_points=ordinary_kriging_settings["max_points"],
+    )
+    x_max = df[x].max()
+    x_min = df[x].min()
+    y_max = df[y].max()
+    y_min = df[y].min() if ordinary_kriging_settings["y_min"] is None else ordinary_kriging_settings["y_min"]
+    if cut_ground is True:
+        df["ground_elevation_ato"] = df.loc[:, "height_ato"] - df.loc[:, "height_agl"]
+        y_min = min(df["ground_elevation_ato"].min(), y_min)
+    x_range, y_range = x_max - x_min, y_max - y_min
+    cell_rough_size = np.sqrt((x_range * y_range) / ordinary_kriging_settings["grid_resolution"])
+    x_nodes, y_nodes = (
+        max(int(r / cell_rough_size), ordinary_kriging_settings["min_nodes"]) for r in [x_range, y_range]
+    )
+    x_cell_size = (x_max - x_min) / x_nodes
+    y_cell_size = (y_max - y_min) / y_nodes
+    xx, yy = np.mgrid[
+        x_min : x_max : x_nodes * 1j,
+        y_min : y_max : y_nodes * 1j,  # type: ignore
+    ]  # type: ignore
+    field = ok.transform(xx.flatten(), yy.flatten()).reshape(xx.shape)
+    if cut_ground:
+        field = remove_values_below_ground(df, field, xx, yy)
+    volume = simpsonintegrate(field, x_cell_size, y_cell_size)
+
+    fieldpos = np.copy(field)
+    fieldpos[fieldpos < 0] = 0
+    volumepos = simpsonintegrate(fieldpos, x_cell_size, y_cell_size)
+
+    fieldneg = np.copy(field)
+    fieldneg[fieldneg > 0] = 0
+    volumeneg = simpsonintegrate(fieldneg, x_cell_size, y_cell_size)
+
+    error_1s = ok.sigma.reshape(xx.shape)
+    # np.nan_to_num(error_1s, copy=False, nan=0)
+    volume_error = simpsonintegrate(error_1s, x_cell_size, y_cell_size)
+
+    contour_plot = plotting.contour_krig(df=df, gas=gas, xx=xx, yy=yy, field=field, x=x, y=y, cut_ground=cut_ground)
+    grid_plot = plotting.heatmap_krig(xx, yy, field)
+    output_text = (
+        f"The emissions flux of {gas.upper()} is {volume:.3f}kgh⁻¹; "
+        f"the cut and fill volumes of the grid are {volumepos:.3f} and {volumeneg:.3f}kgh⁻¹. "
+        f"The grid itself is {x_nodes}x{y_nodes} nodes, with nodes measuring {x_cell_size:.2f}m x {y_cell_size:.2f}m."
+    )
+    krig_variables = {
+        "gas": gas,
+        "field": field,
+        "fieldpos": fieldpos,
+        "fieldneg": fieldneg,
+        "xx": xx,
+        "yy": yy,
+        "volume": volume,
+        "volumepos": volumepos,
+        "volumeneg": volumeneg,
+        "error field (1 sigma)": error_1s,
+        "volume_error": volume_error,
+    }
+    semivariogram_plot = semivariogram.plot(show=False)
+
+    return krig_variables, output_text, contour_plot, grid_plot, semivariogram_plot
+
+
+def remove_values_below_ground(
+    df: pd.DataFrame, field: np.ndarray, xx: np.ndarray, yy: np.ndarray, x: str = "x", alt: str = "height_ato"
+) -> np.ndarray:
+    """
+    Adjust field values based on elevation data, setting values below ground to NaN.
+    """
+    max_x = df[x].max()
+    max_y = df[alt].max()
+
+    x_right = np.empty_like(xx)
+    x_right[:-1, :] = xx[1:, :]
+    x_right[-1, :] = max_x
+    x_points = (xx + x_right) / 2
+
+    y_top = np.empty_like(yy)
+    y_top[:, :-1] = yy[:, 1:]
+    y_top[:, -1] = max_y
+    y_points = (yy + y_top) / 2
+
+    x_points_flat = x_points.ravel()
+    y_points_flat = y_points.ravel()
+
+    ground_levels = compute_relative_ground_levels(df, x_points_flat)
+
+    below_ground = y_points_flat < ground_levels
+    field_flat = field.ravel()
+    field_flat[below_ground] = np.nan
+
+    return field_flat.reshape(field.shape)
+
+
+def compute_relative_ground_levels(
+    df: pd.DataFrame, x_points: np.ndarray, y1: str = "height_agl", y2: str = "height_ato"
+) -> np.ndarray:
+    """
+    Calculate ground levels at given x coordinates, considering elevation above ground and takeoff altitude. A
+    sort of janky averaged DEM, basically. Will accept either ground elevation and altitude, or height above ground
+    level and height above takeoff as inputs for y1 and y2.
+    """
+    df_clean = df.dropna(subset=[y1, y2])
+    df_sorted = df_clean.sort_values(by="x").drop_duplicates(subset="x")
+    y1_at_x = np.interp(x_points, df_sorted["x"], df_sorted[y1])
+    y2_at_x = np.interp(x_points, df_sorted["x"], df_sorted[y2])
+
+    return y2_at_x - y1_at_x
+
+
+# def additive_row_integration(df: pd.DataFrame, rowlabel: str = "slice"):
+#     """2D integration of a dataframe along the x-axis, with the altitude as the y-axis."""
+#     integrals = {}
+#     for i in range(df[rowlabel].max() + 1):
+#         df_slice = df[df[rowlabel] == i]
+#         df_slice = df_slice.sort_values(by="x")
+#         line_integral = integrate.simpson(y=df_slice["ch4_kg_h_m2"], x=df_slice["x"])
+#         area_integral = line_integral * (df_slice["altitude"].max() - df_slice["altitude"].min())
+#         integrals[i] = area_integral
+#     return integrals
diff --git a/src/gasflux/ml.py b/src/gasflux/ml.py
new file mode 100644
index 0000000..8c77567
--- /dev/null
+++ b/src/gasflux/ml.py
@@ -0,0 +1,61 @@
+"""Experimental module for machine learning flight filtering"""
+
+import os
+
+import joblib
+import pandas as pd
+
+from . import plotting
+import plotly.graph_objects as go
+
+model = None  # Lazy loading: Load the model only if it hasn't been loaded yet
+
+
+def load_model():
+    """Load the model from the model file path. If the model has already been loaded, return it."""
+    global model
+    if model is None:
+        default_model_path = os.path.join(os.path.dirname(__file__), "resources/model.pkl")
+        model_file_path = os.getenv("GASFLUX_MODEL_PATH", default_model_path)
+        try:
+            model = joblib.load(model_file_path)
+        except FileNotFoundError as e:
+            raise FileNotFoundError(f"Model file not found at {model_file_path}. Please check the file path.") from e
+        except Exception as e:
+            raise Exception("An error occurred while loading the model.") from e
+
+    return model
+
+
+def make_prediction(
+    df: pd.DataFrame,
+    course_elevation="course_elevation",
+    height_ato="height_ato",
+    horiz_spd="horiz_spd",
+    z_spd="z_spd",
+) -> tuple[pd.DataFrame, go.Figure]:
+    """Make predictions based on the input DataFrame and add them to the DataFrame.
+    :param df: DataFrame containing the required features
+    :param course_azimuth: Name of the column containing the course azimuth
+    :param course_elevation: Name of the column containing the course elevation
+    :param height_ato: Name of the column containing the height above take-off
+    :param idx: Name of the column containing the index
+
+    return: Tuple of the DataFrame with the predictions and a Plotly 3D scatter plot of the predictions
+    """
+    model = load_model()
+    # Ensure the DataFrame contains all the required features
+    required_features = [course_elevation, height_ato, horiz_spd, z_spd]
+    if not all(feature in df.columns for feature in required_features):
+        missing_features = [feature for feature in required_features if feature not in df.columns]
+        raise ValueError(f"DataFrame is missing (or mislabelled) the following required features: {missing_features}")
+    # make idx col if not present
+    if "idx" not in df.columns:
+        df["idx"] = df.index
+    cols_for_model = ["course_elevation", "height_ato", "idx", "horiz_spd", "z_spd"]
+    predictions = model.predict(df[cols_for_model])
+
+    df["predictions"] = predictions
+    fig = plotting.scatter_3d(df)
+
+    return df, fig
diff --git a/src/gasflux/plotting.py b/src/gasflux/plotting.py
new file mode 100644
index 0000000..f7437ac
--- /dev/null
+++ b/src/gasflux/plotting.py
@@ -0,0 +1,609 @@
+"""Various plotting functions mainly based around plotly."""
+
+import matplotlib.colors as mcolors
+import numpy as np
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import plotly.io as pio
+import simplekml
+from plotly.subplots import make_subplots
+
+from . import processing
+
+pio.templates["default"] = go.layout.Template(
+    layout=go.Layout(
+        margin=go.layout.Margin(l=0, r=0, b=0, t=0, pad=0),
+    ),
+)
+
+pio.templates.default = "simple_white+default"
+
+
+styling = {
+    "colorscale": "geyser",
+}
+
+
+def blank_figure():
+    fig = go.Figure()
+    return fig
+
+
+def scatter_3d(
+    df: pd.DataFrame,
+    color: str = "",
+    colorbar_title: str = "",
+    timestamp: str = "timestamp",
+    x: str = "utm_easting",
+    y: str = "utm_northing",
+    z: str = "height_ato",
+    courses: bool = False,
+):
+    fig = px.scatter_3d(df, x=x, y=y, z=z)
+
+    if color:
+        custom_data = [df[timestamp]]
+        if courses:
+            custom_data.extend([df["course_elevation"], df["course_azimuth"]])
+        custom_data = np.stack(custom_data, axis=-1)
+        hover_template = [
+            f"{x}: %{{x:.2f}}",
+            f"{y}: %{{y:.2f}}",
+            f"{z}: %{{z:.2f}}",
+            f"{color}: %{{marker.color:.2f}}",
+            f"{timestamp}: %{{customdata[0]|%Y-%m-%d %H:%M:%S}}",
+            "Index: %{pointNumber}",
+        ]
+
+        if courses:
+            hover_template.extend(
+                [
+                    "Course Elevation: %{customdata[1]:.2f}",
+                    "Course Azimuth: %{customdata[2]:.2f}",
+                ]
+            )
+        hover_template_str = "<br>".join(hover_template)
+        fig.update_traces(
+            marker=dict(
+                color=df[color],
+                size=4,
+                opacity=0.5,
+                colorscale=styling["colorscale"],
+                colorbar=dict(title=colorbar_title),
+            ),
+            customdata=custom_data,
+            hovertemplate=hover_template_str,
+        )
+
+    return fig
+
+
+def scatter_2d(
+    df: pd.DataFrame,
+    x: str,
+    color: str,
+    y: str = "height_ato",
+    **kwargs,
+):
+    fig = px.scatter(
+        df,
+        x=x,
+        y=y,
+        color=color,
+        color_continuous_scale=styling["colorscale"],
+        opacity=0.8,
+        **kwargs,
+    )
+    fig.update_traces(
+        customdata=df.index,
+        hovertemplate="<br>".join(
+            [
+                "x: %{x:.2f}",
+                "height_ato: %{y:.2f}",
+                f"{color}: %{{marker.color:.2f}}",
+                "Time: %{customdata}",
+            ],
+        ),
+    )
+
+    return fig
+
+
+def time_series(
+    df: pd.DataFrame,
+    ys: str | list[str],
+    x: str = "timestamp",
+    color: str | None = None,
+    split=None,
+    y_mins: float | list[float | int] | None = None,
+    rolling_average: bool = True,
+    scatter: bool = True,
+    rolling_window: int = 5,
+    y_titles: str | list[str] | None = None,
+    legend: bool = True,
+) -> go.Figure:
+    colors = px.colors.qualitative.Plotly
+
+    if isinstance(ys, str):
+        ys = [ys]
+    if y_titles is None:
+        y_titles = ys
+        single_title = False
+    elif isinstance(y_titles, str):
+        y_titles = [y_titles]
+        single_title = True
+    elif isinstance(y_titles, list):
+        if len(y_titles) != len(ys):
+            raise ValueError("Length of y_titles must be equal to length of ys")
+        single_title = False
+    else:
+        raise ValueError("Invalid y_titles value")
+    if isinstance(y_mins, (float | int)):
+        y_mins = [y_mins]
+    if isinstance(y_mins, list):
+        if len(y_mins) != len(ys):
+            raise ValueError("Length of y_mins must be equal to length of ys")
+
+    fig = go.Figure()
+
+    axis_space = 0.05
+    domain_start = axis_space * (len(ys)) if len(ys) > 1 else 0
+    fig.update_layout(
+        xaxis=dict(
+            domain=[domain_start, 1],
+        ),
+    )
+
+    for i, y in enumerate(ys):
+        yaxis_name = f"yaxis{i+1}"
+        yaxis_ref = f"y{i+1}"
+
+        trace_color = "black" if single_title and i == 0 else colors[i % len(colors)]
+
+        marker_i = dict(size=8, opacity=0.3 if rolling_average else 0.5, color=trace_color)
+        if color is not None:
+            marker_i["color"] = df[color]  # type: ignore
+            marker_i["colorscale"] = styling["colorscale"]
+
+        hover_template = f"{x}: %{{x}}<br>{y}: %{{y:.2f}}<br>"
+        if color:
+            hover_template += f"{color}: %{{marker.color:.2f}}<br>"
+
+        if scatter:
+            fig.add_trace(
+                go.Scatter(
+                    x=df[x],
+                    y=df[y],
+                    name=y,
+                    mode="markers",
+                    marker=marker_i,
+                    yaxis=yaxis_ref,
+                    hovertemplate=hover_template,
+                    showlegend=legend,
+                )
+            )
+
+        if rolling_average:
+            df[f"rolling_avg_{i}"] = df[y].rolling(window=rolling_window, min_periods=1).mean()
+            fig.add_trace(
+                go.Scatter(
+                    x=df[x],
+                    y=df[f"rolling_avg_{i}"],
+                    name=f"{y} {rolling_window}-point avg",
+                    mode="lines",
+                    line=dict(color=trace_color, width=2),
+                    yaxis=yaxis_ref,
+                    showlegend=legend,
+                )
+            )
+
+        y_data = df[y]
+        y_min_var = y_data.min()
+        y_max_var = y_data.max()
+        y_range = y_max_var - y_min_var or y_max_var * 0.05
+
+        y_axis_min = y_mins[i] if y_mins is not None and y_mins[i] is not None else y_min_var - y_range * 0.05
+        y_axis_max = y_max_var + y_range * 0.05
+
+        if single_title and i == 0:
+            axis_title = dict(text=y_titles[0], font=dict(color="black"))
+        elif not single_title:
+            axis_title = dict(text=y_titles[i], font=dict(color=trace_color))
+        else:
+            axis_title = None
+
+        axis_config = dict(
+            title=axis_title,
+            tickfont=dict(color=trace_color),
+            range=[y_axis_min, y_axis_max],
+            side="left",
+            position=axis_space * i if i > 0 else None,
+            anchor="free" if i > 0 else None,
+            overlaying="y" if i > 0 else None,
+            showgrid=(i == 0),
+        )
+
+        fig.layout[yaxis_name] = axis_config
+
+    if split is not None:
+        fig.add_shape(
+            type="line",
+            xref="x",
+            yref="paper",
+            x0=split,
+            y0=0,
+            x1=split,
+            y1=1,
+            line=dict(color="red", width=2),
+        )
+
+    return fig
+
+
+def background_plotting(df: pd.DataFrame, gas: str):
+    fig = make_subplots(specs=[[{"secondary_y": True}]])
+    ymin = df[gas].min()
+    ymax = df[gas].max()
+    ylim = [ymin * 0.95, ymax * 1.05]
+    y2min = df[f"{gas}_normalised"].min()
+    y2lim = (y2min, y2min + (ylim[1] - ylim[0]))
+    fig.update_yaxes(range=ylim, secondary_y=False, title_text=f"Sensor {gas} (ppm)")
+    fig.update_yaxes(range=y2lim, secondary_y=True, title_text=f"Normalised {gas} (ppm)")
+    fig.add_scatter(x=df["timestamp"], y=df[gas], opacity=0.3, name="Raw Data")
+    fig.add_scatter(
+        x=df["timestamp"], y=df[f"{gas}_fit"], mode="lines", name="Fitted Background", line=dict(dash="dash")
+    )
+    fig.add_scatter(
+        x=df["timestamp"], y=df[f"{gas}_normalised"], yaxis="y2", name="Normalised Data", mode="lines", opacity=0.5
+    )
+    fig.add_scatter(
+        x=df["timestamp"],
+        y=np.where(df[f"{gas}_signal"], df[f"{gas}_normalised"], np.nan),
+        yaxis="y2",
+        name="Classed as signal",
+        mode="lines",
+        opacity=0.5,
+        # color
+        # mode="markers",
+        # marker=dict(size=3),
+    )
+
+    return fig
+
+
+def windrose_process(df: pd.DataFrame):
+    beaufort = {
+        "0": [0, 1],
+        "1": [1, 2],
+        "2": [2, 4],
+        "3": [4, 6],
+        "4": [6, 9],
+        "5": [9, 11],
+        "6": [11, 14],
+        "7": [14, 17],
+        "8": [17, 21],
+        "9": [21, 25],
+        "10": [25, 29],
+        "11": [29, 33],
+        "12": [33, 200],
+    }
+
+    beaufort_ms = {
+        "0": "0-1",
+        "1": "1-2",
+        "2": "2-4",
+        "3": "4-6",
+        "4": "6-9",
+        "5": "9-11",
+        "6": "11-14",
+        "7": "14-17",
+        "8": "17-21",
+        "9": "21-25",
+        "10": "25-29",
+        "11": "29-33",
+        "12": "33+",
+    }
+
+    cardinals = {
+        "N1": [0, 11.25],
+        "NNE": [11.25, 33.75],
+        "NE": [33.75, 56.25],
+        "ENE": [56.25, 78.75],
+        "E": [78.75, 101.25],
+        "ESE": [101.25, 123.75],
+        "SE": [123.75, 146.25],
+        "SSE": [146.25, 168.75],
+        "S": [168.75, 191.25],
+        "SSW": [191.25, 213.75],
+        "SW": [213.75, 236.25],
+        "WSW": [236.25, 258.75],
+        "W": [258.75, 281.25],
+        "WNW": [281.25, 303.75],
+        "NW": [303.75, 326.25],
+        "NNW": [326.25, 348.75],
+        "N2": [348.75, 360],
+    }
+    df["wind_direction_bin"] = pd.cut(
+        df["winddir"],
+        bins=[lower for lower, upper in cardinals.values()] + [list(cardinals.values())[-1][1]],
+        labels=[key for key in cardinals],
+        right=False,
+    )
+
+    df["wind_direction_bin"] = (
+        df["wind_direction_bin"].map(lambda x: "N" if x in ["N1", "N2"] else x).astype("category")
+    )
+    df["beaufort"] = pd.cut(
+        df["windspeed"],
+        bins=[lower for lower, upper in beaufort.values()] + [list(beaufort.values())[-1][1]],
+        labels=[key for key in beaufort],
+        right=False,
+    )
+    df["beaufort_ms"] = df["beaufort"].map(beaufort_ms)
+    df_windrose = df.groupby(["wind_direction_bin", "beaufort"], observed=False).size().reset_index(name="count")  # type: ignore
+    df_windrose["frequency"] = df_windrose["count"] / df_windrose["count"].sum() * 100
+    df_windrose["wind_direction_bin_degs"] = df_windrose["wind_direction_bin"].cat.rename_categories(
+        {
+            "N": 0,
+            "NNE": 22.5,
+            "NE": 45,
+            "ENE": 67.5,
+            "E": 90,
+            "ESE": 112.5,
+            "SE": 135,
+            "SSE": 157.5,
+            "S": 180,
+            "SSW": 202.5,
+            "SW": 225,
+            "WSW": 247.5,
+            "W": 270,
+            "WNW": 292.5,
+            "NW": 315,
+            "NNW": 337.5,
+        },
+    )
+    df_windrose["beaufort"] = df_windrose["beaufort"].astype(int)
+    return df_windrose
+
+
+def windrose_graph(df, plot_transect=False, theta1=None, theta2=None):
+    n_colors = 13
+    colors = px.colors.sample_colorscale("turbo", [n / (n_colors - 1) for n in range(n_colors)])
+    fig = px.bar_polar(
+        df,
+        r="frequency",
+        theta="wind_direction_bin_degs",
+        color="beaufort",
+        labels={
+            "frequency": "Frequency (%)",
+            "wind_direction_bin": "Direction",
+            "beaufort": "Beaufort Scale",
+        },
+        color_discrete_map=colors,
+    )
+    fig.update_layout(polar=dict(radialaxis={"visible": False, "showticklabels": False}))
+    fig.update_layout(
+        polar=dict(
+            angularaxis={
+                "showgrid": False,
+            },
+        ),
+    )
+
+    fig.update_layout(polar_bargap=0)
+    if plot_transect:
+        max_freq = df.groupby("wind_direction_bin", observed=False)["frequency"].sum().max()
+        fig.add_trace(
+            go.Scatterpolar(
+                r=[max_freq, max_freq],
+                theta=[theta1, theta2],
+                mode="lines",
+                line=dict(color="black", width=2, dash="dash"),
+                showlegend=False,
+            ),
+        )
+    return fig
+
+
+def windrose(df: pd.DataFrame, plot_transect=False):
+    df_windrose = windrose_process(df)
+    if plot_transect:
+        theta1, theta2 = processing.bimodal_azimuth(df)
+        fig = windrose_graph(df_windrose, plot_transect=plot_transect, theta1=theta1, theta2=theta2)
+    else:
+        fig = windrose_graph(df_windrose, plot_transect=plot_transect)
+    return fig
+
+
+def outliers(original_data: pd.Series, fence_high: float, fence_low: float):
+    outliers = np.array(original_data > fence_high) | (original_data < fence_low)
+
+    fig = make_subplots(rows=1, cols=2, shared_yaxes=True)
+    fig.add_trace(px.strip(original_data, color=outliers).data[0], row=1, col=1)
+    if sum(outliers) > 0:
+        fig.add_trace(px.strip(original_data, color=outliers).data[1], row=1, col=1)
+        fig.add_shape(
+            go.layout.Shape(
+                type="line",
+                x0=-0.5,
+                y0=fence_high,
+                x1=0.5,
+                y1=fence_high,
+                line=dict(color="red", width=2),
+            ),
+            row=1,
+            col=1,
+        )
+        fig.add_shape(
+            go.layout.Shape(
+                type="line",
+                x0=-0.5,
+                y0=fence_low,
+                x1=0.5,
+                y1=fence_low,
+                line=dict(color="red", width=2),
+            ),
+            row=1,
+            col=1,
+        )
+    fig.update_traces(offsetgroup=0)
+
+    fig.add_trace(px.scatter(original_data, color=outliers).data[0], row=1, col=2)
+    if sum(outliers) > 0:
+        fig.add_trace(px.scatter(original_data, color=outliers).data[1], row=1, col=2)
+    fig.update_layout(showlegend=False, yaxis_title="Windspeed (ms⁻¹)")
+
+    return fig
+
+
+def contour_krig(
+    df: pd.DataFrame,
+    gas: str,
+    # array of float 64
+    xx: np.ndarray,
+    yy: np.ndarray,
+    field: np.ndarray,
+    cut_ground: bool = False,
+    x: str = "x",
+    y: str = "height_ato",
+) -> go.Figure:
+    if np.isnan(field).all():
+        return blank_figure()
+    fig = go.Figure()
+    fig.add_trace(
+        go.Scatter(
+            x=df[x],
+            y=df[y],
+            mode="markers",
+            marker={
+                "color": df[f"{gas}_normalised"],
+                "colorscale": styling["colorscale"],
+                "showscale": True,
+                "colorbar": {
+                    "title": f"{gas} (ppm)",
+                },
+            },
+            showlegend=False,
+        )
+    )
+    fig.add_trace(
+        go.Contour(
+            z=field.T,
+            x=xx[:, 0],
+            y=yy[0, :],
+            contours={
+                "start": field.min(),
+                "end": field.max(),
+                "size": (field[~np.isnan(field)].max() - field[~np.isnan(field)].min()) / 21,
+            },
+            colorscale=styling["colorscale"],
+            opacity=0.5,
+            showlegend=False,
+            showscale=False,
+        )
+    )
+    fig.update_xaxes(
+        showline=True,
+        linewidth=1,
+        linecolor="black",
+        title_text="horizontal distance on projected flux plane (m)",
+        range=[np.min(xx), np.max(xx)],
+        ticks="outside",
+        tickwidth=1,
+        tickcolor="black",
+        ticklen=5,
+        nticks=20,
+    )
+    fig.update_yaxes(
+        showline=True,
+        linewidth=1,
+        linecolor="black",
+        title_text="height above takeoff (m)",
+        range=[np.min(yy), np.max(yy)],
+        ticks="outside",
+        tickwidth=1,
+        tickcolor="black",
+        ticklen=5,
+        nticks=10,
+    )
+    if cut_ground:
+        resolution = 200  # how many points to interpolate over
+        df["ground_elevation_ato"] = df.loc[:, "height_ato"] - df.loc[:, "height_agl"]
+        df_sorted = df.dropna(subset=[x, "ground_elevation_ato"]).sort_values(x)
+        x_min, x_max = df_sorted[x].min(), df_sorted[x].max()
+        x_interp = np.linspace(x_min, x_max, resolution)
+        ground_ato_interp = np.interp(x_interp, df_sorted[x], df_sorted["ground_elevation_ato"])
+        fig.add_trace(
+            go.Scatter(
+                x=x_interp,
+                y=ground_ato_interp,
+                mode="lines",
+                line=dict(color="black", width=2, dash="dash"),
+                name="Interpolated Ground Level",
+            )
+        )
+    fig.layout.coloraxis.colorbar.title = "Emissions flux (kg⋅m⁻²⋅h⁻¹)"
+
+    return fig
+
+
+def heatmap_krig(xx: np.ndarray, yy: np.ndarray, field: np.ndarray):
+    fig = px.imshow(field.T, x=xx[:, 0], y=yy[0, :], color_continuous_scale=styling["colorscale"], origin="lower")
+    fig.layout.coloraxis.colorbar.title = "Emissions flux (kg⋅m⁻²⋅h⁻¹)"
+    fig.update_xaxes(
+        showline=True,
+        linewidth=1,
+        linecolor="black",
+        title_text="horizontal distance on cylindrical projected flux plane (m)",
+        range=[xx.min(), xx.max()],
+        ticks="outside",
+        tickwidth=1,
+        tickcolor="black",
+        ticklen=5,
+        nticks=20,
+    )
+    fig.update_yaxes(
+        showline=True,
+        linewidth=1,
+        linecolor="black",
+        title_text="height above ground level (m)",
+        range=[yy.min(), yy.max()],
+        ticks="outside",
+        tickwidth=1,
+        tickcolor="black",
+        ticklen=5,
+        nticks=10,
+    )
+    fig.update_layout(coloraxis_colorbar=dict(len=0.25))
+    return fig
+
+
+def create_kml_file(data: pd.DataFrame, output_file: str, column: str, altitudemode: str):
+    kml = simplekml.Kml()
+
+    min_value = data[column].min()
+    max_value = data[column].max()
+
+    custom_colors = [
+        "#008080",
+        "#70a494",
+        "#b4c8a8",
+        "#f6edbd",
+        "#edbb8a",
+        "#de8a5a",
+        "#ca562c",
+    ]  # based on plotly geyser
+    cmap = mcolors.LinearSegmentedColormap.from_list("custom_cmap", custom_colors)
+
+    for _index, row in data.iterrows():
+        col_normalized = (row[column] - min) / (max_value - min_value)
+        color = mcolors.rgb2hex(cmap(col_normalized))
+
+        pnt = kml.newpoint(coords=[(row["longitude"], row["latitude"], row["height_ato"])], altitudemode=altitudemode)
+        pnt.iconstyle.icon.href = "http://maps.google.com/mapfiles/kml/shapes/placemark_circle.png"
+        pnt.iconstyle.color = simplekml.Color.rgb(int(color[1:3], 16), int(color[3:5], 16), int(color[5:], 16))
+        pnt.iconstyle.scale = 0.6
+        pnt.description = f"Concentration: {row[column]} ppm"
+
+    kml.save(output_file)
diff --git a/src/gasflux/pre_processing.py b/src/gasflux/pre_processing.py
new file mode 100644
index 0000000..836473b
--- /dev/null
+++ b/src/gasflux/pre_processing.py
@@ -0,0 +1,109 @@
+"""Functions that organise the data into standard columns in pandas dataframes. Conversion functions (e.g. WGS84 to UTM)
+are here but transformations take place in processing.py"""
+
+import geopandas as gpd
+import numpy as np
+import pandas as pd
+
+from . import plotting
+from .processing import circ_median
+
+
+def data_tests(df: pd.DataFrame):
+    assert df["ch4"].min() > 1.6, "ch4 values are too low"
+    assert df.index.is_monotonic_increasing, "data is not sorted by time"
+    assert df.index.is_unique, "data has duplicate timestamps"
+    assert df["ch4"].isna().sum() == 0, "ch4 has missing values"
+    assert df["windspeed"].min() >= 0, "windspeed values are negative"
+    assert df["windspeed"].max() < 20, "windspeed values are too high"
+    if df["windspeed"].max() > 15:
+        print("Warning: windspeed is greater than 15 m/s, perhaps due to errors in the data.")
+
+
+# make timestamp column from UTCs, Month, Day, Year
+def timestamp_from_four_columns(df):
+    df["Year"] = df["Year"] + 2000
+    df["time"] = pd.to_datetime(df["UTCs"], unit="s")
+    df["date"] = pd.to_datetime(df[["Year", "Month", "Day"]])
+    df["timestamp"] = pd.to_datetime(df["date"].dt.date.astype(str) + " " + df["time"].dt.time.astype(str))
+    df.index = df["timestamp"]
+    df.drop(
+        ["Year", "Month", "Day", "time", "date", "timestamp", "UTCs"],
+        axis=1,
+        inplace=True,
+    )
+    return df
+
+
+# add UTM from latitudes and longitudes
+def add_utm(df: pd.DataFrame) -> pd.DataFrame:
+    gdf = gpd.GeoDataFrame(  # type: ignore
+        df,
+        geometry=gpd.points_from_xy(df["longitude"], df["latitude"], crs="EPSG:4326"),
+    )
+    utm = gdf.estimate_utm_crs()
+    gdf = gdf.to_crs(utm)
+    if not isinstance(gdf, gpd.GeoDataFrame):
+        raise TypeError("Failed to reproject to a GeoDataFrame")
+    gdf["utm_easting"] = gdf.geometry.x
+    gdf["utm_northing"] = gdf.geometry.y
+    output_df = pd.DataFrame(gdf.drop(columns="geometry"))
+
+    return output_df
+
+
+# add columns for drone course azimuth and elevation
+def add_course(df, rolling_window=1):
+    df["hor_distance"] = np.sqrt((df["utm_northing"].diff()) ** 2 + (df["utm_easting"].diff()) ** 2)
+    df["vert_distance"] = df["height_ato"].diff()
+    df["vert_distance"] = pd.to_numeric(df["vert_distance"], errors="coerce")
+    df["hor_distance"] = pd.to_numeric(df["hor_distance"], errors="coerce")
+    df["course_azimuth"] = (
+        (np.degrees(np.arctan2(df["utm_easting"].diff(), df["utm_northing"].diff())) % 360)
+        .rolling(rolling_window)
+        .apply(lambda x: circ_median(x), raw=True)
+    )
+    df["course_elevation"] = (
+        np.degrees(np.arctan2(df["vert_distance"], df["hor_distance"]))
+        .rolling(rolling_window)
+        .apply(lambda x: circ_median(x), raw=True)
+    )
+    return df
+
+
+def manual_filtering(dict_dfs: dict, split_times: dict, mask_spans: dict) -> dict:
+    filtered_dfs = {}
+    for name, df in dict_dfs.items():
+        if name in mask_spans:
+            for i in range(len(mask_spans[name])):
+                df = df.drop(
+                    df.between_time(mask_spans[name][i].split(" - ")[0], mask_spans[name][i].split(" - ")[1]).index,
+                ).copy()
+                filtered_dfs[name] = df.copy()
+        if name in split_times:
+            split_times[name].append("23:59:59")
+            split_times[name].insert(0, "00:00:00")
+            for i in range(len(split_times[name]) - 1):
+                df2 = df.between_time(split_times[name][i], split_times[name][i + 1]).copy()
+                filtered_dfs[name + "_" + str(i)] = df2.copy()
+        elif name not in split_times:
+            filtered_dfs[name] = df.copy()
+    return filtered_dfs
+
+
+def remove_outliers(df: pd.DataFrame, column: str, name: str):
+    q1 = df[column].quantile(0.25)
+    q3 = df[column].quantile(0.75)
+    iqr = q3 - q1
+    fence_low = q1 - 3 * iqr
+    fence_high = q3 + 3 * iqr
+    fig = plotting.outliers(df[column], fence_high, fence_low)
+    outliers = df.loc[(df[column] < fence_low) | (df[column] > fence_high)]
+    if len(outliers) > 0:
+        print(f"{len(outliers)} outliers removed from {name} {column} data")
+        # nan for outliers, not row removal
+        df.loc[(df[column] < fence_low) | (df[column] > fence_high), column] = float("nan")
+    elif len(outliers) == 0:
+        print(f"No outliers found in {name} {column} data")
+
+    return df, fig
diff --git a/src/gasflux/processing.py b/src/gasflux/processing.py
new file mode 100644
index 0000000..fb40de0
--- /dev/null
+++ b/src/gasflux/processing.py
@@ -0,0 +1,650 @@
+"""Processing function, usually implying some kind of filtering or data transformation."""
+
+from matplotlib.figure import Figure
+from itertools import groupby
+from typing import Any
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from scipy import odr
+from scipy.optimize import least_squares
+from scipy.signal import find_peaks
+from scipy.stats import circmean
+import warnings
+import logging
+
+
+def circ_median(x: np.ndarray | pd.Series) -> float:
+    """
+    Finds the largest "empty" arc in the circle, splits it there and returns the median of the straight line.
+    Probably there are more elegant ways... see https://github.com/scipy/scipy/issues/6644
+
+    Parameters:
+        x (np.ndarray | pd.Series): The input array of angles.
+
+    Returns:
+        float: The circular median of the input angles.
+    """
+    angles = sorted(x)
+    n = len(angles)
+    if n == 1:
+        return angles[0]
+    gaps = [(angles[(i + 1) % n] - angles[i]) % 360 for i in range(n)]
+    largest_gap_index = gaps.index(max(gaps))
+    rearranged_angles = angles[largest_gap_index + 1 :] + angles[: largest_gap_index + 1]
+    if n % 2 == 1:
+        return rearranged_angles[n // 2]
+    else:
+        return (rearranged_angles[n // 2 - 1] + rearranged_angles[n // 2]) % 360 / 2
+
+
+def min_angular_displacement(x: float | np.ndarray, y: float | np.ndarray) -> float | np.ndarray:
+    """
+    Calculates the minimum circular difference between two angles (in 360 degree space)
+    """
+    return np.minimum(np.abs(x - y) % 360, (360 - np.abs(x - y)) % 360)
+
+
+def wind_offset_correction(df: pd.DataFrame, plane_angle: float) -> pd.DataFrame:
+    """
+    Corrects wind direction data for a given plane angle, assuming the plane is the primary orientation of the dataset.
+    This is useful for aligning wind data with the plane's orientation, facilitating analysis of wind effects.
+
+    Parameters:
+        df (pd.DataFrame): The input dataframe containing wind direction data.
+        wind_dir_col (str): Column name for wind direction data.
+        plane_angle (float): Angle of the plane in degrees.
+
+    Returns:
+        pd.DataFrame: The modified dataframe with corrected wind direction data.
+    """
+    df = df.copy()
+    df["winddir_rel"] = df.apply(lambda row: abs(90 - min_angular_displacement(row["winddir"], plane_angle)), axis=1)  # type: ignore
+    df["windspeed_measured"] = df["windspeed"]
+    df["windspeed"] = df["windspeed"] * np.cos(np.radians(df["winddir_rel"]))
+    return df
+
+
+def bimodal_azimuth(
+    df: pd.DataFrame, course_col: str = "course_azimuth", min_height: int = 5, min_diff: int = 160
+) -> tuple[float, float]:
+    """
+    Identifies the two most frequent course azimuths in the dataset, ensuring they are sufficiently
+    distinct. Filters data by height and removes NaNs before analysis.
+
+    Parameters:
+        df (pd.DataFrame): The input dataframe.
+        course_col (str): Column name for course azimuth data. Default is "course_azimuth".
+        min_altitude (int): Minimum height (_ato typically) for data to be included. Default is 5.
+        min_diff (int): Minimum difference between the two modes. Default is 160.
+
+    Returns:
+        tuple: Two modes of the course azimuth.
+    """
+    df = df[df["height_ato"] >= min_height]
+    data = df[course_col].dropna().to_numpy()
+    hist, edges = np.histogram(data, bins=50)
+    edgedist = edges[1] - edges[0]
+    bin_centers = edges[:-1] + edgedist / 2
+    max_freq_idx = np.argsort(hist)[-2:]  #  top 2 frequencies
+    mode1, mode2 = bin_centers[max_freq_idx]
+    while min_angular_displacement(mode1, mode2) < min_diff:
+        if hist[max_freq_idx[0]] < hist[max_freq_idx[1]]:
+            hist[max_freq_idx[0]] = 0
+            max_freq_idx = np.argsort(hist)[-2:]
+        else:
+            hist[max_freq_idx[1]] = 0
+            max_freq_idx = np.argsort(hist)[-2:]
+        mode1, mode2 = bin_centers[max_freq_idx]
+    if min_angular_displacement(mode1, mode2) < 160:
+        warnings.warn(
+            f"Two modes are close together - this probably should never happen: {mode1:.2f} and {mode2:.2f}",
+            UserWarning,
+            stacklevel=2,
+        )
+    return (mode1, mode2)
+
+
+# this returns modes of slope from -90 to 90 degrees.
+def bimodal_elevation(
+    df: pd.DataFrame, course_col: str = "course_elevation", min_height: float = 5, max_slope: float = 70
+) -> tuple[float, float]:
+    """
+    Identifies the most frequent course elevation in the dataset, adjusted for vertical movements.
+    Filters data by height and removes NaNs before analysis.
+
+    Parameters:
+        df (pd.DataFrame): The input dataframe.
+        course_col (str): Column name for course elevation data. Default is "course_elevation".
+        min_height (int): Minimum height for data to be included. Default is 5.
+        max_slope (int): Maximum slope to consider, avoTupleiding vertical movements. Default is 70.
+
+    Returns:
+        tuple: Mode of course elevation and its negative, representing possible ascent/descent angles.
+    """
+    df = df[df["height_ato"] >= df["height_ato"].min() + min_height]
+    data = df[course_col].to_numpy()
+    data = np.abs(data[~np.isnan(data)])
+    # to get around the edge case where vertical movements are modal
+    data = data[data < max_slope]
+    hist, edges = np.histogram(data, bins=50)
+    max_freq_idx = np.argsort(hist)[::-1][:2]
+    mode = edges[max_freq_idx][0]
+    return (mode, -mode)
+
+
+def height_transect_splitter(df: pd.DataFrame, height_col: str = "height_ato") -> tuple[pd.DataFrame, Figure]:
+    """
+    Splits the dataset into height-based transects and plots histogram peaks to identify prominent
+    height ranges. Only works if the flights are flat.
+
+    Parameters:
+        df (pd.DataFrame): The input dataframe containing height data.
+
+    Returns:
+        tuple: Modified dataframe with transect labels and a figure showing the histogram with peaks.
+    """
+    df = df.copy()
+    heights = df[height_col].to_numpy()
+    counts, bin_edges = np.histogram(heights, bins=40)
+    bin_width = bin_edges[1] - bin_edges[0]
+    bin_edges = np.append(heights.min() - bin_width, bin_edges)  # avoid literal edge effects
+    bin_edges = np.append(bin_edges, heights.max() + bin_width)
+    counts = np.append(0, counts)
+    counts = np.append(counts, 0)
+    bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
+    peaks, properties = find_peaks(counts)
+    transect_edges = (bin_centers[peaks][:-1] + bin_centers[peaks][1:]) / 2
+    transect_edges = np.append(heights.min(), transect_edges)
+    transect_edges = np.append(transect_edges, heights.max())
+    fig, ax = plt.subplots()
+    ax.stairs(edges=bin_edges, values=counts, fill=True)
+    ax.plot(bin_centers[peaks], counts[peaks], "x", color="red")
+    ax.vlines(transect_edges, ymin=0, ymax=max(counts), color="red")
+    df["transect_num"] = pd.cut(df[height_col], bins=list(transect_edges), labels=False, include_lowest=True)  # type: ignore
+    return df, fig
+
+
+def add_transect_azimuth_switches(df: pd.DataFrame, threshold=150, shift=3) -> pd.DataFrame:
+    """
+    Identifies transects based on significant changes in course azimuth, incrementing a transect
+    counter to distinguish different flight paths. This is a really crude function and should probably
+    be only used with data that's already been filtered in some way.
+
+    Parameters:
+        df (pd.DataFrame): The input dataframe with course azimuths.
+
+    Returns:
+        pd.DataFrame: The modified dataframe with a new 'transect' column indicating transect IDs.
+    """
+    df = df.copy()
+    df["transect_num"] = 0
+    df["prev_course_azimuth"] = df["course_azimuth"].shift(shift)  # this gives better behaviour for very neat transects
+    df["deg_displace"] = df.apply(
+        lambda row: min_angular_displacement(row["course_azimuth"], row["prev_course_azimuth"])  # type: ignore
+        if not pd.isnull(row["prev_course_azimuth"])
+        else np.nan,
+        axis=1,
+    )
+    df.loc[df["deg_displace"] > threshold, "transect_num"] = 1
+    # remove ones that are next to each other
+    for i in range(1, len(df)):
+        if (df.loc[i, "transect_num"] == 1 and df.loc[i - 1, "transect_num"] == 1) or (
+            df.loc[i, "transect_num"] == 1 and df.loc[i - 2, "transect_num"] == 1
+        ):
+            df.loc[i, "transect_num"] = 0
+    df["transect_num"] = df["transect_num"].shift(-shift)  # recorrect for shift
+    df["transect_num"] = df["transect_num"].cumsum() + 1  # 1 indexed
+    df["transect_num"] = df["transect_num"].ffill()
+    df = df.drop(columns=["prev_course_azimuth", "deg_displace"])
+    return df
+
+
+def course_filter(
+    df: pd.DataFrame, azimuth_filter: float, azimuth_window: int, elevation_filter: float
+) -> tuple[pd.DataFrame, pd.DataFrame]:
+    """
+    Filters data based on specified course azimuth and elevation, aiming to isolate transects that
+    align with main flight directions. First elevation is filtered to remove significant climbs or descents
+    (beware terrain-following flights). Bimodal course azimuths are calculated and used to filter the
+    data based on the main flight directions, with a rolling median applied (the window) to smooth the data.
+
+    Parameters:
+        df (pd.DataFrame): The input dataframe.
+        azimuth_filter (float): The tolerance for deviation from the main course azimuths.
+        azimuth_window (int): The window size for rolling median calculation of course azimuths.
+        elevation_filter (float): The tolerance for deviation from horizontal flight.
+
+    Returns:
+        tuple: The filtered dataframe and the original unfiltered dataframe for comparison.
+    """
+    df_filtered = df.copy()
+    df_filtered = df_filtered[abs(df["course_elevation"]) < elevation_filter]
+
+    azi1, azi2 = bimodal_azimuth(df_filtered)
+    logging.info(f"Drone appears to be flying mainly on the courses {azi1:.2f} degrees and {azi2:.2f} degrees")
+
+    df_filtered["rolling_course_azimuth"] = (
+        df_filtered["course_azimuth"].rolling(azimuth_window, center=True).apply(lambda x: circ_median(x), raw=True)
+    )
+
+    df_filtered = df_filtered[
+        (df_filtered["rolling_course_azimuth"] < azi1 + azimuth_filter)
+        & (df_filtered["rolling_course_azimuth"] > azi1 - azimuth_filter)
+        | (df_filtered["rolling_course_azimuth"] < azi2 + azimuth_filter)
+        & (df_filtered["rolling_course_azimuth"] > azi2 - azimuth_filter)
+    ]
+
+    return df_filtered, df
+
+
+def mCount_max(data_dict: dict[int, float]) -> tuple[int, int]:
+    """
+    Finds the start and end of the longest monotonic sequence in a dictionary of floats, typically used
+    to identify a series of continuous altitude measurements. The first and last are retained.
+
+    Parameters:
+    data_dict (Dict[int, float]): Dictionary with sequential numeric keys and numeric values representing measures
+    such as altitude.
+
+    Returns:
+    tuple: Start and end indices of the longest monotonic sequence in the dictionary.
+    """
+    if len(data_dict) < 2:
+        raise ValueError("Dictionary must contain at least two values")
+    if list(data_dict.keys()) != list(range(1, len(data_dict) + 1)):
+        raise ValueError("Keys must be sequential integers starting from 1")
+    poscount = 0
+    negcount = 0
+    max_pos_count = 0
+    max_pos_transect = 0
+    max_neg_count = 0
+    max_neg_transect = 0
+    pos_start = 0
+    neg_start = 0
+
+    for i in range(2, len(data_dict) + 1):
+        if data_dict[i] >= data_dict[i - 1]:
+            poscount += 1
+            negcount = 0
+        elif data_dict[i] < data_dict[i - 1]:
+            negcount += 1
+            poscount = 0
+
+        if max_pos_count < poscount:
+            max_pos_count = poscount
+            max_pos_transect = i
+            pos_start = i - poscount
+        elif max_neg_count < negcount:
+            max_neg_count = negcount
+            max_neg_transect = i
+            neg_start = i - negcount
+
+    if max_pos_count > 0 or max_neg_count > 0:
+        if max_pos_count >= max_neg_count:
+            return pos_start, max_pos_transect
+        else:
+            return neg_start, max_neg_transect
+    else:
+        return 0, 0
+
+
+def largest_monotonic_transect_series(
+    df: pd.DataFrame, transect_col: str = "transect_num", alt_col: str = "height_ato"
+) -> tuple[pd.DataFrame, int, int]:
+    """
+    Filters the input dataframe to include only the largest continuous series of transects based on
+    monotonic altitude changes.
+
+    Parameters:
+        df (pd.DataFrame): The input dataframe with transect and altitude information.
+        transect_col (str): Column name for transect numbers. Default is "transect_num".
+        alt_col (str): Column name for altitude data. Default is "height_ato".
+
+    Returns:
+        tuple: The filtered dataframe, start transect, and end transect of the largest monotonic series.
+    """
+    df = add_transect_azimuth_switches(df)  # course switches
+    alt_dict = dict(df.groupby(transect_col)[alt_col].mean())
+    starttransect, endtransect = mCount_max(alt_dict)  # type: ignore
+    # filter to the biggest monotonic series of values
+    df = df[(df[transect_col] >= starttransect) & (df[transect_col] <= endtransect)]
+    logging.info(
+        f"Parsed a flight of {len(np.unique(df[transect_col]))} transects from {alt_dict[starttransect]:.0f}m"
+        f" to {alt_dict[endtransect]:.0f}m between {df['timestamp'].iloc[0]} and {df['timestamp'].iloc[-1]}",
+    )
+    return df, starttransect, endtransect
+
+
+def monotonic_transect_groups(
+    df: pd.DataFrame, transect_col: str = "transect_num", alt_col: str = "height_ato"
+) -> tuple[pd.DataFrame, dict[int, str]]:
+    """
+    Groups transects into a dict of monotonic transect sequences based on altitude, facilitating analysis of continuous
+    flight patterns. Current behaviour is to reuse end transects of previous sequences as the start of the next.
+
+    Parameters:
+        df (pd.DataFrame): The input dataframe with transect and altitude information.
+        transect_col (str): Column name for transect numbers. Default is "transect_num".
+        alt_col (str): Column name for altitude data. Default is "height_ato".
+
+    Returns:
+        tuple: The dataframe with a new 'group' column indicating the monotonic group ID, and a
+        dictionary mapping transects to group IDs.
+    """
+
+    df = add_transect_azimuth_switches(df)
+    alt_dict = dict(df.groupby(transect_col)[alt_col].mean())
+
+    group_dict = {}
+    previous_altitude = None
+    current_group = 1
+    previous_trend = None
+    first_transect_in_series = True
+
+    for transect, altitude in alt_dict.items():
+        if previous_altitude is None:
+            group_dict[transect] = f"Group_{current_group}"
+        else:
+            if altitude == previous_altitude:
+                raise ValueError("Error: altitude is the same as the previous transect")
+            elif altitude > previous_altitude:
+                current_trend = "ascending"
+            else:
+                current_trend = "descending"
+
+            if current_trend != previous_trend and previous_trend is not None and not first_transect_in_series:
+                current_group += 1  # Increment group counter
+                first_transect_in_series = True
+            if current_trend == previous_trend or previous_trend is None:
+                # handles edge case where someone flies up, flies down one transect and then up again (yes really)
+                first_transect_in_series = False
+            group_dict[transect] = f"Group_{current_group}"
+            previous_trend = current_trend
+        previous_altitude = altitude
+    df["group"] = df["transect_num"].map(group_dict)
+
+    return df, group_dict
+
+
+def remove_non_transects(
+    df: pd.DataFrame,
+    chain_length: int = 70,
+    azimuth_tolerance: int = 10,
+    elevation_tolerance: int = 40,
+    smoothing_window: int = 5,
+) -> tuple[pd.DataFrame, pd.DataFrame]:
+    """
+    Filters the dataframe to remove segments not matching the criteria for being considered as transects,
+    based on course azimuth and elevation and segment length.
+
+    Parameters:
+        df (pd.DataFrame): The input dataframe.
+        chain_length (int): Minimum number of consecutive points to be considered a transect. Default is 70.
+        azimuth_tolerance (int): Tolerance for deviation from modal course azimuths. Default is 10.
+        elevation_tolerance (int): Tolerance for deviation from modal course elevation. Default is 40.
+        smoothing_window (int): Window size for rolling median smoothing of courses. Default is 5. Rolling median
+            avoids single point errors.
+
+    Returns:
+        tuple: Dataframes of removed segments and retained segments that fit transect criteria.
+    """
+
+    def get_true_runs(mask):
+        enumerated_mask = list(enumerate(mask))  # add index numbers to mask
+        # group consecutive true/false values
+        groups = groupby(enumerated_mask, key=lambda x: x[1])
+        # retain groups of True values
+        true_runs = [list(group) for key, group in groups if key]
+        return true_runs
+
+    def split_runs_on_azimuth_inversion(df, runs, azimuth_inversion_threshold=120):
+        split_runs = []
+        for run in runs:
+            last_azimuth = df.iloc[run[0][0]]["smoothed_course_azimuth"]
+            current_run = [run[0]]
+            for point in run[1:]:
+                current_azimuth = df.iloc[point[0]]["smoothed_course_azimuth"]
+                if min_angular_displacement(current_azimuth, last_azimuth) > azimuth_inversion_threshold:
+                    split_runs.append(current_run)
+                    current_run = [point]
+                else:
+                    current_run.append(point)
+                last_azimuth = current_azimuth
+            split_runs.append(current_run)
+        return split_runs
+
+    # Create new columns for filtering reasons, initialized with False
+    df_removed = df.copy()
+    df_removed["filtered_by_azimuth"] = False
+    df_removed["filtered_by_elevation"] = False
+    df_removed["filtered_by_chain"] = False
+
+    # Apply azimuth filter
+    modal_course_azimuths = bimodal_azimuth(df, course_col="course_azimuth")
+    modal_elevation_azimuths = bimodal_elevation(df, course_col="course_elevation")
+
+    # Apply rolling median to course azimuth and elevation and store them in new columns
+    df_removed["smoothed_course_azimuth"] = (
+        df_removed["course_azimuth"]
+        .rolling(smoothing_window, center=True)
+        .apply(lambda x: circmean(x, 360, 0), raw=True)
+        .fillna(df_removed["course_azimuth"], inplace=False)
+    )
+
+    df_removed["smoothed_course_elevation"] = (
+        df_removed["course_elevation"]
+        .rolling(smoothing_window, center=True)
+        .apply(lambda x: circmean(x, 360, 0), raw=True)
+        .fillna(df_removed["course_elevation"], inplace=False)
+    )
+
+    azimuth_mask = df_removed["smoothed_course_azimuth"].apply(
+        lambda x: any([min_angular_displacement(x, mode) <= azimuth_tolerance for mode in modal_course_azimuths])
+    )
+
+    df_removed.loc[~azimuth_mask, "filtered_by_azimuth"] = True
+
+    # Apply elevation filter
+    elevation_mask = df_removed["smoothed_course_elevation"].apply(
+        lambda x: any([min_angular_displacement(x, mode) <= elevation_tolerance for mode in modal_elevation_azimuths])
+    )
+    df_removed.loc[~elevation_mask, "filtered_by_elevation"] = True
+
+    # Combined mask for azimuth and elevation
+    mask = azimuth_mask & elevation_mask
+
+    true_runs = get_true_runs(mask)
+    split_runs = split_runs_on_azimuth_inversion(df_removed, true_runs)
+    chain_mask = [len(run) >= chain_length for run in split_runs]
+    chain_filtered_runs = [run for run, filter_by_chain in zip(split_runs, chain_mask, strict=False) if filter_by_chain]
+    chain_filtered_indices = [point[0] for run in chain_filtered_runs for point in run]
+    df_removed.loc[df_removed.index.difference(chain_filtered_indices), "filtered_by_chain"] = True
+
+    filtered_segments = [df_removed.iloc[run[0][0] : run[-1][0] + 1] for run in chain_filtered_runs]
+
+    df_retained = pd.concat(filtered_segments)
+    df_removed = df_removed[~df_removed.index.isin(df_retained.index)]
+
+    return df_removed, df_retained
+
+
+def flatten_linear_plane(
+    df: pd.DataFrame, alt_col: str = "height_ato", distance_filter: float = 10000
+) -> tuple[pd.DataFrame, float]:
+    """
+    Transforms a 3D dataset into a linear plane, focusing on the largest contiguous dataset aligned with
+    the plane's primary orientation. The new x coordinate is the distance along the plane, y is the distance
+    perpendicular to the plane (useful only for deviation), and z is the altitude.
+
+    Parameters:
+        df (pd.DataFrame): Input dataframe with "utm_easting", "utm_northing", and "height_ato" columns.
+        alt_col (str): Column name for altitude data. Default is "height_ato".
+        distance_filter (float): Threshold for filtering points based on their distance from the regression line.
+
+    Returns:
+        tuple: Modified dataframe with new 'x', 'y', and 'z' columns representing transformed coordinates,
+        and one of the plane's angles of rotation (from N, 0-360) in degrees.
+    """
+
+    def orthogonal_distance_regression(df: pd.DataFrame) -> tuple[pd.DataFrame, np.ndarray[Any, Any]]:
+        """
+        Perform orthogonal distance regression on the given DataFrame.
+
+        Parameters:
+            df (pd.DataFrame): DataFrame containing "utm_easting" and "utm_northing" columns.
+
+        Returns:
+            tuple[pd.DataFrame, np.ndarra[Any,Any]]]: Updated DataFrame with "distance_from_fit" column
+            and the fitted parameters (slope, intercept).
+        """
+
+        def linear_reg_equation(B, x):
+            return B[0] * x + B[1]  # y = mx + c
+
+        required_columns = ["utm_easting", "utm_northing"]
+        if not all(col in df.columns for col in required_columns):
+            raise ValueError(f"DataFrame must contain columns: {required_columns}")
+        model = odr.Model(linear_reg_equation)
+        data = odr.Data(df["utm_easting"], df["utm_northing"])
+
+        INITIAL_BETA = [1, 0]  # Initial guess of slope=1, intercept=0
+        odr_instance = odr.ODR(data, model, beta0=INITIAL_BETA)
+        fit = odr_instance.run()
+
+        if fit.stopreason[0] == "Iteration limit reached":
+            raise RuntimeError("ODR fitting failed to converge")
+
+        slope, intercept = fit.beta
+        df = df.assign(
+            distance_from_fit=abs((slope * df["utm_easting"] - df["utm_northing"] + intercept) / np.sqrt(slope**2 + 1))
+        )
+
+        return df, fit.beta
+
+    df, coefs2D = orthogonal_distance_regression(df)
+    df = df.loc[df["distance_from_fit"] < distance_filter, :]
+    df, coefs2D = orthogonal_distance_regression(df)  # this is intentionally done twice
+    df = df.loc[df["distance_from_fit"] < distance_filter, :]
+    rotation = np.arctan(coefs2D[0])
+    df.loc[:, "x"] = (df["utm_easting"] - df["utm_easting"].min()) * np.cos(-rotation) - (
+        df["utm_northing"] - df["utm_northing"].min()
+    ) * np.sin(-rotation)
+    df.loc[:, "y"] = (df["utm_easting"] - df["utm_easting"].min()) * np.sin(-rotation) + (
+        df["utm_northing"] - df["utm_northing"].min()
+    ) * np.cos(-rotation)
+    df.loc[:, "z"] = df[alt_col]
+
+    plane_angle = (np.pi / 2) - np.arctan(coefs2D[0])
+    plane_angle = np.degrees(plane_angle)
+    return df, plane_angle
+
+
+## Functions for circular/spiral flights ##
+
+
+def circle_deviation(df: pd.DataFrame, x_col: str, y_col: str) -> tuple[pd.DataFrame, float, float, float]:
+    """
+    Calculates the deviation of points from a fitted circle and their azimuth angles.
+
+    Parameters:
+        df (pd.DataFrame): The input dataframe containing the data.
+        x (str): Column name for the x-coordinate.
+        y (str): Column name for the y-coordinate.
+
+    Returns:
+        Tuple[pd.Dataframe, float, float, float]: A tuple containing the modified dataframe with azimuth angles
+        and deviations,the radius of the fitted circle, and the coordinates of the circle's center.
+    """
+    df = df.copy()
+    required_columns = [x_col, y_col]
+    if not all(col in df.columns for col in required_columns):
+        raise ValueError(f"DataFrame must contain columns: {required_columns}")
+
+    x = df[x_col]
+    y = df[y_col]
+
+    def midhalf(x):  # select middle half of data to avoid edge effects
+        return x.iloc[int(len(x) * 1 / 4) : int(len(x) * 3 / 4)]
+
+    x_filter, y_filter = midhalf(x), midhalf(y)
+
+    def func(params):
+        xc, yc, r = params
+        return np.sqrt((x - xc) ** 2 + (y - yc) ** 2) - r
+
+    x_m = np.mean(np.array(x_filter))  # initial guess for parameters
+    y_m = np.mean(np.array(y_filter))
+    r_m = np.mean(np.sqrt((np.array(x_filter) - x_m) ** 2 + (np.array(y_filter) - y_m) ** 2))
+
+    params0 = np.array([x_m, y_m, r_m])
+    result = least_squares(func, params0)
+    xc, yc, r = result.x
+
+    deviation = np.sqrt((x - xc) ** 2 + (y - yc) ** 2) - r
+
+    # output azimuth in radians with 0 at north and increasing clockwise thanks to modulos
+    azimuth = np.degrees(np.arctan2(x - xc, y - yc) % (2 * np.pi))
+
+    df["circ_azimuth"] = azimuth
+    df["circ_deviation"] = deviation
+
+    return df, r, xc, yc
+
+
+def recentre_azimuth(df: pd.DataFrame, r: float, x: str = "circ_azimuth", y: str = "ch4_normalised") -> pd.DataFrame:
+    """
+    Recentres the azimuth angles based on the angle of maximum value and computes the distance along the
+    circumference of the circle.
+
+    Parameters:
+        df (pd.DataFrame): The input dataframe.
+        r (float): The radius of the circle.
+        x (str): Column name for azimuth angles. Default is 'circ_azimuth'.
+        y (str): Column name for the values used to find the maximum. Default is 'ch4_normalised'.
+
+    Returns:
+        pd.DataFrame: The modified dataframe with centered azimuth angles and distances along the circumference.
+    """
+    df = df.copy()
+
+    def azimuth_of_max(df: pd.DataFrame, x: str = "circ_azimuth", y: str = "ch4_normalised") -> float:
+        return df.loc[df[y].idxmax()][x]  # type: ignore
+
+    centre_azimuth = azimuth_of_max(df, x, y)
+    df["centred_azimuth"] = df[x] - centre_azimuth
+    df.loc[df["centred_azimuth"] > 180, "centred_azimuth"] -= 360
+    df.loc[df["centred_azimuth"] < -180, "centred_azimuth"] += 360
+    df["circumference_distance"] = r * np.radians(df["centred_azimuth"])
+    df["circumference_distance"] = df["circumference_distance"] - df["circumference_distance"].min()
+    return df
+
+
+def drone_anemo_to_point_wind(
+    df: pd.DataFrame, yaw_col: str, anemo_u_col: str, anemo_v_col: str, easting_col: str, northing_col: str
+) -> pd.DataFrame:
+    """
+    Convert anemometer wind data from drone's coordinate system to Earth's coordinate system
+    and calculate true wind speed and direction.
+
+    Parameters:
+    df (pd.DataFrame): Input DataFrame containing drone yaw, anemometer data, and drone speed.
+    yaw_col (str): Column name for drone's yaw (in degrees, range [-180, 180]).
+    anemo_u_col (str): Column name for anemometer U (wind speed in drone's X direction, from port to starboard).
+    anemo_v_col (str): Column name for anemometer V (wind speed in drone's Y direction, from aft to nose).
+    easting_col (str): Column name for drone's speed from west to east
+    northing_col (str): Column name for drone's speed from south to north
+
+    Returns:
+    pd.DataFrame: DataFrame with calculated true wind speed ("windspeed") and true wind direction ("winddir").
+    """
+    yaw_rad = np.deg2rad(df[yaw_col] % 360)
+    rotated_U = df[anemo_u_col] * np.cos(yaw_rad) + df[anemo_v_col] * np.sin(yaw_rad)
+    rotated_V = -df[anemo_u_col] * np.sin(yaw_rad) + df[anemo_v_col] * np.cos(yaw_rad)
+    true_U = -rotated_U - df[easting_col]
+    true_V = -rotated_V - df[northing_col]
+    df["windspeed"] = np.sqrt(true_U**2 + true_V**2)
+    df["winddir"] = np.degrees(np.arctan2(true_U, true_V)) % 360
+
+    return df
diff --git a/src/gasflux/processing_pipelines.py b/src/gasflux/processing_pipelines.py
new file mode 100644
index 0000000..f3abb1e
--- /dev/null
+++ b/src/gasflux/processing_pipelines.py
@@ -0,0 +1,317 @@
+import logging
+from datetime import datetime
+from pathlib import Path
+from scipy import stats
+
+import pandas as pd
+import plotly.graph_objects as go
+import yaml
+
+from src.gasflux import background,plotting,processing,reporting,interpolation,pre_processing,gas
+
+from abc import ABC, abstractmethod
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+def read_csv(file_path: Path) -> pd.DataFrame:
+    """Read a CSV file and return a DataFrame."""
+    try:
+        return pd.read_csv(file_path)
+    except FileNotFoundError:
+        logger.exception(f"File not found: {file_path}")
+        raise
+
+
+def load_config(config_path: Path) -> dict:
+    """Load a YAML config file and return a dictionary."""
+    try:
+        with open(config_path) as file:
+            return yaml.safe_load(file)
+    except FileNotFoundError:
+        logger.exception(f"Config file not found: {config_path}")
+        raise
+    except yaml.YAMLError as e:
+        logger.exception(f"Error parsing YAML config: {e}")
+        raise
+
+
+class DataValidator:  # TODO(me): decide whether to move this to preprocessing
+    """Validate the data before processing."""
+
+    def __init__(self, df: pd.DataFrame, config: dict) -> None:
+        """Initialise the validator with the DataFrame and config."""
+        self.df = df
+        self.required_cols = config["required_cols"].copy()
+        self.required_cols.update(config["gases"])
+
+    def validate(self) -> None:
+        """Validate the data."""
+        self._check_is_df()
+        self._check_cols()
+        self._check_dtypes()
+        self._check_ranges()
+        logger.info("Data validation passed")
+
+    def _check_is_df(self) -> None:
+        """Check that the input is a DataFrame."""
+        if not isinstance(self.df, pd.DataFrame):
+            logging.error("Input data is not a DataFrame.")
+            raise ValueError("Input data is not a DataFrame.")
+
+    def _check_cols(self) -> None:
+        """Check that the required columns are present."""
+        missing_cols = set(self.required_cols) - set(self.df.columns)
+        if missing_cols:
+            logging.error(f"Missing or mislabelled columns: {missing_cols}")
+            raise ValueError(f"Missing or mislabelled columns: {missing_cols}")
+
+    def _check_dtypes(self) -> None:
+        """Check that the required columns are of the correct type and do not contain NaN values."""
+        for col in self.required_cols:
+            if col in self.df:
+                if self.df[col].isna().any():
+                    logging.error(f"Column '{col}' contains NaN values.")
+                    raise ValueError(f"Column '{col}' contains NaN values.")
+                if self.df[col].dtype != "float64":
+                    logging.error(f"Column '{col}' is not of type 'float64'.")
+                    raise ValueError(f"Column '{col}' is not of type 'float64'.")
+
+    def _check_ranges(self) -> None:
+        """Check that the required columns are within the specified ranges."""
+        for col, (min_val, max_val) in self.required_cols.items():
+            if col in self.df.columns and not self.df[col].between(min_val, max_val, inclusive="both").all():
+                logging.error(f"Column '{col}' contains values out of range: {min_val} to {max_val}.")
+                raise ValueError(f"Column '{col}' contains values out of range: {min_val} to {max_val}.")
+
+
+class BackgroundStrategy(ABC):
+    def __init__(self, data_processor):
+        self.data_processor = data_processor
+
+    @abstractmethod
+    def process(self):
+        pass
+
+
+class AlgorithmicBaselineStrategy(BackgroundStrategy):
+    def process(self):
+        logger.info("Applying algorithmic background correction")
+        for gas in self.data_processor.gases:
+            (
+                self.data_processor.df,
+                self.data_processor.figs["background"][gas],
+                self.data_processor.text[f"background_{gas}"],
+            ) = background.algorithmic_baseline(
+                df=self.data_processor.df,
+                gas=gas,
+                algorithmic_baseline_settings=self.data_processor.config["algorithmic_baseline_settings"],
+            )
+        self.data_processor.df_std = self.data_processor.df.copy()
+
+
+class SensorStrategy(ABC):
+    def __init__(self, data_processor):
+        self.data_processor = data_processor
+
+    @abstractmethod
+    def process(self):
+        pass
+
+
+class InSituSensorStrategy(SensorStrategy):
+    def process(self):
+        logger.info("Processing in-situ (point) data")
+        for gas in self.data_processor.gases:
+            self.data_processor.figs["scatter_3d"][gas] = plotting.scatter_3d(
+                df=self.data_processor.df, color=gas, colorbar_title=f"{gas.upper()} flux (kg/m²/h)"
+            )
+        if SpatialProcessingStrategy == CurtainSpatialProcessingStrategy:
+            self.data_processor.figs["windrose"] = plotting.windrose(self.data_processor.df, plot_transect=True)
+        else:
+            self.data_processor.figs["windrose"] = plotting.windrose(self.data_processor.df)
+        self.data_processor.figs["wind_timeseries"] = plotting.time_series(
+            self.data_processor.df, ys=["windspeed", "winddir"]
+        )
+
+
+class SpatialProcessingStrategy(ABC):
+    def __init__(self, data_processor):
+        self.data_processor = data_processor
+
+    @abstractmethod
+    def process(self):
+        pass
+
+
+class CurtainSpatialProcessingStrategy(SpatialProcessingStrategy):
+    def process(self):
+        logger.info("Applying curtain spatial processing")
+        self.data_processor.dfs["original"] = self.data_processor.df.copy()
+        self.data_processor.df, self.data_processor.start_transect, self.data_processor.end_transect = (
+            processing.largest_monotonic_transect_series(self.data_processor.df)
+        )
+        self.data_processor.dfs["removed"] = self.data_processor.dfs["original"].loc[
+            self.data_processor.dfs["original"].index.difference(self.data_processor.df.index)
+        ]
+        self.data_processor.df, self.data_processor.plane_angle = processing.flatten_linear_plane(
+            self.data_processor.df
+        )
+        self.data_processor.df = processing.wind_offset_correction(
+            self.data_processor.df, self.data_processor.plane_angle
+        )
+        for gas_name in self.data_processor.gases:
+            #计算通量
+            self.data_processor.df = gas.gas_flux_column(self.data_processor.df, gas_name)
+            self.data_processor.figs["scatter_3d"][gas_name].add_trace(
+                go.Scatter3d(
+                    x=self.data_processor.dfs["removed"]["utm_easting"],
+                    y=self.data_processor.dfs["removed"]["utm_northing"],
+                    z=self.data_processor.dfs["removed"]["height_ato"],
+                    mode="markers",
+                    marker={"size": 2, "color": "black", "symbol": "circle", "opacity": 0.5},
+                )
+            )
+
+
+class SpiralSpatialProcessingStrategy(SpatialProcessingStrategy):
+    def process(self):
+        logger.info("Applying spiral spatial processing")
+        self.data_processor.dfs["original"] = self.data_processor.df.copy()
+        # self.data_processor.df, self.data_processor.start_transect, self.data_processor.end_transect = (
+        #     gasflux.processing.largest_monotonic_transect_series(self.data_processor.df)
+
+        # no wind offset correction - assume wind is perpendicular to the spiral
+        self.data_processor.dfs["removed"] = self.data_processor.dfs["original"].loc[
+            self.data_processor.dfs["original"].index.difference(self.data_processor.df.index)
+        ]
+        (
+            self.data_processor.df,
+            self.data_processor.circle_radius,
+            self.data_processor.circle_center_x,
+            self.data_processor.circle_center_y,
+        ) = processing.circle_deviation(self.data_processor.df, x_col="utm_easting", y_col="utm_northing")
+        self.data_processor.df = processing.recentre_azimuth(
+            self.data_processor.df, r=self.data_processor.circle_radius
+        )
+        self.data_processor.df["x"] = self.data_processor.df["circumference_distance"]
+        for gas_name in self.data_processor.gases:
+            self.data_processor.df = gas.gas_flux_column(self.data_processor.df, gas_name)
+            self.data_processor.figs["scatter_3d"][gas_name].add_trace(
+                go.Scatter3d(
+                    x=self.data_processor.dfs["removed"]["utm_easting"],
+                    y=self.data_processor.dfs["removed"]["utm_northing"],
+                    z=self.data_processor.dfs["removed"]["height_ato"],
+                    mode="markers",
+                    marker={"size": 2, "color": "black", "symbol": "circle", "opacity": 0.5},
+                )
+            )
+
+
+class InterpolationStrategy(ABC):
+    def __init__(self, data_processor):
+        self.data_processor = data_processor
+
+    @abstractmethod
+    def process(self):
+        pass
+
+
+class KrigingInterpolationStrategy(InterpolationStrategy):
+    def process(self):
+        logger.info("Applying kriging interpolation")
+        for gas in self.data_processor.gases:
+            (
+                self.data_processor.output_vars["krig_parameters"][gas],
+                self.data_processor.text[f"krig_output_{gas}"],
+                self.data_processor.figs["contour"][gas],
+                self.data_processor.figs["krig_grid"][gas],
+                self.data_processor.figs["semivariogram"][gas],
+            ) = interpolation.ordinary_kriging(
+                df=self.data_processor.df,
+                x="x",
+                y="height_ato",
+                gas=gas,
+                ordinary_kriging_settings=self.data_processor.config["ordinary_kriging_settings"],
+                **self.data_processor.config["semivariogram_settings"],
+            )
+            logger.info(f"Kriged {gas}")
+
+
+class DataProcessor:
+    def __init__(self, config: dict, df: pd.DataFrame):
+        self.config: dict = config
+        self.df: pd.DataFrame = df
+        self.gases: list[str] = list(config["gases"].keys())
+        self.processing_time = datetime.now()
+        self.figs: dict = {
+            "scatter_3d": {},
+            "windrose": None,
+            "wind_timeseries": None,
+            "background": {},
+            "contour": {},
+            "krig_grid": {},
+            "semivariogram": {},
+        }
+        self.text: dict = {}
+        self.output_vars: dict = {"krig_parameters": {}, "std": {}}
+        self.dfs: dict = {}
+        self.reports: dict = {}
+
+    def strategy_selection(self):
+        self.background_strategy: BackgroundStrategy
+        if self.config["strategies"]["background"] == "algorithm":
+            self.background_strategy = AlgorithmicBaselineStrategy(self)
+        self.sensor_strategy: SensorStrategy
+        if self.config["strategies"]["sensor"] == "insitu":
+            self.sensor_strategy = InSituSensorStrategy(self)
+        self.spatial_processing_strategy: SpatialProcessingStrategy
+        if self.config["strategies"]["spatial"] == "curtain":
+            self.spatial_processing_strategy = CurtainSpatialProcessingStrategy(self)
+        if self.config["strategies"]["spatial"] == "spiral":
+            self.spatial_processing_strategy = SpiralSpatialProcessingStrategy(self)
+        self.interpolation_strategy: InterpolationStrategy
+        if self.config["strategies"]["interpolation"] == "kriging":
+            self.interpolation_strategy = KrigingInterpolationStrategy(self)
+
+    def process(self):
+        self.df = pre_processing.add_utm(self.df)
+        self.df = pre_processing.add_course(self.df)
+        DataValidator(self.df, self.config).validate()
+        self.background_strategy.process()
+        self.sensor_strategy.process()
+        self.spatial_processing_strategy.process()
+        self.interpolation_strategy.process()
+
+        # Reporting
+        for gas in self.gases:
+            self.reports[gas] = reporting.mass_balance_report(
+                krig_params=self.output_vars["krig_parameters"][gas],
+                wind_fig=self.figs["wind_timeseries"],
+                background_fig=self.figs["background"][gas],
+                threed_fig=self.figs["scatter_3d"][gas],
+                krig_fig=self.figs["contour"][gas],
+                windrose_fig=self.figs["windrose"],
+            )
+
+        # Collecting descriptive variables
+        self.output_vars["std"]["windspeed"] = self.df["windspeed"].std()
+        self.output_vars["std"]["windddir"] = stats.circstd(self.df["winddir"], high=360)
+        for gas in self.gases:
+            self.output_vars["std"][f"{gas}_background"] = self.df.loc[
+                ~self.df[f"{gas}_signal"], f"{gas}_normalised"
+            ].std()
+
+
+def process_main(data_file: Path, config_file: Path) -> None:
+    """Main function to run the pipeline."""
+    config = load_config(config_file)
+    name = data_file.stem
+    df = read_csv(data_file)
+
+    processor = DataProcessor(config, df)
+    processor.strategy_selection()
+    processor.process()
+    reporting.generate_reports(name, processor, config)
+    logger.info("Processing complete")
diff --git a/src/gasflux/qiya.py b/src/gasflux/qiya.py
new file mode 100644
index 0000000..ba5fed1
--- /dev/null
+++ b/src/gasflux/qiya.py
@@ -0,0 +1,190 @@
+import requests
+import time
+from requests.adapters import HTTPAdapter
+from urllib3.util.retry import Retry
+
+def get_pressure_at_location(lat, lon, altitude, date, time, max_retries=3, timeout=30):
+    """
+    获取指定位置、时间、高度的气压
+
+    Args:
+        lat: 纬度
+        lon: 经度
+        altitude: 海拔高度 (米)
+        date: 日期 (格式: YYYY-MM-DD 或 YYYY/MM/DD)
+        time: 时间 (格式: HH:MM 或 HH:MM:SS)
+        max_retries: 最大重试次数
+        timeout: 请求超时时间(秒)
+
+    Returns:
+        float: 气压值 (hPa)，获取失败返回 None
+    """
+
+    # 标准化日期格式为 YYYY-MM-DD
+    def normalize_date(d):
+        """将各种日期格式标准化为 YYYY-MM-DD"""
+        if not d:
+            from datetime import datetime
+            return datetime.now().strftime("%Y-%m-%d")
+
+        # 处理斜杠分隔符
+        if "/" in d:
+            d = d.replace("/", "-")
+
+        parts = d.split("-")
+        if len(parts) == 3:
+            year = parts[0]
+            month = parts[1].zfill(2)  # 确保月份是两位数
+            day = parts[2].zfill(2)    # 确保日期是两位数
+            return f"{year}-{month}-{day}"
+        else:
+            # 如果格式不正确，返回今天的日期
+            from datetime import datetime
+            return datetime.now().strftime("%Y-%m-%d")
+
+    date = normalize_date(date)
+
+    # 标准化时间格式为 HH:MM
+    def normalize_time(t):
+        """将各种时间格式标准化为 HH:MM"""
+        if not t or ":" not in t:
+            return "12:00"  # 默认中午12点
+
+        parts = t.split(":")
+        if len(parts) >= 2:
+            hour = parts[0].zfill(2)  # 确保小时是两位数
+            minute = parts[1].zfill(2)  # 确保分钟是两位数
+            return f"{hour}:{minute}"
+        elif len(parts) == 1:
+            hour = parts[0].zfill(2)
+            return f"{hour}:00"
+        else:
+            return "12:00"
+
+    time = normalize_time(time)
+
+    url = "https://archive-api.open-meteo.com/v1/archive"
+
+    params = {
+        "latitude": lat,
+        "longitude": lon,
+        "start_date": date,  # 格式: YYYY-MM-DD
+        "end_date": date,
+        "hourly": ["pressure_msl", "surface_pressure"],
+        "timezone": "auto"
+    }
+
+    # 创建带有重试机制的会话
+    session = requests.Session()
+    retry_strategy = Retry(
+        total=max_retries,
+        status_forcelist=[429, 500, 502, 503, 504],
+        backoff_factor=1
+    )
+    adapter = HTTPAdapter(max_retries=retry_strategy)
+    session.mount("http://", adapter)
+    session.mount("https://", adapter)
+
+    try:
+        print(f"正在获取位置 ({lat:.6f}, {lon:.6f}) 在 {date} {time} 的气压数据...")
+        response = session.get(url, params=params, timeout=timeout)
+
+        # 检查响应状态
+        response.raise_for_status()
+
+        data = response.json()
+
+        # 检查API错误
+        if "error" in data:
+            print(f"API错误: {data['error']}")
+            return None
+
+        # 解析气压数据
+        if data and "hourly" in data:
+            times = data["hourly"]["time"]
+            pressures = data["hourly"]["surface_pressure"]  # 地表气压
+
+            if not times or not pressures:
+                print("未找到气压数据")
+                return None
+
+            # 根据时间找到对应气压
+            target_time = f"{date}T{time}"
+            if target_time in times:
+                idx = times.index(target_time)
+                pressure = pressures[idx]
+                print(f"成功获取气压: {pressure} hPa")
+                return pressure
+            else:
+                print(f"在数据中未找到时间: {target_time}")
+                print(f"可用时间范围: {times[0]} 到 {times[-1]}")
+                return None
+        else:
+            print("API响应中没有hourly数据")
+            return None
+
+    except requests.exceptions.ConnectionError as e:
+        print(f"网络连接错误: {e}")
+        print("请检查网络连接或稍后重试")
+        return None
+    except requests.exceptions.Timeout as e:
+        print(f"请求超时: {e}")
+        print(f"已重试 {max_retries} 次，请检查网络连接")
+        return None
+    except requests.exceptions.HTTPError as e:
+        print(f"HTTP错误: {e}")
+        return None
+    except ValueError as e:
+        print(f"数据解析错误: {e}")
+        return None
+    except Exception as e:
+        print(f"未知错误: {e}")
+        return None
+    finally:
+        session.close()
+
+def batch_get_pressure(data_list):
+    """
+    批量获取多个位置的气压数据
+
+    Args:
+        data_list: 包含 (lat, lon, altitude, date, time) 元组的列表
+
+    Returns:
+        list: 气压值列表
+    """
+    results = []
+    for i, (lat, lon, alt, date, time) in enumerate(data_list):
+        print(f"\n处理第 {i+1} 个位置...")
+        pressure = get_pressure_at_location(lat, lon, alt, date, time)
+        results.append(pressure)
+        if pressure is not None:
+            print(f"位置 {i+1}: {pressure} hPa")
+        else:
+            print(f"位置 {i+1}: 获取失败")
+
+        # 添加短暂延迟，避免请求过于频繁
+        if i < len(data_list) - 1:
+            time.sleep(0.5)
+
+    return results
+
+# 使用示例
+if __name__ == "__main__":
+    print("=== 气压数据获取工具 ===\n")
+
+    # 单个位置示例
+    print("1. 单个位置查询:")
+    pressure = get_pressure_at_location(
+        lat=40.3491370,        # 纽约纬度
+        lon=115.7855289,       # 纽约经度 (西经)
+        altitude=435.789,         # 海拔10米
+        date="2016-02-12",
+        time="08:00"        # HH:MM格式
+    )
+
+    if pressure is not None:
+        print(f"纽约当前气压: {pressure} hPa")
+    else:
+        print("获取纽约气压数据失败")
+
diff --git a/src/gasflux/reporting.py b/src/gasflux/reporting.py
new file mode 100644
index 0000000..4de5cee
--- /dev/null
+++ b/src/gasflux/reporting.py
@@ -0,0 +1,130 @@
+"""This module provides functions for generating mass balance reports."""
+
+from pathlib import Path
+
+import plotly.graph_objects as go
+from jinja2 import Template
+from plotly.io import to_html
+from datetime import datetime
+
+import yaml
+
+import logging
+from . import plotting
+
+
+import json
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+
+def mass_balance_report(
+    krig_params: dict,
+    wind_fig: go.Figure,
+    background_fig: go.Figure,
+    threed_fig: go.Figure,
+    krig_fig: go.Figure,
+    windrose_fig: go.Figure,
+) -> str:
+    """Generate a mass balance report."""
+    template_path = Path(__file__).parent / "templates" / "mass_balance_template.html"
+
+    # Convert the figures to HTML
+    plot_htmls = {}
+    for name, fig in zip(
+        ["3D", "krig", "windrose", "wind", "background"],
+        [threed_fig, krig_fig, windrose_fig, wind_fig, background_fig],
+        strict=False,
+    ):
+        if fig:
+            plot_htmls[name] = to_html(fig, full_html=False)
+        else:
+            plot_htmls[name] = plotting.blank_figure()
+
+    summary_data = {
+        "Estimated flux": f"{krig_params.get('volume', 0):.3f} kgh⁻¹",
+    }
+
+    with Path.open(template_path) as f:
+        template_content = f.read()
+
+    template = Template(template_content)
+    return template.render(
+        title="Mass Balance Report",
+        summary_data=summary_data,
+        threeD=plot_htmls["3D"],
+        krig=plot_htmls["krig"],
+        windrose=plot_htmls["windrose"],
+        wind=plot_htmls["wind"],
+        background=plot_htmls["background"],
+    )
+
+
+def generate_reports(name: str, processor, config: dict):
+    """
+    Generates reports, configuration files, and processed output variables for gasflux processing runs.
+
+    Parameters:
+        name (str): The name identifier for the current processing run.
+        processor (object): The processing object containing report data and output variables.
+        config (dict): Configuration dictionary used for processing.
+    """
+    output_dir = Path(config["output_dir"]).expanduser()
+    processing_time = datetime.now()
+    output_path = output_dir / name / processing_time.strftime("%Y-%m-%d_%H-%M-%S-%f_processing_run")
+    output_path.mkdir(parents=True, exist_ok=True)
+
+    # Save reports
+    for gas, report in processor.reports.items():
+        report_path = output_path / f"{name}_{gas}_report.html"
+        with open(report_path, "w", encoding="utf-8") as file:
+            file.write(report)
+
+    # Save config
+    header = f"# Gasflux output config for file {name} from processing run at {processing_time}\n"
+    config_path = output_path / f"{name}_config.yaml"
+    with open(config_path, "w") as file:
+        file.write(header)
+        yaml.safe_dump(config, file)
+
+    # Save DataFrame to CSV
+    if hasattr(processor, 'df') and processor.df is not None:
+        csv_path = output_path / f"{name}_data.csv"
+        processor.df.to_csv(csv_path, index=False)
+        logger.info(f"DataFrame saved to {csv_path}")
+
+    # Save output variables
+    output_vars = processor.output_vars
+    # output_vars = delete_large_arrays(output_vars, threshold_size=50)
+    header = (
+        f"# Gasflux output variables for file {name} from processing run at {processing_time}\n"
+    )
+    filename = output_path / f"{name}_output_vars.json"
+    with open(filename, "w") as file:
+        file.write(header)
+        json.dump(
+            output_vars, file, default=lambda item: item.tolist() if isinstance(item, np.ndarray) else item, indent=4
+        )
+    logger.info(f"Processing run saved to {output_path}")
+
+
+def delete_large_arrays(output_vars: dict, threshold_size: int) -> dict:
+    """
+    Iterate through the output_vars dictionary and replace large numpy arrays
+    with their metadata (e.g., shape and data type).
+
+    Parameters:
+        output_vars (dict): The dictionary containing output data including potential numpy arrays.
+        threshold_size (int): The number of elements above which an array is considered large.
+    """
+    del_keys = []
+    for key, value in output_vars.items():
+        if isinstance(value, dict):
+            output_vars[key] = delete_large_arrays(value, threshold_size)  # recursive
+        elif isinstance(value, np.ndarray):
+            if value.size > threshold_size:
+                del_keys.append(key)
+    for key in del_keys:
+        del output_vars[key]
+    return output_vars
diff --git a/src/gasflux/resources/model.pkl b/src/gasflux/resources/model.pkl
new file mode 100644
index 0000000..16c6811
Binary files /dev/null and b/src/gasflux/resources/model.pkl differ
diff --git a/src/gasflux/run_example.py b/src/gasflux/run_example.py
new file mode 100644
index 0000000..52509b9
--- /dev/null
+++ b/src/gasflux/run_example.py
@@ -0,0 +1,142 @@
+#!/usr/bin/env python3
+"""
+GasFlux 完整处理示例脚本
+
+这个脚本演示了完整的GasFlux处理流程：
+1. 使用data_processor.py处理原始Excel数据
+2. GasFlux数据验证和处理：
+   - 背景校正
+   - 空间处理
+   - 克里金插值
+   - 生成报告
+
+运行方法：
+    python run_example.py input.xlsx
+    python run_example.py input.xlsx --output processed_data.csv
+"""
+
+import sys
+import argparse
+from pathlib import Path
+
+# 导入同一包内的模块
+# 优先尝试相对导入（当作为包的一部分运行时）
+# 如果失败，则尝试绝对导入（当直接运行脚本时）
+try:
+    # 尝试相对导入
+    from .processing_pipelines import process_main
+    from .data_processor import process_file
+    print("✅ 成功导入GasFlux模块（相对导入）")
+except ImportError:
+    try:
+        # 相对导入失败，尝试绝对导入
+        from src.gasflux.processing_pipelines import process_main
+        from src.gasflux.data_processor import process_file
+        print("✅ 成功导入GasFlux模块（绝对导入）")
+    except ImportError as e:
+        print(f"❌ 导入GasFlux模块失败: {e}")
+        print("请确保GasFlux包结构完整，或从项目根目录运行")
+        sys.exit(1)
+
+
+def main():
+    """主函数：运行完整的数据处理和GasFlux分析流程"""
+
+    parser = argparse.ArgumentParser(
+        description="GasFlux完整处理流程：Excel数据预处理 + 通量分析",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+使用示例:
+  python run_example.py data.xlsx                    # 基本使用
+  python run_example.py data.xlsx --output result.csv # 指定输出文件名
+  python run_example.py data.xlsx --no-gasflux       # 仅预处理，不进行通量分析
+
+处理步骤:
+1. 使用data_processor.py处理Excel文件
+2. 对处理后的数据进行GasFlux通量分析
+        """
+    )
+
+    parser.add_argument('input_file', help='输入的Excel文件路径 (.xlsx 或 .xls)')
+    parser.add_argument('-o', '--output', help='预处理后的CSV输出文件名（可选）')
+    parser.add_argument('--no-gasflux', action='store_true',
+                       help='仅执行数据预处理，跳过GasFlux通量分析')
+    parser.add_argument('-c', '--config', help='GasFlux配置文件路径（可选，默认使用内置配置）')
+
+    args = parser.parse_args()
+
+    print("=== GasFlux 完整处理流程 ===\n")
+
+    # 检查输入文件
+    input_path = Path(args.input_file)
+    if not input_path.exists():
+        print(f"❌ 错误：输入文件不存在: {input_path}")
+        return
+
+    if input_path.suffix.lower() not in ['.xlsx', '.xls']:
+        print(f"❌ 错误：输入文件必须是Excel格式: {input_path}")
+        return
+
+    # 确定输出文件名
+    if args.output:
+        processed_csv = Path(args.output)
+    else:
+        processed_csv = input_path.with_suffix('.processed.csv')
+
+    # 确定配置文件
+    if args.config:
+        config_file = Path(args.config)
+    else:
+        config_file = Path(__file__).parent / "data" / "gasflux_config.yaml"
+
+    print(f"输入Excel文件: {input_path}")
+    print(f"预处理输出CSV: {processed_csv}")
+    if not args.no_gasflux:
+        print(f"GasFlux配置文件: {config_file}")
+    print()
+
+    try:
+        # 第一步：数据预处理
+        print("🔄 第一步：数据预处理...")
+        processed_df = process_file(str(input_path), str(processed_csv))
+        print(f"✅ 数据预处理完成，输出文件: {processed_csv}")
+        print()
+
+        # 如果不需要进行GasFlux分析，直接返回
+        if args.no_gasflux:
+            print("⏭️  跳过GasFlux通量分析")
+            return
+
+        # 第二步：GasFlux通量分析
+        print("🔄 第二步：GasFlux通量分析...")
+
+        # 检查配置文件
+        if not config_file.exists():
+            print(f"❌ 错误：GasFlux配置文件不存在: {config_file}")
+            return
+
+        print(f"使用数据文件: {processed_csv}")
+        print(f"使用配置文件: {config_file}")
+        print()
+
+        # 运行GasFlux处理流程
+        process_main(processed_csv, config_file)
+        print("\n✅ GasFlux通量分析完成！")
+
+        # 显示输出信息
+        output_dir = Path("../../examples/basic_usage/5m") / processed_csv.stem / "processing_run"
+        if output_dir.exists():
+            print(f"\n📁 GasFlux输出目录: {output_dir}")
+            print("📊 生成的文件:")
+            for file in output_dir.rglob("*"):
+                if file.is_file():
+                    print(f"   - {file.name}")
+
+    except Exception as e:
+        print(f"\n❌ 处理失败: {e}")
+        import traceback
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/gasflux/templates/mass_balance_template.html b/src/gasflux/templates/mass_balance_template.html
new file mode 100644
index 0000000..b5e0381
--- /dev/null
+++ b/src/gasflux/templates/mass_balance_template.html
@@ -0,0 +1,67 @@
+<!DOCTYPE html>
+<html>
+<head>
+<style>
+.grid-container {
+    display: grid;
+    grid-template-columns: repeat(4, 1fr);
+    grid-gap: 10px;
+    height: 800px;
+}
+
+.grid-item.double {
+    grid-column: span 2;
+}
+
+.grid-item {
+    width: 100%;
+    min-width: 300px;
+}
+
+.grid-item .plot-container {
+    width: 100%;
+    height: 100%;
+}
+</style>
+</head>
+<body>
+<h1>{{title}}</h1>
+<table>
+<tbody>
+{% for key, value in summary_data.items() %}
+<tr>
+    <td>{{key}}</td>
+    <td>{{value}}</td>
+</tr>
+{% endfor %}
+</tbody>
+</table>
+<div class="grid-container">
+<div class="grid-item">
+    <div class="plot-container">
+        {{ threeD|safe }}
+    </div>
+</div>
+<div class="grid-item double">
+    <div class="plot-container">
+        {{ krig|safe }}
+    </div>
+</div>
+<div class="grid-item">
+    <div class="plot-container">
+        {{ windrose|safe }}
+    </div>
+</div>
+<div class="grid-item double">
+    <div class="plot-container">
+        {{ wind|safe }}
+    </div>
+</div>
+<div class="grid-item double">
+    <div class="plot-container">
+        {{ background|safe }}
+    </div>
+</div>
+</div>
+</body>
+</html>
\ No newline at end of file
diff --git a/src/gasflux/testdata/exampledata.csv b/src/gasflux/testdata/exampledata.csv
new file mode 100644
index 0000000..24fa7be
--- /dev/null
+++ b/src/gasflux/testdata/exampledata.csv
@@ -0,0 +1,1001 @@
+timestamp,latitude,longitude,height_ato,windspeed,winddir,temperature,pressure,ch4,co2,c2h6
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diff --git a/src/gasflux/testdata/testconfig.yaml b/src/gasflux/testdata/testconfig.yaml
new file mode 100644
index 0000000..1ff330e
--- /dev/null
+++ b/src/gasflux/testdata/testconfig.yaml
@@ -0,0 +1,95 @@
+# testconfig.yaml
+
+horizontal_pixels: 500 # Width of the concentration map in pixels
+vertical_pixels: 100 # Height of the concentration map in pixels
+num_plumes: 10 # Number of Gaussian plumes
+groupiness: 0.5 # Groupiness of the plumes (0.0 to 1.0)
+spread: 0.1 # Spread of the plumes (0.0 to 1.0)
+wind_reference_height: 10 # Reference height for wind speed calculation (m)
+windspeed_avg: 5 # Average wind speed at 10m height (m/s)
+windspeed_rel_std: 0.2 # Relative standard deviation of wind speed (0.0 to inf), recommend 0.2-0.4
+surface_roughness: 0.1 # Surface roughness length (m)
+seed: 42 # Random seed for reproducibility
+simplex_octaves: 4 # Number of octaves for simplex noise (1 to inf, def 1)
+simplex_persistence: 0.7 # Persistence of simplex noise (0.0 to 1.0, def 0.5) - specifies the amplitude of each octave relative to the one below it
+simplex_lacunarity: 2.0 # Lacunarity of simplex noise (1.0 to inf, def 2.0) - specifies the frequency of each octave relative to the one below it
+winddir_avg: 0.0 # Average wind direction in degrees rel to plane (0 is CW)
+winddir_std: 10 # Standard deviation of wind direction in degrees
+timestamp: "2022-09-26 02:03:00"
+flight_time_seconds: 1000
+sample_frequency: 1
+start_coords:
+  - 54.87667
+  - 15.41
+transect_azimuth: 260 # the wind will start off 90 degrees CW to this azimuth, and is modified relative to that by the winddir_avg. 260 is a good value to test N problems
+sampling_altitude_ato_range:
+  - -10 # negative values should be fine
+  - 100
+sampling_horizontal_range:
+  - 50
+  - 950
+scene_altitude_range:
+  - -20
+  - 120
+scene_horizontal_range:
+  - 0
+  - 1000
+number_of_transects: 10
+gases:
+  ch4:
+    - 1.95
+    - 10.0
+  co2:
+    - 380.0
+    - 500.0
+  c2h6:
+    - 0.0
+    - 1.0
+temperature: 10.0
+pressure: 1000.0
+
+output_dir: ~/gasflux_reports
+
+algorithmic_baseline_settings:
+  algorithm: fastchrom
+
+semivariogram_settings:
+  model: spherical
+  estimator: cressie
+  n_lags: 20
+  bin_func: even
+  fit_method: lm
+  maxlag: 100
+  #fit_sigma: linear
+  tolerance: 10
+  azimuth: 0
+  bandwidth: 20
+
+ordinary_kriging_settings:
+  min_points: 3
+  max_points: 100
+  grid_resolution: 500
+  min_nodes: 10
+  cut_ground: False
+  y_min: ~
+
+required_cols:
+  latitude: [-90, 90]
+  longitude: [-180, 180]
+  height_ato: [-100, 500]
+  windspeed: [0, 30]
+  winddir: [0, 360]
+  temperature: [-50, 60]
+  pressure: [900, 1100]
+
+filters:
+  course_filter:
+    azimuth_filter: 10
+    azimuth_window: 5
+    elevation_filter: 5
+
+strategies:
+  background: "algorithm"
+  sensor: "insitu"
+  spatial: "curtain"
+  interpolation: "kriging"
\ No newline at end of file
diff --git a/src/gasflux/testdata/testdata.csv b/src/gasflux/testdata/testdata.csv
new file mode 100644
index 0000000..3b15806
--- /dev/null
+++ b/src/gasflux/testdata/testdata.csv
@@ -0,0 +1,1001 @@
+timestamp,latitude,longitude,x,height_ato,windspeed,temperature,pressure,ch4,co2,c2h6,norm_x,norm_altitude_ato,index_x,index_y,winddir,geometry,utm_easting,utm_northing,hor_distance,vert_distance,course_azimuth,course_elevation
+2022-09-26 02:03:00,54.87667,15.41,50.0,-10.0,5.7636649217848985,10.0,1000.0,1.9500281451845713,380.00001357720237,1.1292820243772104e-05,0.05,0.07142857142857142,24,7,359.37311574951946,POINT (526307.4073509022 6081144.509516094),526307.4073509022,6081144.509516094,,,,
+2022-09-26 02:03:01,54.8766544010694,15.409846579958499,59.09090909090909,-10.0,5.701597666420126,10.0,1000.0,1.9500404026708875,380.0000236957387,1.3841592705542263e-05,0.0590909090909091,0.07142857142857142,29,7,358.7056703640377,POINT (526297.573457074 6081142.716126854),526297.573457074,6081142.716126854,9.996084873172414,0.0,259.66465276264836,0.0
+2022-09-26 02:03:02,54.87663880213877,15.409693159976248,68.18181818181819,-10.0,5.658136726485464,10.0,1000.0,1.9500575591729477,380.0000408897218,1.696505410230664e-05,0.06818181818181819,0.07142857142857142,34,7,357.5735954982973,POINT (526287.7395593789 6081140.922759174),526287.7395593789,6081140.922759174,9.99608480905653,0.0,259.6647783179793,0.0
+2022-09-26 02:03:03,54.87662320320811,15.409539740053246,77.27272727272728,-10.0,5.642223864269387,10.0,1000.0,1.9500759373742647,380.00006260790616,1.997797100264311e-05,0.07727272727272728,0.07142857142857142,38,7,356.55246896166614,POINT (526277.9056578164 6081139.129413056),526277.9056578164,6081139.129413056,9.996084745561282,0.0,259.6649038739105,0.0
+2022-09-26 02:03:04,54.8766076042774,15.409386320189489,86.36363636363637,-10.0,5.648587858244058,10.0,1000.0,1.9501065050569175,380.00010501078367,2.4554107668660794e-05,0.08636363636363638,0.07142857142857142,43,7,355.3969654033975,POINT (526268.0717523864 6081137.336088495),526268.0717523864,6081137.336088495,9.996084682896887,0.0,259.6650294089567,0.0
+2022-09-26 02:03:05,54.87659200534666,15.409232900384978,95.45454545454547,-10.0,5.673670472887558,10.0,1000.0,1.9501386625700552,380.0001566833345,2.9029119474096733e-05,0.09545454545454547,0.07142857142857142,47,7,354.7333112625282,POINT (526258.2378430893 6081135.542785497),526258.2378430893,6081135.542785497,9.996084618819895,0.0,259.665154975035,0.0
+2022-09-26 02:03:06,54.87657640641588,15.409079480639713,104.54545454545455,-10.0,5.725218623129551,10.0,1000.0,1.9501911520790183,380.0002537022056,3.5944744990592254e-05,0.10454545454545455,0.07142857142857142,52,7,354.3696970043718,POINT (526248.4039299246 6081133.74950406),526248.4039299246,6081133.74950406,9.996084555750253,0.0,259.6652805258391,0.0
+2022-09-26 02:03:07,54.87656080748504,15.408926060953696,113.63636363636364,-10.0,5.777457959822437,10.0,1000.0,1.9502453482327697,380.0003673772214,4.284519345805669e-05,0.11363636363636365,0.07142857142857142,56,7,354.4698031579665,POINT (526238.5700128927 6081131.95624418),526238.5700128927,6081131.95624418,9.996084492719179,0.0,259.66540606578246,0.0
+2022-09-26 02:03:08,54.87654520855417,15.408772641326928,122.72727272727273,-10.0,5.849489690452739,10.0,1000.0,1.9503321173207224,380.0005721245226,5.376078814724531e-05,0.12272727272727274,0.07142857142857142,61,7,354.95635221245993,POINT (526228.7360919937 6081130.1630058605),526228.7360919937,6081130.1630058605,9.99608442928742,0.0,259.66553161611085,0.0
+2022-09-26 02:03:09,54.87652960962325,15.408619221759407,131.8181818181818,-10.0,5.909313535638171,10.0,1000.0,1.9504200055612742,380.0008023487054,6.492406450991009e-05,0.1318181818181818,0.07142857142857142,65,7,355.4171553952361,POINT (526218.9021672276 6081128.3697891),526218.9021672276,6081128.3697891,9.99608436618526,0.0,259.665657161309,0.0
+2022-09-26 02:03:10,54.87651401069229,15.408465802251133,140.90909090909093,-10.0,5.9868615132561365,10.0,1000.0,1.9505579657370602,380.0011996287321,8.303859430897744e-05,0.14090909090909093,0.07142857142857142,70,7,355.9292556857745,POINT (526209.0682385943 6081126.576593901),526209.0682385943,6081126.576593901,9.996084302682425,0.0,259.6657827168922,0.0
+2022-09-26 02:03:11,54.8764984117613,15.408312382802109,150.0,-10.0,6.047436830746507,10.0,1000.0,1.9506949940913105,380.00162789200834,0.00010201092521687018,0.15,0.07142857142857142,74,7,356.25175315815284,POINT (526199.2343060941 6081124.783420263),526199.2343060941,6081124.783420263,9.996084239113065,0.0,259.6659082723572,0.0
+2022-09-26 02:03:12,54.87648281283027,15.40815896341233,159.0909090909091,-10.0,6.110698796456781,10.0,1000.0,1.9509058065986362,380.00233552390125,0.000133460504925043,0.1590909090909091,0.07142857142857142,79,7,356.43058552081504,POINT (526189.4003697266 6081122.990268184),526189.4003697266,6081122.990268184,9.99608417626942,0.0,259.6660338176798,0.0
+2022-09-26 02:03:13,54.8764672138992,15.408005544081798,168.1818181818182,-10.0,6.1425832539255305,10.0,1000.0,1.951111051797966,380.003066562536,0.00016696064399955834,0.16818181818181818,0.07142857142857142,83,7,356.3010730073197,POINT (526179.5664294921 6081121.197137665),526179.5664294921,6081121.197137665,9.996084112963123,0.0,259.6661593678965,0.0
+2022-09-26 02:03:14,54.876451614968076,15.407852124810512,177.27272727272728,-10.0,6.149695797254641,10.0,1000.0,1.9514204099907506,380.00422329066174,0.0002231584344845958,0.17727272727272728,0.07142857142857142,88,7,355.7104399903312,POINT (526169.7324853905 6081119.404028704),526169.7324853905,6081119.404028704,9.996084050153474,0.0,259.66628490773127,0.0
+2022-09-26 02:03:15,54.87643601603692,15.407698705598474,186.36363636363637,-10.0,6.125816753150344,10.0,1000.0,1.951715522233947,380.00536886456354,0.0002834073416901586,0.18636363636363637,0.07142857142857142,92,7,354.87931200031227,POINT (526159.8985374217 6081117.610941307),526159.8985374217,6081117.610941307,9.996083986671046,0.0,259.66641047394523,0.0
+2022-09-26 02:03:16,54.876420417105734,15.407545286445682,195.45454545454547,-10.0,6.060527264557768,10.0,1000.0,1.95215115434926,380.0071063346476,0.00038456749108773183,0.19545454545454546,0.07142857142857142,97,7,353.4525568817885,POINT (526150.0645855862 6081115.817875468),526150.0645855862,6081115.817875468,9.996083923446747,0.0,259.66653601867574,0.0
+2022-09-26 02:03:17,54.8764048181745,15.407391867352137,204.54545454545456,-10.0,5.964725552413674,10.0,1000.0,1.952668587658934,380.0092087742348,0.0005241859808887582,0.20454545454545456,0.07142857142857142,102,7,351.7543322756277,POINT (526140.2306298836 6081114.024831189),526140.2306298836,6081114.024831189,9.996083860614025,0.0,259.6666615637668,0.0
+2022-09-26 02:03:18,54.87638921924323,15.407238448317841,213.63636363636365,-10.0,5.876499214541821,10.0,1000.0,1.9531464376301855,380.01116495987935,0.000672405204139021,0.21363636363636365,0.07142857142857142,106,7,350.36285284045294,POINT (526130.3966703143 6081112.2318084715),526130.3966703143,6081112.2318084715,9.996083796984552,0.0,259.6667871242552,0.0
+2022-09-26 02:03:19,54.87637362031191,15.407085029342788,222.72727272727275,-10.0,5.767526596105532,10.0,1000.0,1.9538288886685624,380.01395756899325,0.0009171525311755627,0.22272727272727275,0.07142857142857142,111,7,348.81008837668503,POINT (526120.5627068777 6081110.438807312),526120.5627068777,6081110.438807312,9.996083734529401,0.0,259.6669126642176,0.0
+2022-09-26 02:03:20,54.876358021380554,15.406931610426984,231.81818181818184,-10.0,5.692501672821711,10.0,1000.0,1.9544459293779177,380.01646684306246,0.0011729770851334276,0.23181818181818184,0.07142857142857142,115,7,347.8645065399562,POINT (526110.7287395744 6081108.645827714),526110.7287395744,6081108.645827714,9.996083671162983,0.0,259.6670382194576,0.0
+2022-09-26 02:03:21,54.87634242244917,15.406778191570428,240.90909090909093,-10.0,5.623289813014538,10.0,1000.0,1.9553085392625313,380.0199384141567,0.0015877209423254676,0.24090909090909093,0.07142857142857142,120,7,347.15224859703216,POINT (526100.8947684044 6081106.8528696755),526100.8947684044,6081106.8528696755,9.99608360817866,0.0,259.66716376419595,0.0
+2022-09-26 02:03:22,54.876326823517736,15.406624772773114,250.00000000000003,-10.0,5.586216204987441,10.0,1000.0,1.9560722189805266,380.02297638400654,0.002012733632866698,0.25000000000000006,0.07142857142857142,124,7,346.8965761925583,POINT (526091.0607933672 6081105.059933197),526091.0607933672,6081105.059933197,9.996083545533395,0.0,259.6672893146663,0.0
+2022-09-26 02:03:23,54.87631122458625,15.406471354035048,259.0909090909091,-10.0,5.549886168164713,10.0,1000.0,1.9571177382412297,380.0270955887956,0.0026869916544790488,0.25909090909090915,0.07142857142857142,129,7,346.67890109905267,POINT (526081.2268144635 6081103.267018278),526081.2268144635,6081103.267018278,9.996083482248963,0.0,259.66741486442004,0.0
+2022-09-26 02:03:24,54.87629562565474,15.406317935356228,268.1818181818182,-10.0,5.536757918681367,10.0,1000.0,1.9580248137529428,380.0306547976658,0.0033627114618665476,0.2681818181818182,0.07142857142857142,133,7,346.5238221291873,POINT (526071.3928316928 6081101.474124919),526071.3928316928,6081101.474124919,9.99608341963768,0.0,259.6675404094025,0.0
+2022-09-26 02:03:25,54.87628002672318,15.406164516736656,277.27272727272725,-10.0,5.553984921173504,10.0,1000.0,1.9592427450782604,380.03546290986253,0.004409848469979827,0.2772727272727272,0.07142857142857142,138,7,346.4133059650662,POINT (526061.5588450555 6081099.681253118),526061.5588450555,6081099.681253118,9.996083356730807,0.0,259.66766595402754,0.0
+2022-09-26 02:03:26,54.876264427791575,15.40601109817633,286.3636363636364,-10.0,5.596731976119806,10.0,1000.0,1.9602804940176155,380.03963871448894,0.005434819923077223,0.2863636363636364,0.07142857142857142,142,7,346.4280687579564,POINT (526051.7248545515 6081097.888402879),526051.7248545515,6081097.888402879,9.996083293652381,0.0,259.66779150927704,0.0
+2022-09-26 02:03:27,54.87624882885993,15.405857679675249,295.4545454545455,-10.0,5.681108422815226,10.0,1000.0,1.9616514906743125,380.0453589367831,0.006985106320066661,0.29545454545454547,0.07142857142857142,147,7,346.6025241241012,POINT (526041.8908601807 6081096.095574198),526041.8908601807,6081096.095574198,9.996083231123063,0.0,259.66791704877323,0.0
+2022-09-26 02:03:28,54.87623322992825,15.405704261233415,304.54545454545456,-10.0,5.765436047103088,10.0,1000.0,1.9628037712233117,380.05042660478796,0.008466457886030777,0.30454545454545456,0.07142857142857142,151,7,346.8659028558997,POINT (526032.0568619432 6081094.302767078),526032.0568619432,6081094.302767078,9.996083168140633,0.0,259.6680426040259,0.0
+2022-09-26 02:03:29,54.876217630996535,15.405550842850829,313.6363636363637,-10.0,5.878251249339562,10.0,1000.0,1.9643103874725674,380.0575292118875,0.01065274853977091,0.31363636363636366,0.07142857142857142,156,7,347.31776778746314,POINT (526022.2228598393 6081092.509981518),526022.2228598393,6081092.509981518,9.996083105196686,0.0,259.66816814841786,0.0
+2022-09-26 02:03:30,54.87620203206478,15.405397424527488,322.72727272727275,-10.0,5.983211661504501,10.0,1000.0,1.9658904999335982,380.0656822468473,0.013243854419196299,0.32272727272727275,0.07142857142857142,161,7,347.8362184127275,POINT (526012.3888538688 6081090.717217519),526012.3888538688,6081090.717217519,9.996083042028697,0.0,259.66829370880566,0.0
+2022-09-26 02:03:31,54.87618643313298,15.405244006263391,331.81818181818187,-10.0,6.050795848923459,10.0,1000.0,1.9672104553430505,380.07310908243596,0.01562789431806899,0.33181818181818185,0.07142857142857142,165,7,348.23040615298555,POINT (526002.5548440315 6081088.924475078),526002.5548440315,6081088.924475078,9.996082979972908,0.0,259.66841924317623,0.0
+2022-09-26 02:03:32,54.87617083420114,15.405090588058542,340.90909090909093,-10.0,6.1041582380035075,10.0,1000.0,1.968938654777339,380.08369414463016,0.019014009025306398,0.34090909090909094,0.07142857142857142,170,7,348.59825765740175,POINT (525992.7208303278 6081087.131754198),525992.7208303278,6081087.131754198,9.996082916557336,0.0,259.66854480320814,0.0
+2022-09-26 02:03:33,54.876155235269266,15.40493716991294,350.0,-10.0,6.1169539105253525,10.0,1000.0,1.970393547095642,380.09330072537955,0.02205368013350821,0.35,0.07142857142857142,174,7,348.7202284998166,POINT (525982.8868127575 6081085.339054877),525982.8868127575,6081085.339054877,9.996082854315977,0.0,259.6686703427138,0.0
+2022-09-26 02:03:34,54.876139636337356,15.404783751826582,359.0909090909091,-10.0,6.0937322562567955,10.0,1000.0,1.9723198036460294,380.1067925577085,0.02626352996423706,0.3590909090909091,0.07142857142857142,179,7,348.5800679852571,POINT (525973.0527913208 6081083.546377118),525973.0527913208,6081083.546377118,9.996082790996407,0.0,259.6687959027489,0.0
+2022-09-26 02:03:35,54.8761240374054,15.404630333799467,368.1818181818182,-10.0,6.046528432536637,10.0,1000.0,1.9739617824223328,380.1187429829405,0.029947517280256575,0.36818181818181817,0.07142857142857142,183,7,348.2077087278944,POINT (525963.2187660174 6081081.753720916),525963.2187660174,6081081.753720916,9.996082729132583,0.0,259.66892143712573,0.0
+2022-09-26 02:03:36,54.8761084384734,15.404476915831602,377.2727272727273,-10.0,5.9613837979131095,10.0,1000.0,1.9761608915353386,380.1349575608213,0.03491648734640442,0.3772727272727273,0.07142857142857142,188,7,347.4508418570288,POINT (525953.3847368477 6081079.961086275),525953.3847368477,6081079.961086275,9.996082665961504,0.0,259.6690469917927,0.0
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+2022-09-26 02:19:37,54.87663880213877,15.409693159976248,68.18181818181819,89.0,6.629367160116742,10.0,1000.0,2.5204316159258746,380.0279023736738,0.007651136592231039,0.06818181818181819,0.7785714285714286,34,77,351.51089643599613,POINT (526287.7395593789 6081140.922759174),526287.7395593789,6081140.922759174,9.996084745561282,0.0,79.66490387391048,0.0
+2022-09-26 02:19:38,54.8766544010694,15.409846579958499,59.09090909090909,89.0,6.8734191073537705,10.0,1000.0,2.453946258322088,380.02446226113483,0.005905633022495055,0.0590909090909091,0.7785714285714286,29,77,352.16224240731185,POINT (526297.573457074 6081142.716126854),526297.573457074,6081142.716126854,9.99608480905653,0.0,79.66477831797933,0.0
+2022-09-26 02:19:39,54.87667,15.41,50.0,89.0,7.175195387416064,10.0,1000.0,2.3947926564515765,380.02130694907993,0.004516716004716476,0.05,0.7785714285714286,24,77,353.18128618082153,POINT (526307.4073509022 6081144.509516094),526307.4073509022,6081144.509516094,9.996084873172414,0.0,79.66465276264836,0.0
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/test_processing.py b/tests/test_processing.py
new file mode 100644
index 0000000..31de69b
--- /dev/null
+++ b/tests/test_processing.py
@@ -0,0 +1,205 @@
+import pandas as pd
+from src.gasflux import processing
+import yaml
+from pathlib import Path
+import numpy as np
+from src.gasflux.processing import min_angular_displacement
+import pytest
+
+
+testdf = pd.read_csv(Path(__file__).parents[1] / "src" / "gasflux" / "testdata" / "testdata.csv")
+testconfig = yaml.safe_load(open(Path(__file__).parents[1] / "src" / "gasflux" / "testdata" / "testconfig.yaml"))
+
+
+def load_cols(cols):
+    return testdf[cols]
+
+
+def test_min_angular_diff_def():
+    def test_min_angular_displacement():
+        assert min_angular_displacement(10, 350) == 20
+        assert min_angular_displacement(0, 180) == 180
+        x = np.array([10, 0])
+        y = np.array([350, 180])
+        expected = np.array([20, 180])
+        result = min_angular_displacement(x, y)
+        assert np.all(result == expected), "Vectorized function failed"
+
+
+def test_circ_median():
+    x = np.array([0, 1, 2, 359, 4, 3])
+    median = processing.circ_median(x)
+    assert median == 1.5, "Circular median not calculated correctly"
+
+
+@pytest.mark.parametrize(
+    "plane_angle,expected_winddir_rel,expected_windspeed_normal",
+    [
+        (
+            90,
+            [0, 90, 0, 90, 0],
+            [5, 0, 5, 0, 5],
+        ),
+        (
+            30,
+            [60, 30, 60, 30, 60],
+            np.array([1 / 2, np.sqrt(3) / 2, 1 / 2, np.sqrt(3) / 2, 1 / 2]) * 5,
+        ),
+        (
+            60,
+            [30, 60, 30, 60, 30],
+            np.array([np.sqrt(3) / 2, 1 / 2, np.sqrt(3) / 2, 1 / 2, np.sqrt(3) / 2]) * 5,
+        ),
+    ],
+)
+def test_wind_offset_correction_parametrized(plane_angle, expected_winddir_rel, expected_windspeed_normal):
+    data = {"winddir": [0, 90, 180, 270, 360], "windspeed": [5, 5, 5, 5, 5]}
+    df = pd.DataFrame(data)
+    corrected_df = processing.wind_offset_correction(df, plane_angle)
+    assert "winddir_rel" in corrected_df.columns, f"Relative wind direction column not added for angle {plane_angle}"
+    assert "windspeed" in corrected_df.columns, f"Normalised wind speed column not added for angle {plane_angle}"
+    assert np.allclose(corrected_df["winddir_rel"], expected_winddir_rel, rtol=1e-5, atol=1e-10), (
+        f"Relative wind directions not calculated correctly for angle {plane_angle}"
+    )
+    assert np.allclose(corrected_df["windspeed"], expected_windspeed_normal, rtol=1e-5, atol=1e-10), (
+        f"Normalised wind speeds not calculated correctly for angle {plane_angle}"
+    )
+
+
+def test_bimodal_azimuth():
+    input_mode = testconfig["transect_azimuth"]
+    input_reciprocal_mode = (input_mode + 180) % 360
+    df = load_cols(["course_azimuth", "height_ato"])
+    mode1, mode2 = processing.bimodal_azimuth(df)
+    assert (
+        min_angular_displacement(mode1, input_mode) < 3 or min_angular_displacement(mode1, input_reciprocal_mode) < 3
+    ), "Mode1 does not match expected azimuth or its reciprocal within 3 degrees"
+
+    if min_angular_displacement(mode1, input_mode) < 3:
+        assert min_angular_displacement(mode2, input_reciprocal_mode) < 3, (
+            "Mode2 does not match expected reciprocal azimuth within 3 degrees"
+        )
+    else:
+        assert min_angular_displacement(mode2, input_mode) < 3, "Mode2 does not match expected azimuth within 3 degrees"
+
+
+def test_bimodal_elevation():
+    df = load_cols(["course_elevation", "height_ato"])
+    input_mode = 0
+    input_reciprocal_mode = 0 - input_mode
+    mode1, mode2 = processing.bimodal_elevation(df)
+    assert (
+        min_angular_displacement(mode1, input_mode) < 3 or min_angular_displacement(mode1, input_reciprocal_mode) < 3
+    ), "Mode1 does not match expected elevation or its reciprocal within 3 degrees"
+    if min_angular_displacement(mode1, input_mode) < 3:
+        assert min_angular_displacement(mode2, input_reciprocal_mode) < 3, (
+            "Mode2 does not match expected reciprocal elevation within 3 degrees"
+        )
+    else:
+        assert min_angular_displacement(mode2, input_mode) < 3, (
+            "Mode2 does not match expected elevation within 3 degrees"
+        )
+
+
+def test_height_transect_splitter():
+    df = load_cols(["height_ato"])
+    df, fig = processing.height_transect_splitter(df)
+    assert "transect_num" in df.columns, "Transect number column not added to dataframe"
+    assert df["transect_num"].nunique() == testconfig["number_of_transects"], (
+        "Dataframe was not split into the right number of transects"
+    )
+
+
+def test_add_transect_azimuth_switches():
+    df = load_cols(["course_azimuth"])
+    df = processing.add_transect_azimuth_switches(df)
+    assert df["transect_num"].nunique() == testconfig["number_of_transects"], (
+        "Transect azimuth switches not added to dataframe"
+    )
+
+
+def test_course_filter():
+    df = load_cols(["course_azimuth", "course_elevation", "height_ato"])
+    azimuth_filter = testconfig["filters"]["course_filter"]["azimuth_filter"]
+    azimuth_window = testconfig["filters"]["course_filter"]["azimuth_window"]
+    elevation_filter = testconfig["filters"]["course_filter"]["elevation_filter"]
+    df_filtered, df_unfiltered = processing.course_filter(
+        df, azimuth_filter=azimuth_filter, azimuth_window=azimuth_window, elevation_filter=elevation_filter
+    )
+    input_mode = testconfig["transect_azimuth"]
+    input_reciprocal_mode = (input_mode + 180) % 360
+    # assert that the filtered dataframe contains the expected azimuth or its reciprocal within the window
+    df_filtered["near_mode1"] = df_filtered["rolling_course_azimuth"].apply(
+        lambda x: min_angular_displacement(x, input_mode) < azimuth_window
+    )
+    df_filtered["near_mode2"] = df_filtered["rolling_course_azimuth"].apply(
+        lambda x: min_angular_displacement(x, input_reciprocal_mode) < azimuth_window
+    )
+    assert df_filtered["near_mode1"].any() or df_filtered["near_mode2"].any(), (
+        "Filtered dataframe does not contain expected azimuth or its reciprocal within the window"
+    )
+
+
+def test_mCount_max():
+    data_dict = {1: -5.4, 2: 0.6, 3: 5.6, 4: 3.2, 5: 10.4, 6: 18.4, 7: 20.8, 8: 19.4}
+    start, end = processing.mCount_max(data_dict)
+    assert start == 4, "Start index of max count not calculated correctly"
+    assert end == 7, "End index of max count not calculated correctly"
+
+
+def test_largest_monotonic_transect_series():
+    df = load_cols(
+        ["timestamp", "height_ato", "course_azimuth", "longitude", "latitude", "utm_easting", "utm_northing"]
+    )
+    df, starttransect, endtransect = processing.largest_monotonic_transect_series(df)
+    starttransect = 1
+    endtransect = testconfig["number_of_transects"]
+    assert starttransect == starttransect, "Start index of largest monotonic transect not calculated correctly"
+    assert endtransect == endtransect, "End index of largest monotonic transect not calculated correctly"
+
+
+def test_remove_non_transects():
+    df = load_cols(
+        ["height_ato", "course_azimuth", "course_elevation", "longitude", "latitude", "utm_easting", "utm_northing"]
+    )
+    retained_df, removed_df = processing.remove_non_transects(df)
+    assert retained_df is not None, "Retained dataframe is None"
+    assert removed_df is not None, "Removed dataframe is None"
+
+
+def test_flatten_linear_plane():
+    df = load_cols(["height_ato", "utm_easting", "utm_northing"])
+    df, plane_angle = processing.flatten_linear_plane(df)
+    input_plane_angle = testconfig["transect_azimuth"]
+    reciprocal_plane_angle = (input_plane_angle + 180) % 360
+    assert (
+        min_angular_displacement(plane_angle, input_plane_angle) < 3
+        or min_angular_displacement(plane_angle, reciprocal_plane_angle) < 3
+    ), "Plane angle not calculated correctly"
+
+
+def test_drone_anemo_to_point_wind():
+    data = {
+        "yaw": [0, 90, 0, -90, 180],
+        "anemo_u": [0, 0, 10, 10, 10],
+        "anemo_v": [0, 0, 0, 0, 0],
+        "easting": [0, 10, 0, 10, 0],
+        "northing": [0, 0, 0, 0, 10],
+    }
+    df_test = pd.DataFrame(data)
+    yaw_col = "yaw"
+    anemo_u_col = "anemo_u"
+    anemo_v_col = "anemo_v"
+    easting_col = "easting"
+    northing_col = "northing"
+    result_df = processing.drone_anemo_to_point_wind(
+        df_test, yaw_col, anemo_u_col, anemo_v_col, easting_col, northing_col
+    )
+    expected_windspeed = np.array([0, 10, 10, np.sqrt(200), np.sqrt(200)])
+    expected_winddir = np.array(
+        [180, 270, 270, 225, 135]
+    )  # 180 not zero because of the way IEEE 754 handles floating point numbers
+    windspeed_diff = np.abs(result_df["windspeed"].values - expected_windspeed)
+    winddir_diff = processing.min_angular_displacement(result_df["winddir"].to_numpy(), expected_winddir)
+    assert np.all(windspeed_diff < 1e-10), "Wind speed not calculated correctly"
+    assert np.all(np.array(winddir_diff) < 3), "Wind direction not calculated correctly"
diff --git a/tests/test_processing_pipelines.py b/tests/test_processing_pipelines.py
new file mode 100644
index 0000000..54bbd7b
--- /dev/null
+++ b/tests/test_processing_pipelines.py
@@ -0,0 +1,58 @@
+from src.gasflux import processing_pipelines
+import yaml
+from pathlib import Path
+import pytest
+
+
+@pytest.fixture
+def setup_test_environment(tmp_path):
+    """Prepare actual test data and configuration with a modified output directory."""
+    df_path = Path(__file__).parents[1] / "src" / "gasflux" / "testdata" / "testdata.csv"
+    config_path = Path(__file__).parents[1] / "src" / "gasflux" / "testdata" / "testconfig.yaml"
+
+    with open(config_path) as f:
+        config = yaml.safe_load(f)
+    config["output_dir"] = str(tmp_path)
+
+    temp_config_path = tmp_path / "temp_testconfig.yaml"
+    with open(temp_config_path, "w") as f:
+        yaml.safe_dump(config, f)
+
+    return df_path, temp_config_path
+
+
+def test_process_main_config_output(setup_test_environment):
+    df_path, temp_config_path = setup_test_environment
+    processing_pipelines.process_main(df_path, temp_config_path)
+    with open(temp_config_path) as f:
+        temp_config = yaml.safe_load(f)
+    output_dir = Path(temp_config["output_dir"]) / df_path.stem
+    assert output_dir.exists(), "Output directory does not exist."
+    processing_run_dirs = [d for d in output_dir.iterdir() if d.is_dir()]
+    assert len(processing_run_dirs) > 0, "No processing run directory found."
+    processing_run_dir = processing_run_dirs[0]
+
+    with open(temp_config_path) as f:
+        original_config = yaml.safe_load(f)
+
+    for gas in original_config.get("gases", []):
+        report_path = processing_run_dir / f"{df_path.stem}_{gas}_report.html"
+        assert report_path.exists(), f"Report for {gas} does not exist."
+
+    config_dump_path = processing_run_dir / f"{df_path.stem}_config.yaml"
+    assert config_dump_path.exists(), "Config dump file does not exist."
+
+    def load_and_redump(yaml_file):
+        with open(yaml_file) as file:
+            data = yaml.safe_load(file)
+            redumped_data = yaml.dump(data, sort_keys=True, default_flow_style=False)
+        return redumped_data
+
+    assert load_and_redump(temp_config_path) == load_and_redump(
+        config_dump_path
+    ), "The dumped config does not match the input config."
+
+
+def test_process_main_deterministic_output(setup_test_environment):
+    df_path, temp_config_path = setup_test_environment
+    processing_pipelines.process_main(df_path, temp_config_path)
diff --git a/tools/mono_flight_splitter.py b/tools/mono_flight_splitter.py
new file mode 100644
index 0000000..8180d99
--- /dev/null
+++ b/tools/mono_flight_splitter.py
@@ -0,0 +1,108 @@
+"""a script to split a folder of flights into multiple flights based on the monotonic changes in altitude.
+Takes a csv file as an input and will split it out into several csvs.
+Needs course azimuths to work properly.
+"""
+
+import argparse
+from pathlib import Path
+
+import colorama
+import numpy as np
+import pandas as pd
+
+from src.gasflux import processing
+
+colorama.init()
+
+
+def main(target_dir, search_string, filter_mask, output_dir=None):
+    df_list = {}
+    for file_path in Path(target_dir).rglob(search_string):
+        df = pd.read_csv(file_path)
+        df_list[file_path] = df
+
+    for file_path, df in df_list.items():
+        print(colorama.Fore.WHITE + "----------------------------------------")
+        if filter_mask in df.columns:  # check for boolean mask columns
+            df = df[~df[filter_mask]].reset_index(drop=True)
+        df, groupdict = processing.monotonic_transect_groups(df)
+        last_transect = None
+        last_group_trend = None
+        for group in df["group"].unique():
+            # leaving this out for now as the group folder is a good identifier
+            # if len(df['group'].unique()) == 1:
+            #     print(colorama.Fore.RED + f'Skipping{file_path.name} - only one group')
+            #     continue
+            group_df = df[df["group"] == group]
+            avg_altitudes = group_df.groupby("transect_num")["height_ato"].mean().values
+            avg_change = (
+                sum([avg_altitudes[i + 1] - avg_altitudes[i] for i in range(len(avg_altitudes) - 1)])
+                / len(avg_altitudes)
+                if len(avg_altitudes) > 1
+                else 0
+            )
+            # what's the trend
+            current_group_trend = "ascending" if avg_change > 0 else "descending"
+            # add last transect from previous group if there's a change in trend
+            if last_group_trend and last_group_trend != current_group_trend and last_transect is not None:
+                group_df = pd.concat([last_transect, group_df])
+                avg_altitudes = group_df.groupby("transect_num")["height_ato"].mean().values
+            avg_altitudes = np.array(avg_altitudes)
+            # check if the group is monotonic
+            is_monotonic = np.all(np.diff(avg_altitudes) > 0) or np.all(np.diff(avg_altitudes) < 0)
+            if not is_monotonic:  # exception
+                print(f"group {group} is not monotonic - check the code!")
+            formatted_avg_altitudes = ", ".join([f"{alt:.1f}" for alt in avg_altitudes])
+            # do it where transect is the maximum transect number
+            last_transect = group_df[group_df["transect_num"] == group_df["transect_num"].max()].copy()
+            last_transect.loc[:, "transect_num"] = 0
+            last_group_trend = current_group_trend
+            unique_transects = len(group_df["transect_num"].unique())
+            if unique_transects < 3:
+                print(
+                    colorama.Fore.RED + f"Not saving {group} from {file_path} - too few transects"
+                    f"({unique_transects} transects at {formatted_avg_altitudes}m)"
+                )
+            else:
+                file_path = Path(file_path)
+                if output_dir:
+                    output_path = Path(
+                        Path(output_dir)
+                        / f"{file_path.parents[1].name}"
+                        / f"{file_path.parent.name}_{group}"
+                        / f"{file_path.stem}_{group}.csv"
+                    )
+                else:
+                    # assuming in date and time folder
+                    output_path = Path(
+                        file_path.parent.parent.parent  # time -> date -> analysis
+                        / "splits"
+                        / file_path.parent.parent.name  # date
+                        / f"{file_path.parent.name}_{group}"  # time + group
+                        / f"{file_path.stem}_{group}.csv"
+                    )
+                output_path.parent.mkdir(parents=True, exist_ok=True)
+                group_df.to_csv(output_path, index=False)
+                print(
+                    colorama.Fore.GREEN
+                    + f'wrote {unique_transects} monotonic transects at {formatted_avg_altitudes}m to \n'
+                    f'{"/".join(output_path.parts[-5:])}'
+                )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--target-dir", help="the directory to search for csvs", default="survey")
+    parser.add_argument("--search-string", help="the string to search for in the directory", default="*filtered.csv")
+    parser.add_argument(
+        "--filter-mask", help="name of a column with a boolean filter; TRUE means discard", default="filtered"
+    )
+    parser.add_argument("--output-dir", help="the metadirectory to save the date/time/csvs to", required=False)
+
+    args = parser.parse_args()
+    main(
+        args.target_dir,
+        args.search_string,
+        args.filter_mask,
+        args.output_dir,
+    )