Initial commit

.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

.gitignore

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+# 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/
+*.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
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# 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
+
+# celery beat schedule file
+celerybeat-schedule
+
+# 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/

EddyData.py

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+import numpy as np
+import csv
+
+class EddyData():
+
+    def __init__(self, filename, time_offset = np.timedelta64(-8, 'h')):
+
+        time_offset = np.timedelta64(-8, 'h')
+        reader = csv.reader(open(filename,'r',encoding = 'utf-8-sig'), delimiter=',')
+        columns = None
+        self.data_structure = {}
+
+        for line in reader:
+            if columns is None:
+                columns = line[0:]
+                self.data_structure = {key:[] for key in columns}
+            else:
+                self.data_structure[columns[0]] += [np.datetime64(line[0])+time_offset]
+                for (key, data) in zip(columns[1:], line[1:]):
+                    self.data_structure[key] += [np.float(data)]
+
+        self.data_structure[columns[0]] = np.array(self.data_structure[columns[0]], dtype = np.datetime64)
+        for key in columns[1:]:
+            self.data_structure[key] = np.array(self.data_structure[key])
+
+    def __apply_filtered_indexes(self, filtered_indexes):
+        for key in self.data_structure:
+            self.data_structure[key] = self.data_structure[key][filtered_indexes]
+
+    def filter_by_ustar(self, ustar):
+        i = np.where(np.all((self.data_structure['qc_co2_flux'] < 1, self.data_structure['ustar'] >= ustar), axis=0))
+        self.__apply_filtered_indexes(i)
+
+    def denoise(self, field):
+        from scipy.signal import detrend
+        from scipy.optimize import fsolve
+        from scipy.special import erf
+
+        def find_zscore(f):
+            def inner_function(zscore):
+                return 1 - f - erf(zscore / np.sqrt(2.0))
+
+            return fsolve(inner_function, 3)
+
+        value_m = self.data_structure[field]
+        dates_m = self.data_structure['Datetime']
+
+        cleared = False
+
+        while not cleared:
+            value_m = value_m - np.mean(value_m)
+
+            value_m = detrend(value_m)
+
+            zscore = np.abs(value_m) / np.std(value_m)
+
+            number_of_points = value_m.size
+
+            f = 1/number_of_points
+
+            max_zscore = find_zscore(f)
+
+            i = np.where(zscore <= max_zscore)
+
+            if len(i[0]) == number_of_points:
+                cleared = True
+            else:
+                value_m = value_m[i]
+                dates_m = dates_m[i]
+
+        _, indexes, _ = np.intersect1d(self.data_structure['Datetime'], dates_m, assume_unique = False, return_indices = True)
+
+        self.__apply_filtered_indexes(indexes)
+
+    def detrend(self, field):
+
+        from scipy.signal import detrend
+        return_data = self.data_structure[field]
+        return_data -= np.mean(return_data)
+        return_data = detrend(return_data)
+
+        return return_data
+
+    @property
+    def time(self):
+        return self.data_structure['Datetime']
+
+    @property
+    def co2_flux(self):
+        return self.data_structure['co2_flux']
+
+

LICENSE

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+MIT License
+
+Copyright (c) 2021 George Hilley
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.