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Add Flux variables CSV and enhance micromet functionality
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Added a comprehensive CSV file for Flux variables, enhancing data tracking. Updated multiple scripts in the micromet package to improve configuration handling, CSV path management, and file compilation. Moreover, introduced new dependencies in the environment file and pyproject.toml to support enhanced data visualization and statistical analysis features.
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@inkenbrandt
inkenbrandt committed Sep 20, 2024
1 parent b964b01 commit 8827552
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3 changes: 3 additions & 0 deletions micromet/__init__.py
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Expand Up @@ -3,8 +3,11 @@
# from .Larry_Flux_Calc import *
from .cs_wrapper import *
from .licor_wrapper import *
from .csi_header_python import *
from .converter import *
from .tools import *
from .outliers import *
from .graphs import *
__version__ = '0.2.0'

#%%
168 changes: 93 additions & 75 deletions micromet/converter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -76,28 +76,50 @@
'LW_OUT',
'P']

math_soils = ["VWC_5cm_N_Avg","VWC_5cm_S_Avg","Ka_5cm_N_Avg","T_5cm_N_Avg","BulkEC_5cm_N_Avg","VWC_10cm_N_Avg","Ka_10cm_N_Avg","T_10cm_N_Avg","BulkEC_10cm_N_Avg","VWC_20cm_N_Avg","Ka_20cm_N_Avg","T_20cm_N_Avg","BulkEC_20cm_N_Avg","VWC_30cm_N_Avg","Ka_30cm_N_Avg","T_30cm_N_Avg","BulkEC_30cm_N_Avg","VWC_40cm_N_Avg","Ka_40cm_N_Avg","T_40cm_N_Avg","BulkEC_40cm_N_Avg","VWC_50cm_N_Avg","Ka_50cm_N_Avg","T_50cm_N_Avg","BulkEC_50cm_N_Avg","VWC_60cm_N_Avg","Ka_60cm_N_Avg","T_60cm_N_Avg","BulkEC_60cm_N_Avg","VWC_75cm_N_Avg","Ka_75cm_N_Avg","T_75cm_N_Avg","BulkEC_75cm_N_Avg","VWC_100cm_N_Avg","Ka_100cm_N_Avg","T_100cm_N_Avg","BulkEC_100cm_N_Avg","Ka_5cm_S_Avg","T_5cm_S_Avg","BulkEC_5cm_S_Avg","VWC_10cm_S_Avg","Ka_10cm_S_Avg","T_10cm_S_Avg","BulkEC_10cm_S_Avg","VWC_20cm_S_Avg","Ka_20cm_S_Avg","T_20cm_S_Avg","BulkEC_20cm_S_Avg","VWC_30cm_S_Avg","Ka_30cm_S_Avg","T_30cm_S_Avg","BulkEC_30cm_S_Avg","VWC_40cm_S_Avg","Ka_40cm_S_Avg","T_40cm_S_Avg","BulkEC_40cm_S_Avg","VWC_50cm_S_Avg","Ka_50cm_S_Avg","T_50cm_S_Avg","BulkEC_50cm_S_Avg","VWC_60cm_S_Avg","Ka_60cm_S_Avg","T_60cm_S_Avg","BulkEC_60cm_S_Avg","VWC_75cm_S_Avg","Ka_75cm_S_Avg","T_75cm_S_Avg","BulkEC_75cm_S_Avg","VWC_100cm_S_Avg","Ka_100cm_S_Avg","T_100cm_S_Avg"]

math_soils_v2 = ["SWC_3_1_1","SWC_4_1_1","K_3_1_1","TS_3_1_1","EC_3_1_1","SWC_3_2_1","K_3_2_1","TS_3_2_1","EC_3_2_1","SWC_3_3_1","K_3_3_1","TS_3_3_1","EC_3_3_1","SWC_3_4_1","K_3_4_1","TS_3_4_1","EC_3_4_1","SWC_3_5_1","K_3_5_1","TS_3_5_1","EC_3_5_1","SWC_3_6_1","K_3_6_1","TS_3_6_1","EC_3_6_1","SWC_3_7_1","K_3_7_1","TS_3_7_1","EC_3_7_1","SWC_3_8_1","K_3_8_1","TS_3_8_1","EC_3_8_1","SWC_3_9_1","K_3_9_1","TS_3_9_1","EC_3_9_1","K_4_1_1","TS_4_1_1","EC_4_1_1","SWC_4_2_1","K_4_2_1","TS_4_2_1","EC_4_2_1","SWC_4_3_1","K_4_3_1","TS_4_3_1","EC_4_3_1","SWC_4_4_1","K_4_4_1","TS_4_4_1","EC_4_4_1","SWC_4_5_1","K_4_5_1","TS_4_5_1","EC_4_5_1","SWC_4_6_1","K_4_6_1","TS_4_6_1","EC_4_6_1","SWC_4_7_1","K_4_7_1","TS_4_7_1","EC_4_7_1","SWC_4_8_1","K_4_8_1","TS_4_8_1","EC_4_8_1","EC_4_9_1","SWC_4_9_1","K_4_9_1","TS_4_9_1","TS_1_1_1","TS_2_1_1","SWC_1_1_1","SWC_2_1_1"]

well_soils =["SWC_3_1_1","SWC_4_1_1","K_3_1_1","TS_3_1_1","EC_3_1_1",
"SWC_3_2_1","K_3_2_1","TS_3_2_1","EC_3_2_1",
"SWC_3_3_1","K_3_3_1","TS_3_3_1","EC_3_3_1",
"SWC_3_4_1","K_3_4_1","TS_3_4_1","EC_3_4_1",
"SWC_3_5_1","K_3_5_1","TS_3_5_1","EC_3_5_1",
"SWC_3_6_1","K_3_6_1","TS_3_6_1","EC_3_6_1",
"SWC_3_7_1","K_3_7_1","TS_3_7_1","EC_3_7_1",
"SWC_3_8_1","K_3_8_1","TS_3_8_1","EC_3_8_1",
"SWC_3_9_1","K_3_9_1","TS_3_9_1","EC_3_9_1",]

bflat = list(filter(lambda item: item not in('TA_1_1_4','TS_1_1_2','SWC_1_1_2'),default))
wellington = list(filter(lambda item: item not in('TS_1_1_1'),default))
math_soils = ["VWC_5cm_N_Avg", "VWC_5cm_S_Avg", "Ka_5cm_N_Avg", "T_5cm_N_Avg", "BulkEC_5cm_N_Avg", "VWC_10cm_N_Avg",
"Ka_10cm_N_Avg", "T_10cm_N_Avg", "BulkEC_10cm_N_Avg", "VWC_20cm_N_Avg", "Ka_20cm_N_Avg", "T_20cm_N_Avg",
"BulkEC_20cm_N_Avg", "VWC_30cm_N_Avg", "Ka_30cm_N_Avg", "T_30cm_N_Avg", "BulkEC_30cm_N_Avg",
"VWC_40cm_N_Avg", "Ka_40cm_N_Avg", "T_40cm_N_Avg", "BulkEC_40cm_N_Avg", "VWC_50cm_N_Avg", "Ka_50cm_N_Avg",
"T_50cm_N_Avg", "BulkEC_50cm_N_Avg", "VWC_60cm_N_Avg", "Ka_60cm_N_Avg", "T_60cm_N_Avg",
"BulkEC_60cm_N_Avg", "VWC_75cm_N_Avg", "Ka_75cm_N_Avg", "T_75cm_N_Avg", "BulkEC_75cm_N_Avg",
"VWC_100cm_N_Avg", "Ka_100cm_N_Avg", "T_100cm_N_Avg", "BulkEC_100cm_N_Avg", "Ka_5cm_S_Avg", "T_5cm_S_Avg",
"BulkEC_5cm_S_Avg", "VWC_10cm_S_Avg", "Ka_10cm_S_Avg", "T_10cm_S_Avg", "BulkEC_10cm_S_Avg",
"VWC_20cm_S_Avg", "Ka_20cm_S_Avg", "T_20cm_S_Avg", "BulkEC_20cm_S_Avg", "VWC_30cm_S_Avg", "Ka_30cm_S_Avg",
"T_30cm_S_Avg", "BulkEC_30cm_S_Avg", "VWC_40cm_S_Avg", "Ka_40cm_S_Avg", "T_40cm_S_Avg",
"BulkEC_40cm_S_Avg", "VWC_50cm_S_Avg", "Ka_50cm_S_Avg", "T_50cm_S_Avg", "BulkEC_50cm_S_Avg",
"VWC_60cm_S_Avg", "Ka_60cm_S_Avg", "T_60cm_S_Avg", "BulkEC_60cm_S_Avg", "VWC_75cm_S_Avg", "Ka_75cm_S_Avg",
"T_75cm_S_Avg", "BulkEC_75cm_S_Avg", "VWC_100cm_S_Avg", "Ka_100cm_S_Avg", "T_100cm_S_Avg"]

math_soils_v2 = ["SWC_3_1_1", "SWC_4_1_1", "K_3_1_1", "TS_3_1_1", "EC_3_1_1", "SWC_3_2_1", "K_3_2_1", "TS_3_2_1",
"EC_3_2_1", "SWC_3_3_1", "K_3_3_1", "TS_3_3_1", "EC_3_3_1", "SWC_3_4_1", "K_3_4_1", "TS_3_4_1",
"EC_3_4_1", "SWC_3_5_1", "K_3_5_1", "TS_3_5_1", "EC_3_5_1", "SWC_3_6_1", "K_3_6_1", "TS_3_6_1",
"EC_3_6_1", "SWC_3_7_1", "K_3_7_1", "TS_3_7_1", "EC_3_7_1", "SWC_3_8_1", "K_3_8_1", "TS_3_8_1",
"EC_3_8_1", "SWC_3_9_1", "K_3_9_1", "TS_3_9_1", "EC_3_9_1", "K_4_1_1", "TS_4_1_1", "EC_4_1_1",
"SWC_4_2_1", "K_4_2_1", "TS_4_2_1", "EC_4_2_1", "SWC_4_3_1", "K_4_3_1", "TS_4_3_1", "EC_4_3_1",
"SWC_4_4_1", "K_4_4_1", "TS_4_4_1", "EC_4_4_1", "SWC_4_5_1", "K_4_5_1", "TS_4_5_1", "EC_4_5_1",
"SWC_4_6_1", "K_4_6_1", "TS_4_6_1", "EC_4_6_1", "SWC_4_7_1", "K_4_7_1", "TS_4_7_1", "EC_4_7_1",
"SWC_4_8_1", "K_4_8_1", "TS_4_8_1", "EC_4_8_1", "EC_4_9_1", "SWC_4_9_1", "K_4_9_1", "TS_4_9_1",
"TS_1_1_1", "TS_2_1_1", "SWC_1_1_1", "SWC_2_1_1"]

well_soils = ["SWC_3_1_1", "SWC_4_1_1", "K_3_1_1", "TS_3_1_1", "EC_3_1_1",
"SWC_3_2_1", "K_3_2_1", "TS_3_2_1", "EC_3_2_1",
"SWC_3_3_1", "K_3_3_1", "TS_3_3_1", "EC_3_3_1",
"SWC_3_4_1", "K_3_4_1", "TS_3_4_1", "EC_3_4_1",
"SWC_3_5_1", "K_3_5_1", "TS_3_5_1", "EC_3_5_1",
"SWC_3_6_1", "K_3_6_1", "TS_3_6_1", "EC_3_6_1",
"SWC_3_7_1", "K_3_7_1", "TS_3_7_1", "EC_3_7_1",
"SWC_3_8_1", "K_3_8_1", "TS_3_8_1", "EC_3_8_1",
"SWC_3_9_1", "K_3_9_1", "TS_3_9_1", "EC_3_9_1", ]

bflat = list(filter(lambda item: item not in ('TA_1_1_4', 'TS_1_1_2', 'SWC_1_1_2'), default))
wellington = list(filter(lambda item: item not in ('TS_1_1_1'), default))
big_math = wellington[:-10] + math_soils + wellington[-10:] + ['T_CANOPY']
big_math_v2 = wellington[:-10] + math_soils_v2 + wellington[-7:] + ['T_CANOPY']
big_math_v2_filt = list(filter(lambda item: item not in('TA_1_1_4'),big_math_v2))
big_math_v2_filt = list(filter(lambda item: item not in ('TA_1_1_4'), big_math_v2))

big_well = list(filter(lambda item: item not in ('TA_1_1_4'), default)) + well_soils
header_dict = {60: default, 57: bflat, 59: wellington, 96: big_well, 131: big_math, 132: big_math_v2_filt}

big_well = list(filter(lambda item: item not in('TA_1_1_4'),default)) + well_soils
header_dict = {60:default, 57:bflat, 59:wellington, 96:big_well, 131:big_math, 132:big_math_v2_filt}

def check_header(csv_file):
"""
Expand Down Expand Up @@ -142,13 +164,13 @@ def dataframe_from_file(file):
df = pd.read_csv(file, na_values=['-9999', 'NAN', 'NaN'])
elif check_header(file) == 2:

df = pd.read_csv(file, na_values=['-9999', 'NAN', 'NaN'], skiprows=[0,2,3])
df = pd.read_csv(file, na_values=['-9999', 'NAN', 'NaN'], skiprows=[0, 2, 3])
print(file)
if "TIMESTAMP" in df.columns:
df.drop(['TIMESTAMP'], axis=1, inplace=True)
else:
# count the number of columns in the first row of headerless data
first_line = pd.read_csv(file, header=None, nrows=1,na_values=['-9999', 'NAN', 'NaN'])
first_line = pd.read_csv(file, header=None, nrows=1, na_values=['-9999', 'NAN', 'NaN'])
fll = len(first_line.columns)
# match header count (key) to key in dictionary
header = header_dict[fll]
Expand All @@ -160,7 +182,7 @@ def dataframe_from_file(file):
return None


def raw_file_compile(raw_fold, station_folder_name, search_str = "*Flux_AmeriFluxFormat*.dat"):
def raw_file_compile(raw_fold, station_folder_name, search_str="*Flux_AmeriFluxFormat*.dat"):
"""
Compiles raw AmeriFlux datalogger files into a single dataframe.
Expand Down Expand Up @@ -197,6 +219,7 @@ def raw_file_compile(raw_fold, station_folder_name, search_str = "*Flux_AmeriFlu
et_data = None
return et_data


def remove_extra_soil_params(df):
"""
Removes extra soil parameters from the given dataframe.
Expand All @@ -212,14 +235,14 @@ def remove_extra_soil_params(df):
main_var = collist[0]

# get rid of columns that don't follow the typical pattern
if len(collist) > 3 and collist[2] not in ['N','S']:
if len(collist) > 3 and collist[2] not in ['N', 'S']:
depth_var = int(collist[2])
if main_var in ['SWC','TS','EC','K'] and (depth_var >= 1 and int(collist[1]) >= 3):
if main_var in ['SWC', 'TS', 'EC', 'K'] and (depth_var >= 1 and int(collist[1]) >= 3):
df.drop(col, axis=1, inplace=True)
# drop cols from a specified list math_soils_v2
elif col in math_soils_v2[:-4]:
df.drop(col, axis=1, inplace=True)
elif main_var in ['VWC','Ka'] or 'cm_N' in col or 'cm_S' in col:
elif main_var in ['VWC', 'Ka'] or 'cm_N' in col or 'cm_S' in col:
df.drop(col, axis=1, inplace=True)
return df

Expand All @@ -246,37 +269,38 @@ class Reformatter(object):
# dictionary to fix naming convention issues with EasyFluxDL;
# https://ameriflux.lbl.gov/wp-content/uploads/2015/10/AmeriFlux_DataVariables.pdf
col_name_match = {"TA_1_1_2": "TA_1_2_1",
"RH_1_1_2": "RH_1_2_1",
"T_DP_1_1_2": "T_DP_1_2_1",
"TA_1_1_3": "TA_1_3_1",
"RH_1_1_3": "RH_1_3_1",
"T_DP_1_1_3": "T_DP_1_3_1",
"RH_1_1_2": "RH_1_2_1",
"T_DP_1_1_2": "T_DP_1_2_1",
"TA_1_1_3": "TA_1_3_1",
"RH_1_1_3": "RH_1_3_1",
"T_DP_1_1_3": "T_DP_1_3_1",
"TA_2_1_1": "TA_1_2_1",
"RH_2_1_1": "RH_1_2_1",
"T_DP_2_1_1": "T_DP_1_2_1",
"TA_3_1_1": "TA_1_3_1",
"RH_3_1_1": "RH_1_3_1",
"TA_1_1_4": "TA_1_4_1",
"T_DP_3_1_1": "T_DP_1_3_1",
"PBLH": "PBLH_F",
"TS_1_1_2": "TS_2_1_1",
"SWC_1_1_2": "SWC_2_1_1"}
"PBLH": "PBLH_F",
"TS_1_1_2": "TS_2_1_1",
"SWC_1_1_2": "SWC_2_1_1"}

# Variables to despike
despikey = ['CO2', 'H2O', 'FC', 'LE', 'ET', 'H', 'G', 'SG', 'FETCH_MAX', 'FETCH_90', 'FETCH_55', 'FETCH_40',
'WS',
'USTAR', 'TAU', 'MO_LENGTH', 'U', 'V', 'W', 'PA', 'TA_1_1_1', 'RH_1_1_1', 'T_DP_1_1_1',
'TA_1_2_1', 'RH_1_2_1', 'T_DP_1_2_1', 'TA_1_3_1', 'RH_1_3_1', 'T_DP_1_3_1', 'VPD', 'T_SONIC',
'PBLH', 'TS_1_1_1', 'TS_2_1_1', 'SWC_1_1_1', 'SWC_2_1_1', 'ALB', 'NETRAD', 'SW_IN', 'SW_OUT',
'LW_IN', 'LW_OUT']
'WS',
'USTAR', 'TAU', 'MO_LENGTH', 'U', 'V', 'W', 'PA', 'TA_1_1_1', 'RH_1_1_1', 'T_DP_1_1_1',
'TA_1_2_1', 'RH_1_2_1', 'T_DP_1_2_1', 'TA_1_3_1', 'RH_1_3_1', 'T_DP_1_3_1', 'VPD', 'T_SONIC',
'PBLH', 'TS_1_1_1', 'TS_2_1_1', 'SWC_1_1_1', 'SWC_2_1_1', 'ALB', 'NETRAD', 'SW_IN', 'SW_OUT',
'LW_IN', 'LW_OUT']

def __init__(self, et_data, drop_soil=True):
# read in variable limits
try:
data_path = pathlib.Path('../data/FP_variable_20220810.csv')
self.varlimits = pd.read_csv(data_path, index_col='Name')
except FileNotFoundError:
data_path = pathlib.Path('/content/drive/Shareddrives/UGS_Flux/Data_Processing/Jupyter_Notebooks/Micromet/data/FP_variable_20220810.csv')
data_path = pathlib.Path(
'/content/drive/Shareddrives/UGS_Flux/Data_Processing/Jupyter_Notebooks/Micromet/data/FP_variable_20220810.csv')
self.varlimits = pd.read_csv(data_path, index_col='Name')
# fix datetimes
self.et_data = self.datefixer(et_data)
Expand Down Expand Up @@ -320,8 +344,6 @@ def __init__(self, et_data, drop_soil=True):
else:
self.et_data[col] = self.et_data[col].astype(np.float32)



def datefixer(self, et_data):
"""
Fixes the date and time format in the given data.
Expand Down Expand Up @@ -366,7 +388,7 @@ def datefixer(self, et_data):
et_data = et_data[~et_data.index.duplicated(keep='first')]

# eliminate implausible dates that are set in the future
et_data = et_data[et_data.index<=datetime.datetime.today()+pd.Timedelta(days=1)]
et_data = et_data[et_data.index <= datetime.datetime.today() + pd.Timedelta(days=1)]

# fix time offsets to harmonize sample frequency to 30 min
et_data = et_data.resample('15min').asfreq().interpolate(method='linear', limit=2).resample(
Expand Down Expand Up @@ -416,7 +438,8 @@ def ssitc_scale(self):
ssitc_columns = ['FC_SSITC_TEST', 'LE_SSITC_TEST', 'ET_SSITC_TEST', 'H_SSITC_TEST', 'TAU_SSITC_TEST']
for column in ssitc_columns:
if column in self.et_data.columns:
self.et_data[column] = self.scale_and_convert(self.et_data[column])
if (self.et_data[column] > 3).any() > 2:
self.et_data[column] = self.scale_and_convert(self.et_data[column])

@staticmethod
def rating(x):
Expand All @@ -440,7 +463,6 @@ def rating(x):
x = 2
return x


def _extract_variable_name(self, variable):
"""
Extracts the variable name based on given variable.
Expand Down Expand Up @@ -497,7 +519,6 @@ def extreme_limiter(self, df, replace_w=np.nan):
df = self.replace_out_of_range_with_nan(df, variable, replace_w)
return df


def despike(self, arr, nstd: float = 4.5):
"""Removes spikes from an array of values based on a specified deviation from the mean.
Expand Down Expand Up @@ -542,6 +563,7 @@ def fix_swc_percent(self):
for col in self.et_data.columns:
if "SWC" in col and self.et_data[col].max() < 1.5:
self.et_data.loc[self.et_data[col] > -9999, col] *= 100

def timestamp_reset(self):
self.et_data['TIMESTAMP_START'] = self.et_data.index
self.et_data['TIMESTAMP_END'] = self.et_data.index + pd.Timedelta(minutes=30)
Expand Down Expand Up @@ -575,39 +597,35 @@ def despike_ewma_fb(self, df_column, span, delta):
return np_remove_outliers


main_header_part = ['TIMESTAMP_START', 'TIMESTAMP_END', 'CO2', 'CO2_SIGMA', 'H2O', 'H2O_SIGMA', 'FC', 'FC_SSITC_TEST',
'LE',
'LE_SSITC_TEST', 'ET', 'ET_SSITC_TEST', 'H', 'H_SSITC_TEST', 'G', 'SG', 'FETCH_MAX', 'FETCH_90',
'FETCH_55',
'FETCH_40', 'WD', 'WS', 'WS_MAX', 'USTAR', 'ZL', 'TAU', 'TAU_SSITC_TEST', 'MO_LENGTH', 'U',
'U_SIGMA', 'V',
'V_SIGMA', 'W', 'W_SIGMA', 'PA', 'TA_1_1_1', 'RH_1_1_1', 'T_DP_1_1_1', ]




main_header_part = ['TIMESTAMP_START','TIMESTAMP_END','CO2','CO2_SIGMA','H2O','H2O_SIGMA','FC','FC_SSITC_TEST','LE',
'LE_SSITC_TEST','ET','ET_SSITC_TEST','H','H_SSITC_TEST','G','SG','FETCH_MAX','FETCH_90','FETCH_55',
'FETCH_40','WD','WS','WS_MAX','USTAR','ZL','TAU','TAU_SSITC_TEST','MO_LENGTH','U','U_SIGMA','V',
'V_SIGMA','W','W_SIGMA','PA','TA_1_1_1','RH_1_1_1','T_DP_1_1_1',]


bet_part = ['TA_1_2_1','RH_1_2_1','T_DP_1_2_1',
'TA_1_3_1','RH_1_3_1','T_DP_1_3_1','VPD','T_SONIC','T_SONIC_SIGMA','PBLH','TS_1_1_1','SWC_1_1_1',
'ALB','NETRAD','SW_IN','SW_OUT','LW_IN','LW_OUT','P']
bet_part = ['TA_1_2_1', 'RH_1_2_1', 'T_DP_1_2_1',
'TA_1_3_1', 'RH_1_3_1', 'T_DP_1_3_1', 'VPD', 'T_SONIC', 'T_SONIC_SIGMA', 'PBLH', 'TS_1_1_1', 'SWC_1_1_1',
'ALB', 'NETRAD', 'SW_IN', 'SW_OUT', 'LW_IN', 'LW_OUT', 'P']
bet_header = main_header_part + bet_part

met_headers = ["TIMESTAMP_START","TIMESTAMP_END","CO2","CO2_SIGMA","H2O","H2O_SIGMA","FC","FC_SSITC_TEST","LE",
"LE_SSITC_TEST","ET","ET_SSITC_TEST","H","H_SSITC_TEST","G","SG","FETCH_MAX","FETCH_90","FETCH_55",
"FETCH_40","WD","WS","WS_MAX","USTAR","ZL","TAU","TAU_SSITC_TEST","MO_LENGTH","U","U_SIGMA","V",
"V_SIGMA","W","W_SIGMA","PA","TA_1_1_1","RH_1_1_1","T_DP_1_1_1","TA_1_1_2","RH_1_1_2","T_DP_1_1_2",
"TA_1_1_3","RH_1_1_3","T_DP_1_1_3","VPD","T_SONIC","T_SONIC_SIGMA","PBLH","TS_1_1_1","TS_1_1_2",
"SWC_1_1_1","SWC_1_1_2","ALB","NETRAD","SW_IN","SW_OUT","LW_IN","LW_OUT","P"]





bet_spikey = ['CO2','H2O','FC','LE', 'ET','H','G','SG','FETCH_MAX','FETCH_90','FETCH_55','FETCH_40','WS','USTAR',
'TAU','MO_LENGTH','U','V','W','PA','TA_1_1_1','RH_1_1_1','T_DP_1_1_1',
'TA_1_2_1','RH_1_2_1','T_DP_1_2_1','TA_1_3_1','RH_1_3_1','T_DP_1_3_1','VPD','T_SONIC','T_SONIC_SIGMA',
'PBLH_F','TS_1_1_1','SWC_1_1_1','ALB','NETRAD','SW_IN','SW_OUT','LW_IN','LW_OUT']


met_headers = ["TIMESTAMP_START", "TIMESTAMP_END", "CO2", "CO2_SIGMA", "H2O", "H2O_SIGMA", "FC", "FC_SSITC_TEST", "LE",
"LE_SSITC_TEST", "ET", "ET_SSITC_TEST", "H", "H_SSITC_TEST", "G", "SG", "FETCH_MAX", "FETCH_90",
"FETCH_55",
"FETCH_40", "WD", "WS", "WS_MAX", "USTAR", "ZL", "TAU", "TAU_SSITC_TEST", "MO_LENGTH", "U", "U_SIGMA",
"V",
"V_SIGMA", "W", "W_SIGMA", "PA", "TA_1_1_1", "RH_1_1_1", "T_DP_1_1_1", "TA_1_1_2", "RH_1_1_2",
"T_DP_1_1_2",
"TA_1_1_3", "RH_1_1_3", "T_DP_1_1_3", "VPD", "T_SONIC", "T_SONIC_SIGMA", "PBLH", "TS_1_1_1", "TS_1_1_2",
"SWC_1_1_1", "SWC_1_1_2", "ALB", "NETRAD", "SW_IN", "SW_OUT", "LW_IN", "LW_OUT", "P"]

bet_spikey = ['CO2', 'H2O', 'FC', 'LE', 'ET', 'H', 'G', 'SG', 'FETCH_MAX', 'FETCH_90', 'FETCH_55', 'FETCH_40', 'WS',
'USTAR',
'TAU', 'MO_LENGTH', 'U', 'V', 'W', 'PA', 'TA_1_1_1', 'RH_1_1_1', 'T_DP_1_1_1',
'TA_1_2_1', 'RH_1_2_1', 'T_DP_1_2_1', 'TA_1_3_1', 'RH_1_3_1', 'T_DP_1_3_1', 'VPD', 'T_SONIC',
'T_SONIC_SIGMA',
'PBLH_F', 'TS_1_1_1', 'SWC_1_1_1', 'ALB', 'NETRAD', 'SW_IN', 'SW_OUT', 'LW_IN', 'LW_OUT']


def load_data():
Expand All @@ -616,4 +634,4 @@ def load_data():


if __name__ == '__main__':
data = load_data()
data = load_data()

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