nocollier

import numpy as np
from netCDF4 import Dataset
import pylab as plt
import os
import time
from urllib.request import urlretrieve

remote_source = "http://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv"
gist_source = "https://gist.github.com/nocollier/d73585731756fa472731065389af45dc"
local_source = os.path.basename(remote_source)

nocollier

"""
Converts csv files downloaded from the Ameriflux archive into a CF-compliant netCDF4 file.
"""
import argparse
import glob
import re
import xlrd
import numpy as np
import cftime as cf
from netCDF4 import Dataset

nocollier

from cf_units import Unit
import xarray as xr
import numpy as np

@xr.register_dataarray_accessor('ilamb')
class ilamb_variable:
    def __init__(self, xarray_obj):
        self._obj = xarray_obj
        self.measure = None
        self.bounds = None

nocollier

import sys
import os
sys.path.insert(0, os.path.abspath('config'))
import configure

mode = 'debug'
if len(sys.argv) == 2: mode = sys.argv[1]

configure_options = [
  '--download-exodusii',

nocollier

import argparse
import glob
from netCDF4 import Dataset
import numpy as np
import xlrd,os,time

remote_source = "doi:10.18160/GCP-2019"
gist_source = "https://gist.github.com/nocollier/6f848e5b643d6f83537ac5a9bab0f9e0"
local_source = "Global_Carbon_Budget_2019v1.0.xlsx"
stamp = time.strftime('%Y-%m-%d', time.localtime(os.path.getmtime(local_source)))

nocollier

nocollier

description: "The Physiological Basis for Estimating Photosynthesis from
Chlorophyll a Fluorescence. Please consult
[here](https://ecommons.cornell.edu/handle/1813/110978) for more information
regarding this data. Columns in each source are species, Tleaf (leaf temperature
in °C), LeafReplicateNumber (replicate number for individual leaf), PARi
(photosynthetically active radiation in µmol m-2 s-1), CO2R (reference CO2
concentration in µmol mol-1), Ci (intercellular CO2 concentration in µmol
mol-1), Anet (net photosynthesis in µmol m-2 s-1), Fm' (maximum chlorophyll a
fluorescence under light in mV), Fs (steady-state chlorophyll a fluorescence
under light in mV), VpdL (leaf-to-air vapour pressure deficit in kPa), Press

nocollier

import re

import pandas as pd
from globus_sdk import SearchClient
from globus_sdk.response import GlobusHTTPResponse

# the Globus uuid of the test ESGF2 Argonne index
INDEX_ID = "d927e2d9-ccdb-48e4-b05d-adbc3d97bbc5"

nocollier

"""ESGF could consider maintaining a database of mean and standard deivations for each
variable in each experiment/table. There should be a public API to this so tools could
be created to test model data that is to be submitted. For example, a z-statistic could
be used:

z = | (submission_mean - esgf_mean) / sqrt(submission_std**2 + esgf_std**2) |

If z < 2, then the distributions are to be considered similar. Perhaps there are better
statistical tests that could be performed if ESGF does the work of providing these
important scalars.

nocollier

import globus_sdk as sdk

# I created a client_uuid for intake-esgf
CLIENT_ID = "81a13009-8326-456e-a487-2d1557d8eb11"
client = sdk.NativeAppAuthClient(CLIENT_ID)

# Authorize our interactions with Globus.
client.oauth2_start_flow()
authorize_url = client.oauth2_get_authorize_url()
print(