#file18 = "https://cires.s3-us-west-2.amazonaws.com/GU1002/18/149018.csv"
file18 = "https://cires.s3-us-west-2.amazonaws.com/SH1305/18/SaKe_2013-D20130622-T220228_to_SaKe_2013-D20130622-T224822.csv"
#file38 = "https://cires.s3-us-west-2.amazonaws.com/GU1002/38/149018.csv"
#file120 = "https://cires.s3-us-west-2.amazonaws.com/GU1002/120/149018.csv"
#file200 = "https://cires.s3-us-west-2.amazonaws.com/GU1002/200/149018.csv"
file18
Raw CSV to Python Numpy N-Dimensional Array
df18 = pd.read_csv(file18, header=1, na_values='NA', skiprows=0)
#df38 = pd.read_csv(file38, header=None, na_values='NA', skiprows=0)
#df120 = pd.read_csv(file120, header=None, na_values='NA', skiprows=0)
#df200 = pd.read_csv(file200, header=None, na_values='NA', skiprows=0)
print('type:', type(df18))
df18
data18 = df18.iloc[:, 4:].to_numpy()
data38 = df38.iloc[:, 4:].to_numpy()
data120 = df120.iloc[:, 4:].to_numpy()
data200 = df200.iloc[:, 4:].to_numpy()
print(data18.shape)
print('type:', type(data18))
The next step is to convert the ek60 data to a numpy data array.
The data are now available in a numpy ndarray format, the next step is to convert the data to a python xarray object.