How to save a dataframe to a netcdf file
31 Mar 2019How to save a txt file to Netcdf file format
- Read one daily station file from USCRN
- Save it as a Netcdf file with dimension (time, station)
import xarray as xr
import pandas as pd
# An example of USCRN station data
uscrn_file = 'https://www1.ncdc.noaa.gov/pub/data/uscrn/products/daily01/2019/CRND0103-2019-AK_Bethel_87_WNW.txt'
uscrn_header_file = 'ftp://ftp.ncdc.noaa.gov/pub/data/uscrn/products/daily01/HEADERS.txt'
# Read header file
uscrn_header = pd.read_csv(uscrn_header_file, sep='\s+')
# Read data file
uscrn_df = pd.read_csv(uscrn_file, sep='\s+', header=None)
uscrn_header
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | ... | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | WBANNO | LST_DATE | CRX_VN | LONGITUDE | LATITUDE | T_DAILY_MAX | T_DAILY_MIN | T_DAILY_MEAN | T_DAILY_AVG | P_DAILY_CALC | ... | SOIL_MOISTURE_5_DAILY | SOIL_MOISTURE_10_DAILY | SOIL_MOISTURE_20_DAILY | SOIL_MOISTURE_50_DAILY | SOIL_MOISTURE_100_DAILY | SOIL_TEMP_5_DAILY | SOIL_TEMP_10_DAILY | SOIL_TEMP_20_DAILY | SOIL_TEMP_50_DAILY | SOIL_TEMP_100_DAILY |
| 1 | XXXXX | YYYYMMDD | XXXXXX | Decimal_degrees | Decimal_degrees | Celsius | Celsius | Celsius | Celsius | mm | ... | m^3/m^3 | m^3/m^3 | m^3/m^3 | m^3/m^3 | m^3/m^3 | Celsius | Celsius | Celsius | Celsius | Celsius |
2 rows × 28 columns
uscrn_df.head(5)
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 26656 | 20190101 | 2.515 | -164.08 | 61.35 | 0.7 | -12.4 | -5.8 | -5.1 | 0.0 | ... | -99.0 | -99.0 | -99.0 | -99.0 | -99.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 |
| 1 | 26656 | 20190102 | 2.515 | -164.08 | 61.35 | -12.3 | -17.0 | -14.7 | -14.4 | 0.0 | ... | -99.0 | -99.0 | -99.0 | -99.0 | -99.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 |
| 2 | 26656 | 20190103 | 2.515 | -164.08 | 61.35 | -9.9 | -14.0 | -12.0 | -11.8 | 0.0 | ... | -99.0 | -99.0 | -99.0 | -99.0 | -99.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 |
| 3 | 26656 | 20190104 | 2.515 | -164.08 | 61.35 | -7.6 | -12.3 | -9.9 | -10.4 | 0.0 | ... | -99.0 | -99.0 | -99.0 | -99.0 | -99.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 |
| 4 | 26656 | 20190105 | 2.515 | -164.08 | 61.35 | -8.2 | -14.1 | -11.2 | -11.5 | 0.0 | ... | -99.0 | -99.0 | -99.0 | -99.0 | -99.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 |
5 rows × 28 columns
# Set uscrn_df column name using uscrn_header information
uscrn_df.columns = uscrn_header.iloc[0, :]
uscrn_df.head(5)
| WBANNO | LST_DATE | CRX_VN | LONGITUDE | LATITUDE | T_DAILY_MAX | T_DAILY_MIN | T_DAILY_MEAN | T_DAILY_AVG | P_DAILY_CALC | ... | SOIL_MOISTURE_5_DAILY | SOIL_MOISTURE_10_DAILY | SOIL_MOISTURE_20_DAILY | SOIL_MOISTURE_50_DAILY | SOIL_MOISTURE_100_DAILY | SOIL_TEMP_5_DAILY | SOIL_TEMP_10_DAILY | SOIL_TEMP_20_DAILY | SOIL_TEMP_50_DAILY | SOIL_TEMP_100_DAILY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 26656 | 20190101 | 2.515 | -164.08 | 61.35 | 0.7 | -12.4 | -5.8 | -5.1 | 0.0 | ... | -99.0 | -99.0 | -99.0 | -99.0 | -99.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 |
| 1 | 26656 | 20190102 | 2.515 | -164.08 | 61.35 | -12.3 | -17.0 | -14.7 | -14.4 | 0.0 | ... | -99.0 | -99.0 | -99.0 | -99.0 | -99.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 |
| 2 | 26656 | 20190103 | 2.515 | -164.08 | 61.35 | -9.9 | -14.0 | -12.0 | -11.8 | 0.0 | ... | -99.0 | -99.0 | -99.0 | -99.0 | -99.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 |
| 3 | 26656 | 20190104 | 2.515 | -164.08 | 61.35 | -7.6 | -12.3 | -9.9 | -10.4 | 0.0 | ... | -99.0 | -99.0 | -99.0 | -99.0 | -99.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 |
| 4 | 26656 | 20190105 | 2.515 | -164.08 | 61.35 | -8.2 | -14.1 | -11.2 | -11.5 | 0.0 | ... | -99.0 | -99.0 | -99.0 | -99.0 | -99.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 |
5 rows × 28 columns
# Drop constant variables
uscrn_df_clean = uscrn_df.drop(['WBANNO', 'LONGITUDE', 'LATITUDE', 'CRX_VN'], axis=1)
uscrn_df_clean.head(5)
| LST_DATE | T_DAILY_MAX | T_DAILY_MIN | T_DAILY_MEAN | T_DAILY_AVG | P_DAILY_CALC | SOLARAD_DAILY | SUR_TEMP_DAILY_TYPE | SUR_TEMP_DAILY_MAX | SUR_TEMP_DAILY_MIN | ... | SOIL_MOISTURE_5_DAILY | SOIL_MOISTURE_10_DAILY | SOIL_MOISTURE_20_DAILY | SOIL_MOISTURE_50_DAILY | SOIL_MOISTURE_100_DAILY | SOIL_TEMP_5_DAILY | SOIL_TEMP_10_DAILY | SOIL_TEMP_20_DAILY | SOIL_TEMP_50_DAILY | SOIL_TEMP_100_DAILY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 20190101 | 0.7 | -12.4 | -5.8 | -5.1 | 0.0 | 0.26 | C | -1.1 | -12.4 | ... | -99.0 | -99.0 | -99.0 | -99.0 | -99.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 |
| 1 | 20190102 | -12.3 | -17.0 | -14.7 | -14.4 | 0.0 | 0.68 | C | -12.4 | -21.3 | ... | -99.0 | -99.0 | -99.0 | -99.0 | -99.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 |
| 2 | 20190103 | -9.9 | -14.0 | -12.0 | -11.8 | 0.0 | 0.17 | C | -9.9 | -15.0 | ... | -99.0 | -99.0 | -99.0 | -99.0 | -99.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 |
| 3 | 20190104 | -7.6 | -12.3 | -9.9 | -10.4 | 0.0 | 0.25 | C | -7.7 | -13.3 | ... | -99.0 | -99.0 | -99.0 | -99.0 | -99.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 |
| 4 | 20190105 | -8.2 | -14.1 | -11.2 | -11.5 | 0.0 | 0.48 | C | -8.8 | -14.3 | ... | -99.0 | -99.0 | -99.0 | -99.0 | -99.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 |
5 rows × 24 columns
# Set LST_DATE as the dataframe index
uscrn_df_clean = uscrn_df_clean.set_index('LST_DATE')
uscrn_df_clean.head(5)
| T_DAILY_MAX | T_DAILY_MIN | T_DAILY_MEAN | T_DAILY_AVG | P_DAILY_CALC | SOLARAD_DAILY | SUR_TEMP_DAILY_TYPE | SUR_TEMP_DAILY_MAX | SUR_TEMP_DAILY_MIN | SUR_TEMP_DAILY_AVG | ... | SOIL_MOISTURE_5_DAILY | SOIL_MOISTURE_10_DAILY | SOIL_MOISTURE_20_DAILY | SOIL_MOISTURE_50_DAILY | SOIL_MOISTURE_100_DAILY | SOIL_TEMP_5_DAILY | SOIL_TEMP_10_DAILY | SOIL_TEMP_20_DAILY | SOIL_TEMP_50_DAILY | SOIL_TEMP_100_DAILY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| LST_DATE | |||||||||||||||||||||
| 20190101 | 0.7 | -12.4 | -5.8 | -5.1 | 0.0 | 0.26 | C | -1.1 | -12.4 | -5.3 | ... | -99.0 | -99.0 | -99.0 | -99.0 | -99.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 |
| 20190102 | -12.3 | -17.0 | -14.7 | -14.4 | 0.0 | 0.68 | C | -12.4 | -21.3 | -16.4 | ... | -99.0 | -99.0 | -99.0 | -99.0 | -99.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 |
| 20190103 | -9.9 | -14.0 | -12.0 | -11.8 | 0.0 | 0.17 | C | -9.9 | -15.0 | -11.8 | ... | -99.0 | -99.0 | -99.0 | -99.0 | -99.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 |
| 20190104 | -7.6 | -12.3 | -9.9 | -10.4 | 0.0 | 0.25 | C | -7.7 | -13.3 | -10.5 | ... | -99.0 | -99.0 | -99.0 | -99.0 | -99.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 |
| 20190105 | -8.2 | -14.1 | -11.2 | -11.5 | 0.0 | 0.48 | C | -8.8 | -14.3 | -11.9 | ... | -99.0 | -99.0 | -99.0 | -99.0 | -99.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 | -9999.0 |
5 rows × 23 columns
# Use xarray function to convert dataframe into dataset
uscrn_ds = xr.Dataset.from_dataframe(uscrn_df_clean)
uscrn_ds
<xarray.Dataset>
Dimensions: (LST_DATE: 89)
Coordinates:
* LST_DATE (LST_DATE) int64 20190101 20190102 ... 20190330
Data variables:
T_DAILY_MAX (LST_DATE) float64 0.7 -12.3 -9.9 ... 0.0 0.0
T_DAILY_MIN (LST_DATE) float64 -12.4 -17.0 -14.0 ... -4.5 -2.5
T_DAILY_MEAN (LST_DATE) float64 -5.8 -14.7 -12.0 ... -2.3 -1.3
T_DAILY_AVG (LST_DATE) float64 -5.1 -14.4 -11.8 ... -1.4 -0.3
P_DAILY_CALC (LST_DATE) float64 0.0 0.0 0.0 ... 0.0 0.0
SOLARAD_DAILY (LST_DATE) float64 0.26 0.68 0.17 ... 2.02 0.57
SUR_TEMP_DAILY_TYPE (LST_DATE) object 'C' 'C' 'C' 'C' ... 'U' 'C' 'C'
SUR_TEMP_DAILY_MAX (LST_DATE) float64 -1.1 -12.4 -9.9 ... 6.5 3.6
SUR_TEMP_DAILY_MIN (LST_DATE) float64 -12.4 -21.3 -15.0 ... -6.5 -3.6
SUR_TEMP_DAILY_AVG (LST_DATE) float64 -5.3 -16.4 -11.8 ... -0.2 0.1
RH_DAILY_MAX (LST_DATE) float64 97.6 92.8 ... -9.999e+03
RH_DAILY_MIN (LST_DATE) float64 79.3 82.0 ... -9.999e+03
RH_DAILY_AVG (LST_DATE) float64 92.0 87.4 ... -9.999e+03
SOIL_MOISTURE_5_DAILY (LST_DATE) float64 -99.0 -99.0 ... -99.0 -99.0
SOIL_MOISTURE_10_DAILY (LST_DATE) float64 -99.0 -99.0 ... -99.0 -99.0
SOIL_MOISTURE_20_DAILY (LST_DATE) float64 -99.0 -99.0 ... -99.0 -99.0
SOIL_MOISTURE_50_DAILY (LST_DATE) float64 -99.0 -99.0 ... -99.0 -99.0
SOIL_MOISTURE_100_DAILY (LST_DATE) float64 -99.0 -99.0 ... -99.0 -99.0
SOIL_TEMP_5_DAILY (LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_10_DAILY (LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_20_DAILY (LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_50_DAILY (LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_100_DAILY (LST_DATE) float64 -9.999e+03 ... -9.999e+03
# Add dropped constant ['WBANNO', 'LONGITUDE', 'LATITUDE', 'CRX_VN'] to xarray dataset
for x in ['WBANNO', 'LONGITUDE', 'LATITUDE', 'CRX_VN']:
uscrn_ds[x] = uscrn_df[x][0]
uscrn_ds
<xarray.Dataset>
Dimensions: (LST_DATE: 89)
Coordinates:
* LST_DATE (LST_DATE) int64 20190101 20190102 ... 20190330
Data variables:
T_DAILY_MAX (LST_DATE) float64 0.7 -12.3 -9.9 ... 0.0 0.0
T_DAILY_MIN (LST_DATE) float64 -12.4 -17.0 -14.0 ... -4.5 -2.5
T_DAILY_MEAN (LST_DATE) float64 -5.8 -14.7 -12.0 ... -2.3 -1.3
T_DAILY_AVG (LST_DATE) float64 -5.1 -14.4 -11.8 ... -1.4 -0.3
P_DAILY_CALC (LST_DATE) float64 0.0 0.0 0.0 ... 0.0 0.0
SOLARAD_DAILY (LST_DATE) float64 0.26 0.68 0.17 ... 2.02 0.57
SUR_TEMP_DAILY_TYPE (LST_DATE) object 'C' 'C' 'C' 'C' ... 'U' 'C' 'C'
SUR_TEMP_DAILY_MAX (LST_DATE) float64 -1.1 -12.4 -9.9 ... 6.5 3.6
SUR_TEMP_DAILY_MIN (LST_DATE) float64 -12.4 -21.3 -15.0 ... -6.5 -3.6
SUR_TEMP_DAILY_AVG (LST_DATE) float64 -5.3 -16.4 -11.8 ... -0.2 0.1
RH_DAILY_MAX (LST_DATE) float64 97.6 92.8 ... -9.999e+03
RH_DAILY_MIN (LST_DATE) float64 79.3 82.0 ... -9.999e+03
RH_DAILY_AVG (LST_DATE) float64 92.0 87.4 ... -9.999e+03
SOIL_MOISTURE_5_DAILY (LST_DATE) float64 -99.0 -99.0 ... -99.0 -99.0
SOIL_MOISTURE_10_DAILY (LST_DATE) float64 -99.0 -99.0 ... -99.0 -99.0
SOIL_MOISTURE_20_DAILY (LST_DATE) float64 -99.0 -99.0 ... -99.0 -99.0
SOIL_MOISTURE_50_DAILY (LST_DATE) float64 -99.0 -99.0 ... -99.0 -99.0
SOIL_MOISTURE_100_DAILY (LST_DATE) float64 -99.0 -99.0 ... -99.0 -99.0
SOIL_TEMP_5_DAILY (LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_10_DAILY (LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_20_DAILY (LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_50_DAILY (LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_100_DAILY (LST_DATE) float64 -9.999e+03 ... -9.999e+03
WBANNO int64 26656
LONGITUDE float64 -164.1
LATITUDE float64 61.35
CRX_VN float64 2.515
# Set time and station as xarray dataset coordinates
uscrn_ds.set_coords(['LST_DATE', 'WBANNO'])
<xarray.Dataset>
Dimensions: (LST_DATE: 89)
Coordinates:
* LST_DATE (LST_DATE) int64 20190101 20190102 ... 20190330
WBANNO int64 26656
Data variables:
T_DAILY_MAX (LST_DATE) float64 0.7 -12.3 -9.9 ... 0.0 0.0
T_DAILY_MIN (LST_DATE) float64 -12.4 -17.0 -14.0 ... -4.5 -2.5
T_DAILY_MEAN (LST_DATE) float64 -5.8 -14.7 -12.0 ... -2.3 -1.3
T_DAILY_AVG (LST_DATE) float64 -5.1 -14.4 -11.8 ... -1.4 -0.3
P_DAILY_CALC (LST_DATE) float64 0.0 0.0 0.0 ... 0.0 0.0
SOLARAD_DAILY (LST_DATE) float64 0.26 0.68 0.17 ... 2.02 0.57
SUR_TEMP_DAILY_TYPE (LST_DATE) object 'C' 'C' 'C' 'C' ... 'U' 'C' 'C'
SUR_TEMP_DAILY_MAX (LST_DATE) float64 -1.1 -12.4 -9.9 ... 6.5 3.6
SUR_TEMP_DAILY_MIN (LST_DATE) float64 -12.4 -21.3 -15.0 ... -6.5 -3.6
SUR_TEMP_DAILY_AVG (LST_DATE) float64 -5.3 -16.4 -11.8 ... -0.2 0.1
RH_DAILY_MAX (LST_DATE) float64 97.6 92.8 ... -9.999e+03
RH_DAILY_MIN (LST_DATE) float64 79.3 82.0 ... -9.999e+03
RH_DAILY_AVG (LST_DATE) float64 92.0 87.4 ... -9.999e+03
SOIL_MOISTURE_5_DAILY (LST_DATE) float64 -99.0 -99.0 ... -99.0 -99.0
SOIL_MOISTURE_10_DAILY (LST_DATE) float64 -99.0 -99.0 ... -99.0 -99.0
SOIL_MOISTURE_20_DAILY (LST_DATE) float64 -99.0 -99.0 ... -99.0 -99.0
SOIL_MOISTURE_50_DAILY (LST_DATE) float64 -99.0 -99.0 ... -99.0 -99.0
SOIL_MOISTURE_100_DAILY (LST_DATE) float64 -99.0 -99.0 ... -99.0 -99.0
SOIL_TEMP_5_DAILY (LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_10_DAILY (LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_20_DAILY (LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_50_DAILY (LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_100_DAILY (LST_DATE) float64 -9.999e+03 ... -9.999e+03
LONGITUDE float64 -164.1
LATITUDE float64 61.35
CRX_VN float64 2.515
# Expand dataset dimension from one to two
uscrn_ds.set_coords(['LST_DATE', 'WBANNO']).expand_dims('WBANNO')
<xarray.Dataset>
Dimensions: (LST_DATE: 89, WBANNO: 1)
Coordinates:
* LST_DATE (LST_DATE) int64 20190101 20190102 ... 20190330
* WBANNO (WBANNO) int64 26656
Data variables:
T_DAILY_MAX (WBANNO, LST_DATE) float64 0.7 -12.3 ... 0.0 0.0
T_DAILY_MIN (WBANNO, LST_DATE) float64 -12.4 -17.0 ... -2.5
T_DAILY_MEAN (WBANNO, LST_DATE) float64 -5.8 -14.7 ... -2.3 -1.3
T_DAILY_AVG (WBANNO, LST_DATE) float64 -5.1 -14.4 ... -1.4 -0.3
P_DAILY_CALC (WBANNO, LST_DATE) float64 0.0 0.0 0.0 ... 0.0 0.0
SOLARAD_DAILY (WBANNO, LST_DATE) float64 0.26 0.68 ... 2.02 0.57
SUR_TEMP_DAILY_TYPE (WBANNO, LST_DATE) object 'C' 'C' 'C' ... 'C' 'C'
SUR_TEMP_DAILY_MAX (WBANNO, LST_DATE) float64 -1.1 -12.4 ... 6.5 3.6
SUR_TEMP_DAILY_MIN (WBANNO, LST_DATE) float64 -12.4 -21.3 ... -3.6
SUR_TEMP_DAILY_AVG (WBANNO, LST_DATE) float64 -5.3 -16.4 ... -0.2 0.1
RH_DAILY_MAX (WBANNO, LST_DATE) float64 97.6 92.8 ... -9.999e+03
RH_DAILY_MIN (WBANNO, LST_DATE) float64 79.3 82.0 ... -9.999e+03
RH_DAILY_AVG (WBANNO, LST_DATE) float64 92.0 87.4 ... -9.999e+03
SOIL_MOISTURE_5_DAILY (WBANNO, LST_DATE) float64 -99.0 -99.0 ... -99.0
SOIL_MOISTURE_10_DAILY (WBANNO, LST_DATE) float64 -99.0 -99.0 ... -99.0
SOIL_MOISTURE_20_DAILY (WBANNO, LST_DATE) float64 -99.0 -99.0 ... -99.0
SOIL_MOISTURE_50_DAILY (WBANNO, LST_DATE) float64 -99.0 -99.0 ... -99.0
SOIL_MOISTURE_100_DAILY (WBANNO, LST_DATE) float64 -99.0 -99.0 ... -99.0
SOIL_TEMP_5_DAILY (WBANNO, LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_10_DAILY (WBANNO, LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_20_DAILY (WBANNO, LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_50_DAILY (WBANNO, LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_100_DAILY (WBANNO, LST_DATE) float64 -9.999e+03 ... -9.999e+03
LONGITUDE (WBANNO) float64 -164.1
LATITUDE (WBANNO) float64 61.35
CRX_VN (WBANNO) float64 2.515
# Make sure assign the results to a variable
uscrn_ds = uscrn_ds.set_coords(['LST_DATE', 'WBANNO']).expand_dims('WBANNO')
uscrn_ds
<xarray.Dataset>
Dimensions: (LST_DATE: 89, WBANNO: 1)
Coordinates:
* LST_DATE (LST_DATE) int64 20190101 20190102 ... 20190330
* WBANNO (WBANNO) int64 26656
Data variables:
T_DAILY_MAX (WBANNO, LST_DATE) float64 0.7 -12.3 ... 0.0 0.0
T_DAILY_MIN (WBANNO, LST_DATE) float64 -12.4 -17.0 ... -2.5
T_DAILY_MEAN (WBANNO, LST_DATE) float64 -5.8 -14.7 ... -2.3 -1.3
T_DAILY_AVG (WBANNO, LST_DATE) float64 -5.1 -14.4 ... -1.4 -0.3
P_DAILY_CALC (WBANNO, LST_DATE) float64 0.0 0.0 0.0 ... 0.0 0.0
SOLARAD_DAILY (WBANNO, LST_DATE) float64 0.26 0.68 ... 2.02 0.57
SUR_TEMP_DAILY_TYPE (WBANNO, LST_DATE) object 'C' 'C' 'C' ... 'C' 'C'
SUR_TEMP_DAILY_MAX (WBANNO, LST_DATE) float64 -1.1 -12.4 ... 6.5 3.6
SUR_TEMP_DAILY_MIN (WBANNO, LST_DATE) float64 -12.4 -21.3 ... -3.6
SUR_TEMP_DAILY_AVG (WBANNO, LST_DATE) float64 -5.3 -16.4 ... -0.2 0.1
RH_DAILY_MAX (WBANNO, LST_DATE) float64 97.6 92.8 ... -9.999e+03
RH_DAILY_MIN (WBANNO, LST_DATE) float64 79.3 82.0 ... -9.999e+03
RH_DAILY_AVG (WBANNO, LST_DATE) float64 92.0 87.4 ... -9.999e+03
SOIL_MOISTURE_5_DAILY (WBANNO, LST_DATE) float64 -99.0 -99.0 ... -99.0
SOIL_MOISTURE_10_DAILY (WBANNO, LST_DATE) float64 -99.0 -99.0 ... -99.0
SOIL_MOISTURE_20_DAILY (WBANNO, LST_DATE) float64 -99.0 -99.0 ... -99.0
SOIL_MOISTURE_50_DAILY (WBANNO, LST_DATE) float64 -99.0 -99.0 ... -99.0
SOIL_MOISTURE_100_DAILY (WBANNO, LST_DATE) float64 -99.0 -99.0 ... -99.0
SOIL_TEMP_5_DAILY (WBANNO, LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_10_DAILY (WBANNO, LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_20_DAILY (WBANNO, LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_50_DAILY (WBANNO, LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_100_DAILY (WBANNO, LST_DATE) float64 -9.999e+03 ... -9.999e+03
LONGITUDE (WBANNO) float64 -164.1
LATITUDE (WBANNO) float64 61.35
CRX_VN (WBANNO) float64 2.515
# List the variables of the dataset
uscrn_ds.data_vars
Data variables:
T_DAILY_MAX (WBANNO, LST_DATE) float64 0.7 -12.3 ... 0.0 0.0
T_DAILY_MIN (WBANNO, LST_DATE) float64 -12.4 -17.0 ... -2.5
T_DAILY_MEAN (WBANNO, LST_DATE) float64 -5.8 -14.7 ... -2.3 -1.3
T_DAILY_AVG (WBANNO, LST_DATE) float64 -5.1 -14.4 ... -1.4 -0.3
P_DAILY_CALC (WBANNO, LST_DATE) float64 0.0 0.0 0.0 ... 0.0 0.0
SOLARAD_DAILY (WBANNO, LST_DATE) float64 0.26 0.68 ... 2.02 0.57
SUR_TEMP_DAILY_TYPE (WBANNO, LST_DATE) object 'C' 'C' 'C' ... 'C' 'C'
SUR_TEMP_DAILY_MAX (WBANNO, LST_DATE) float64 -1.1 -12.4 ... 6.5 3.6
SUR_TEMP_DAILY_MIN (WBANNO, LST_DATE) float64 -12.4 -21.3 ... -3.6
SUR_TEMP_DAILY_AVG (WBANNO, LST_DATE) float64 -5.3 -16.4 ... -0.2 0.1
RH_DAILY_MAX (WBANNO, LST_DATE) float64 97.6 92.8 ... -9.999e+03
RH_DAILY_MIN (WBANNO, LST_DATE) float64 79.3 82.0 ... -9.999e+03
RH_DAILY_AVG (WBANNO, LST_DATE) float64 92.0 87.4 ... -9.999e+03
SOIL_MOISTURE_5_DAILY (WBANNO, LST_DATE) float64 -99.0 -99.0 ... -99.0
SOIL_MOISTURE_10_DAILY (WBANNO, LST_DATE) float64 -99.0 -99.0 ... -99.0
SOIL_MOISTURE_20_DAILY (WBANNO, LST_DATE) float64 -99.0 -99.0 ... -99.0
SOIL_MOISTURE_50_DAILY (WBANNO, LST_DATE) float64 -99.0 -99.0 ... -99.0
SOIL_MOISTURE_100_DAILY (WBANNO, LST_DATE) float64 -99.0 -99.0 ... -99.0
SOIL_TEMP_5_DAILY (WBANNO, LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_10_DAILY (WBANNO, LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_20_DAILY (WBANNO, LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_50_DAILY (WBANNO, LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_100_DAILY (WBANNO, LST_DATE) float64 -9.999e+03 ... -9.999e+03
LONGITUDE (WBANNO) float64 -164.1
LATITUDE (WBANNO) float64 61.35
CRX_VN (WBANNO) float64 2.515
# Add units for each variable
for x in uscrn_ds.data_vars:
uscrn_ds[x].attrs['unit'] = uscrn_header.loc[1, uscrn_header.iloc[0, :]==x].values[0]
uscrn_ds
<xarray.Dataset>
Dimensions: (LST_DATE: 89, WBANNO: 1)
Coordinates:
* LST_DATE (LST_DATE) int64 20190101 20190102 ... 20190330
* WBANNO (WBANNO) int64 26656
Data variables:
T_DAILY_MAX (WBANNO, LST_DATE) float64 0.7 -12.3 ... 0.0 0.0
T_DAILY_MIN (WBANNO, LST_DATE) float64 -12.4 -17.0 ... -2.5
T_DAILY_MEAN (WBANNO, LST_DATE) float64 -5.8 -14.7 ... -2.3 -1.3
T_DAILY_AVG (WBANNO, LST_DATE) float64 -5.1 -14.4 ... -1.4 -0.3
P_DAILY_CALC (WBANNO, LST_DATE) float64 0.0 0.0 0.0 ... 0.0 0.0
SOLARAD_DAILY (WBANNO, LST_DATE) float64 0.26 0.68 ... 2.02 0.57
SUR_TEMP_DAILY_TYPE (WBANNO, LST_DATE) object 'C' 'C' 'C' ... 'C' 'C'
SUR_TEMP_DAILY_MAX (WBANNO, LST_DATE) float64 -1.1 -12.4 ... 6.5 3.6
SUR_TEMP_DAILY_MIN (WBANNO, LST_DATE) float64 -12.4 -21.3 ... -3.6
SUR_TEMP_DAILY_AVG (WBANNO, LST_DATE) float64 -5.3 -16.4 ... -0.2 0.1
RH_DAILY_MAX (WBANNO, LST_DATE) float64 97.6 92.8 ... -9.999e+03
RH_DAILY_MIN (WBANNO, LST_DATE) float64 79.3 82.0 ... -9.999e+03
RH_DAILY_AVG (WBANNO, LST_DATE) float64 92.0 87.4 ... -9.999e+03
SOIL_MOISTURE_5_DAILY (WBANNO, LST_DATE) float64 -99.0 -99.0 ... -99.0
SOIL_MOISTURE_10_DAILY (WBANNO, LST_DATE) float64 -99.0 -99.0 ... -99.0
SOIL_MOISTURE_20_DAILY (WBANNO, LST_DATE) float64 -99.0 -99.0 ... -99.0
SOIL_MOISTURE_50_DAILY (WBANNO, LST_DATE) float64 -99.0 -99.0 ... -99.0
SOIL_MOISTURE_100_DAILY (WBANNO, LST_DATE) float64 -99.0 -99.0 ... -99.0
SOIL_TEMP_5_DAILY (WBANNO, LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_10_DAILY (WBANNO, LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_20_DAILY (WBANNO, LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_50_DAILY (WBANNO, LST_DATE) float64 -9.999e+03 ... -9.999e+03
SOIL_TEMP_100_DAILY (WBANNO, LST_DATE) float64 -9.999e+03 ... -9.999e+03
LONGITUDE (WBANNO) float64 -164.1
LATITUDE (WBANNO) float64 61.35
CRX_VN (WBANNO) float64 2.515
# Save the dataset to a netcdf file
uscrn_ds.to_netcdf(str(uscrn_ds.WBANNO.values[0]) + '_' + str(uscrn_ds.LST_DATE.values[0]) + '_' + str(uscrn_ds.LST_DATE.values[-1]) + '.nc')
!ls -lrth
total 232
-rw-r--r-- 1 zeng staff 61K Mar 31 21:11 xarray_notes.ipynb
-rw-r--r-- 1 zeng staff 49K Mar 31 21:11 26656_20190101_20190330.nc