| Name | igra JSON |
| Version |
23.5
JSON |
| download |
| home_page | https://github.com/MBlaschek/igra |
| Summary | IGRAv2 radiosonde tools |
| upload_time | 2023-05-08 12:25:16 |
| maintainer | |
| docs_url | None |
| author | MB |
| requires_python | >=3.6 |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://badge.fury.io/py/igra)
# IGRA
Author: M. Blaschek
Date: Dezember 2019
Version: 23.05
This Python 3 Module is intended to read IGRA v2 NCDC data to pandas DataFrames or Xarray Datasets (interpolated to standard pressure levels).
# Install
The code uses Python3 as a standard.
Dependencies:
* numpy
* pandas
* netCDF4
* xarray
Download the Source code from GitHub
```shell
git clone https://github.com/MBlaschek/igra
```
or use the package on [PyPI igra](https://pypi.org/igra)
```shell
pip install igra
```
# Usage
## Download station list
Read the station list into pandas DataFrame (from file `igra2-station-list.txt` in the IGRAv2 repository)
```python
>>> import igra
>>> stations = igra.download.stationlist('/tmp')
Download complete, reading table ...
Data read from: /tmp/igra2-station-list.txt
Data processed 2787
>>> stations
wmo lat lon alt state name start end total
id
ACM00078861 078861 17.1170 -61.7830 10.0 COOLIDGE FIELD (UA) 1947 1993 13896
AEM00041217 041217 24.4333 54.6500 16.0 ABU DHABI INTERNATIONAL AIRPOR 1983 2019 36252
AEXUAE05467 25.2500 55.3700 4.0 SHARJAH 1935 1942 2477
AFM00040911 040911 36.7000 67.2000 378.0 MAZAR-I-SHARIF 2010 2014 2179
AFM00040913 040913 36.6667 68.9167 433.0 KUNDUZ 2010 2013 4540
AFM00040938 040938 34.2170 62.2170 977.0 HERAT 1978 1988 1107
AFM00040948 040948 34.5500 69.2167 1791.0 KABUL AIRPORT 1961 2019 17809
AFM00040990 040990 31.5000 65.8500 1010.0 KANDAHAR AIRPORT 1976 2014 5934
AGM00060355 060355 36.8830 6.9000 3.0 SKIKDA 1974 1976 639
AGM00060360 060360 36.8330 7.8170 4.0 ANNABA 1973 2008 30088
AGM00060390 060390 36.6833 3.2167 25.0 DAR-EL-BEIDA 1948 2019 68372
AGM00060402 060402 36.7170 5.0670 6.0 BEJAIA-AEROPORT 1973 1988 12372
AGM00060419 060419 36.2830 6.6170 694.0 CONSTANTINE 1973 2008 21262
AGM00060425 060425 36.2170 1.3330 141.0 CHLEF 1973 1977 3213
AGM00060430 060430 36.3000 2.2330 715.0 MILIANA 1973 1990 14988
...
```
## Download station
a radiosonde station with the `id` from the station list into `tmp` directory
```python
>>> igra.download.station("AUM00011035", "/tmp")
https://www1.ncdc.noaa.gov/pub/data/igra/data/data-por/AUM00011035-data.txt.zip to /tmp/AUM00011035-data.txt.zip
Downloaded: /tmp/ACM00078861-data.txt.zip
```
## Read station
The downloaded station file can be read to standard pressure levels (default) or table like with all significant levels (different amount of levels per sounding) using `return_table=True`. It is also possible to interpolate to different standard pressure levels with `levels=...`.
Usually the standard pressure levels need to be reported, thus no interpolation should be required.
```python
>>> print(igra.std_plevels)
[1000.0, 2000.0, 3000.0, 5000.0, 7000.0, 10000.0, 15000.0, 20000.0, 25000.0, 30000.0, 40000.0, 50000.0, 70000.0,
85000.0, 92500.0, 100000.0]
>>> data, station = igra.read.igra("AUM00011035", "/tmp/ACM00078861-data.txt.zip")
[AUM00011035] [2019-12-09T12:25:42.761655] AUM00011035 [1000.0, 2000.0, 3000.0, 5000.0, 7000.0, 10000.0, 15000.0, 20000.0, 25000.0, 30000.0, 40000.0, 50000.0, 70000.0, 85000.0, 92500.0, 100000.0]
[AUM00011035] [2019-12-09T12:25:42.761692] Reading ascii data into dataframes
[AUM00011035] [2019-12-09T12:25:57.710039] IGRAv2 Lines read: 3758652 Header count: 79638
[AUM00011035] [2019-12-09T12:26:11.583290] Missing pressure values 588657
[AUM00011035] [2019-12-09T12:26:11.583346] Interpolating to standard pressure levels
[AUM00011035] [2019-12-09T12:28:18.356667] (3090358, 7) >> (3331343, 8)
[AUM00011035] [2019-12-09T12:28:18.373608] Converting to xarray
[AUM00011035] [2019-12-09T12:28:18.377815] Adding Metadata
[AUM00011035] [2019-12-09T12:28:18.378166] Converting temperature and humidity
[AUM00011035] [2019-12-09T12:28:18.546226] Collecting Station information
>>> data
<xarray.Dataset>
Dimensions: (date: 78059, pres: 16)
Coordinates:
* date (date) datetime64[ns] 1949-02-08T04:00:00 ... 2019-10-01
* pres (pres) float64 1e+03 2e+03 3e+03 5e+03 ... 8.5e+04 9.25e+04 1e+05
Data variables:
gph (date, pres) float64 nan nan nan nan ... 1.491e+03 791.0 nan
temp (date, pres) float64 nan nan nan nan nan ... 274.4 280.0 285.2 nan
rhumi (date, pres) float64 nan nan nan nan nan ... nan nan nan nan nan
dpd (date, pres) float64 nan nan nan nan nan ... 7.0 21.0 7.0 8.0 nan
windd (date, pres) float64 nan nan nan nan nan ... 295.0 290.0 300.0 nan
winds (date, pres) float64 nan nan nan nan nan ... 21.0 14.0 10.0 nan
flag_int (date, pres) float64 1.0 1.0 1.0 1.0 1.0 ... 0.0 0.0 0.0 0.0 1.0
Attributes:
ident: AUM00011035
source: NOAA NCDC
dataset: IGRAv2
processed: UNIVIE, IMG
interpolated: to pres levs (#16)
>>> station
<xarray.Dataset>
Dimensions: (date: 78059)
Coordinates:
* date (date) datetime64[ns] 1949-02-08T04:00:00 ... 2019-10-01
Data variables:
numlev (date) int64 14 10 12 13 10 15 15 15 ... 37 41 38 136 131 39 105
lat (date) float64 48.25 48.25 48.25 48.25 ... 48.25 48.25 48.25 48.25
lon (date) float64 16.36 16.36 16.36 16.36 ... 16.36 16.36 16.36 16.36
Attributes:
ident: AUM00011035
source: NOAA NCDC
dataset: IGRAv2
processed: UNIVIE, IMG
```
## Download a UADB Station
a radiosonde station with the `wmo` from station list into `tmp` directory
However, you need to register for this data at RDA UCAR. You will need to enter your Email address and password to download the files.
```python
>>> igra.download.uadb("78861", "/tmp", "EMail-Adress", "Password")
https://rda.ucar.edu/cgi-bin/login to /tmp/uadb_trhc_78861.txt
100.000 % Completed
Downloaded: /tmp/uadb_trhc_78861.txt
```
## Read a UADB Station
The downloaded station file can be read to standard pressure levels (default) or table like with all significant levels (different amount of levels per sounding) using `return_table=True`. It is also possible to interpolate to different standard pressure levels with `levels=...`.
Usually the standard pressure levels need to be reported, thus no interpolation should be required.
```python
>>> data, station = igra.read.uadb("078861","/tmp/uadb_trhc_78861.txt")
[078861] [2019-12-09T13:22:15.726180] 078861 [1000.0, 2000.0, 3000.0, 5000.0, 7000.0, 10000.0, 15000.0, 20000.0, 25000.0, 30000.0, 40000.0, 50000.0, 70000.0, 85000.0, 92500.0, 100000.0]
[078861] [2019-12-09T13:22:15.726269] Reading ascii data into dataframes
[078861] [2019-12-09T13:22:17.448871] UADB Lines read: 579008 skipped: 0 Header: 8747
[078861] [2019-12-09T13:22:18.708963] Missing pressure values 167254
[078861] [2019-12-09T13:22:18.709015] Interpolating to standard pressure levels
[078861] [2019-12-09T13:22:33.040262] (403008, 6) >> (414833, 7)
[078861] [2019-12-09T13:22:33.040344] Converting to xarray
[078861] [2019-12-09T13:22:33.043989] Adding Metadata
[078861] [2019-12-09T13:22:33.044279] Converting temperature and humidity
[078861] [2019-12-09T13:22:33.063961] Collecting Station information
>>> data
<xarray.Dataset>
Dimensions: (date: 8729, pres: 16)
Coordinates:
* date (date) datetime64[ns] 1961-04-01 1961-04-09 ... 1993-04-28T12:12:00
* pres (pres) float64 1e+03 2e+03 3e+03 5e+03 ... 8.5e+04 9.25e+04 1e+05
Data variables:
gph (date, pres) float64 nan nan nan nan ... 1.527e+03 797.0 124.0
temp (date, pres) float64 nan nan nan nan ... 281.5 288.5 291.6 297.9
rhumi (date, pres) float64 nan nan nan nan ... 0.313 0.674 0.9393 0.799
windd (date, pres) float64 nan nan nan nan ... 220.0 140.0 134.8 130.0
winds (date, pres) float64 nan nan nan nan nan ... 11.3 3.1 6.2 7.761 9.2
flag_int (date, pres) float64 1.0 1.0 1.0 1.0 1.0 ... 0.0 0.0 0.0 nan 0.0
Attributes:
ident: 078861
source: NCAR RSA
dataset: UADB, ds370.1
processed: UNIVIE, IMG
interpolated: to pres levs (#16)
>>> station
<xarray.Dataset>
Dimensions: (date: 8743)
Coordinates:
* date (date) datetime64[ns] 1961-04-01 1961-04-09 ... 1993-04-28T12:00:00
Data variables:
uid (date) int64 8041502 8041503 8041504 ... 58207091 58209344 58211593
numlev (date) int64 17 42 20 43 54 20 56 47 ... 94 99 98 115 89 108 98 104
lat (date) float64 17.12 17.12 17.12 17.12 ... 17.12 17.12 17.12 17.12
lon (date) float64 298.2 298.2 298.2 298.2 ... 298.2 298.2 298.2 298.2
alt (date) float64 3.0 3.0 3.0 3.0 3.0 3.0 ... 5.0 5.0 5.0 5.0 5.0 5.0
stype (date) int64 3 3 3 3 3 3 3 3 3 3 3 3 3 ... 3 3 3 3 3 3 3 3 3 3 3 3
Attributes:
ident: 078861
source: NCAR RSA
dataset: UADB, ds370.1
processed: UNIVIE, IMG)
```
## Interpolate to custom pressure levels
For example you could use the 32 lowest ERA-Interim pressure levels.
```python
>>> print(igra.era_plevels)
[1000.0, 2000.0, 3000.0, 5000.0, 7000.0, 10000.0, 12500.0, 15000.0, 17500.0, 20000.0, 22500.0, 25000.0, 30000.0,
35000.0, 40000.0, 45000.0, 50000.0, 55000.0, 60000.0, 65000.0, 70000.0, 75000.0, 77500.0, 80000.0, 82500.0, 85000.0,
87500.0, 90000.0, 92500.0, 95000.0, 97500.0, 100000.0]
>>> data, station = igra.read.uadb("078861","/tmp/uadb_trhc_78861.txt", levels=igra.era_plevels)
```
## Read as DataFrame
The data is available as ASCII, therefore the closest representation is a pandas DataFrame. This is how the data is read and in the above steps converted to an xarray representation.
```python
>>> data,station = igra.read.ascii_to_dataframe("/tmp/ACM00078861-data.txt.zip") IGRAv2 Lines read: 508055 Header count: 13896
>>> station
numlev lat lon
date
1947-01-08 01:00:00 10 17.117 -61.783
1947-01-10 01:00:00 10 17.117 -61.783
1947-01-11 02:00:00 5 17.117 -61.783
1947-01-12 02:00:00 9 17.117 -61.783
1947-01-13 03:00:00 4 17.117 -61.783
... ... ... ...
1993-04-20 12:00:00 93 17.117 -61.783
1993-04-21 12:00:00 105 17.117 -61.783
1993-04-22 12:00:00 82 17.117 -61.783
1993-04-26 12:00:00 100 17.117 -61.783
1993-04-27 12:00:00 87 17.117 -61.783
1993-04-28 12:00:00 99 17.117 -61.783
[13896 rows x 3 columns]
>>> data
pres gph temp rhumi dpd windd winds
date
1947-01-08 01:00:00 101600.0 10.0 25.4 82.0 NaN 70.0 8.0
1947-01-08 01:00:00 100000.0 156.0 24.3 83.0 NaN 70.0 9.0
1947-01-08 01:00:00 85000.0 1559.0 14.5 93.0 NaN 80.0 10.0
1947-01-08 01:00:00 70000.0 3187.0 8.8 13.0 NaN 110.0 6.0
1947-01-08 01:00:00 50000.0 5894.0 -8.1 38.0 NaN 90.0 7.0
1947-01-08 01:00:00 40000.0 7587.0 -20.0 18.0 NaN NaN NaN
1947-01-08 01:00:00 30000.0 9666.0 -33.2 NaN NaN 80.0 20.0
1947-01-08 01:00:00 25000.0 10926.0 -41.7 NaN NaN NaN NaN
1947-01-08 01:00:00 20000.0 12416.0 -49.2 NaN NaN 10.0 7.0
1947-01-08 01:00:00 15000.0 14263.0 -61.3 NaN NaN NaN NaN
... ... ... ... ... ... ... ...
1993-04-28 12:00:00 960.0 31525.0 -38.3 NaN NaN NaN NaN
1993-04-28 12:00:00 900.0 31976.0 -34.9 NaN NaN NaN NaN
1993-04-28 12:00:00 880.0 32100.0 NaN NaN NaN 95.0 18.5
1993-04-28 12:00:00 810.0 32700.0 NaN NaN NaN 95.0 9.2
1993-04-28 12:00:00 700.0 33730.0 -31.5 NaN 49.0 NaN NaN
1993-04-28 12:00:00 680.0 NaN -31.9 NaN NaN NaN NaN
[494160 rows x 7 columns]
```
# License
MIT License
Copyright (c) 2019 Michael Blaschek
Raw data
{
"_id": null,
"home_page": "https://github.com/MBlaschek/igra",
"name": "igra",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.6",
"maintainer_email": "",
"keywords": "",
"author": "MB",
"author_email": "michael.blaschek@univie.ac.at",
"download_url": "https://files.pythonhosted.org/packages/03/97/f2bc9cacd0ea9f5841dfdf8d8c530fb2941eb21a985a0a0bbcb8dd0083f4/igra-23.5.tar.gz",
"platform": null,
"description": "[](https://badge.fury.io/py/igra)\n\n# IGRA\n\nAuthor: M. Blaschek\nDate: Dezember 2019\n\nVersion: 23.05\n\nThis Python 3 Module is intended to read IGRA v2 NCDC data to pandas DataFrames or Xarray Datasets (interpolated to standard pressure levels).\n\n# Install\n\nThe code uses Python3 as a standard.\n\nDependencies:\n\n* numpy\n* pandas\n* netCDF4\n* xarray\n\nDownload the Source code from GitHub\n\n```shell\ngit clone https://github.com/MBlaschek/igra\n```\n\nor use the package on [PyPI igra](https://pypi.org/igra)\n\n```shell\npip install igra\n```\n\n\n\n# Usage\n\n## Download station list \n\nRead the station list into pandas DataFrame (from file `igra2-station-list.txt` in the IGRAv2 repository)\n\n```python\n>>> import igra\n>>> stations = igra.download.stationlist('/tmp')\nDownload complete, reading table ...\nData read from: /tmp/igra2-station-list.txt\nData processed 2787\n>>> stations\n wmo lat lon alt state name start end total\nid \nACM00078861 078861 17.1170 -61.7830 10.0 COOLIDGE FIELD (UA) 1947 1993 13896\nAEM00041217 041217 24.4333 54.6500 16.0 ABU DHABI INTERNATIONAL AIRPOR 1983 2019 36252\nAEXUAE05467 25.2500 55.3700 4.0 SHARJAH 1935 1942 2477\nAFM00040911 040911 36.7000 67.2000 378.0 MAZAR-I-SHARIF 2010 2014 2179\nAFM00040913 040913 36.6667 68.9167 433.0 KUNDUZ 2010 2013 4540\nAFM00040938 040938 34.2170 62.2170 977.0 HERAT 1978 1988 1107\nAFM00040948 040948 34.5500 69.2167 1791.0 KABUL AIRPORT 1961 2019 17809\nAFM00040990 040990 31.5000 65.8500 1010.0 KANDAHAR AIRPORT 1976 2014 5934\nAGM00060355 060355 36.8830 6.9000 3.0 SKIKDA 1974 1976 639\nAGM00060360 060360 36.8330 7.8170 4.0 ANNABA 1973 2008 30088\nAGM00060390 060390 36.6833 3.2167 25.0 DAR-EL-BEIDA 1948 2019 68372\nAGM00060402 060402 36.7170 5.0670 6.0 BEJAIA-AEROPORT 1973 1988 12372\nAGM00060419 060419 36.2830 6.6170 694.0 CONSTANTINE 1973 2008 21262\nAGM00060425 060425 36.2170 1.3330 141.0 CHLEF 1973 1977 3213\nAGM00060430 060430 36.3000 2.2330 715.0 MILIANA 1973 1990 14988\n...\n```\n\n## Download station \n\na radiosonde station with the `id` from the station list into `tmp` directory\n\n```python\n>>> igra.download.station(\"AUM00011035\", \"/tmp\")\nhttps://www1.ncdc.noaa.gov/pub/data/igra/data/data-por/AUM00011035-data.txt.zip to /tmp/AUM00011035-data.txt.zip\nDownloaded: /tmp/ACM00078861-data.txt.zip\n```\n\n## Read station\n\nThe downloaded station file can be read to standard pressure levels (default) or table like with all significant levels (different amount of levels per sounding) using `return_table=True`. It is also possible to interpolate to different standard pressure levels with `levels=...`.\n\nUsually the standard pressure levels need to be reported, thus no interpolation should be required.\n```python\n>>> print(igra.std_plevels)\n[1000.0, 2000.0, 3000.0, 5000.0, 7000.0, 10000.0, 15000.0, 20000.0, 25000.0, 30000.0, 40000.0, 50000.0, 70000.0, \n85000.0, 92500.0, 100000.0]\n>>> data, station = igra.read.igra(\"AUM00011035\", \"/tmp/ACM00078861-data.txt.zip\") \n[AUM00011035] [2019-12-09T12:25:42.761655] AUM00011035 [1000.0, 2000.0, 3000.0, 5000.0, 7000.0, 10000.0, 15000.0, 20000.0, 25000.0, 30000.0, 40000.0, 50000.0, 70000.0, 85000.0, 92500.0, 100000.0]\n[AUM00011035] [2019-12-09T12:25:42.761692] Reading ascii data into dataframes\n[AUM00011035] [2019-12-09T12:25:57.710039] IGRAv2 Lines read: 3758652 Header count: 79638\n[AUM00011035] [2019-12-09T12:26:11.583290] Missing pressure values 588657\n[AUM00011035] [2019-12-09T12:26:11.583346] Interpolating to standard pressure levels\n[AUM00011035] [2019-12-09T12:28:18.356667] (3090358, 7) >> (3331343, 8)\n[AUM00011035] [2019-12-09T12:28:18.373608] Converting to xarray\n[AUM00011035] [2019-12-09T12:28:18.377815] Adding Metadata\n[AUM00011035] [2019-12-09T12:28:18.378166] Converting temperature and humidity\n[AUM00011035] [2019-12-09T12:28:18.546226] Collecting Station information\n>>> data\n<xarray.Dataset>\n Dimensions: (date: 78059, pres: 16)\n Coordinates:\n * date (date) datetime64[ns] 1949-02-08T04:00:00 ... 2019-10-01\n * pres (pres) float64 1e+03 2e+03 3e+03 5e+03 ... 8.5e+04 9.25e+04 1e+05\n Data variables:\n gph (date, pres) float64 nan nan nan nan ... 1.491e+03 791.0 nan\n temp (date, pres) float64 nan nan nan nan nan ... 274.4 280.0 285.2 nan\n rhumi (date, pres) float64 nan nan nan nan nan ... nan nan nan nan nan\n dpd (date, pres) float64 nan nan nan nan nan ... 7.0 21.0 7.0 8.0 nan\n windd (date, pres) float64 nan nan nan nan nan ... 295.0 290.0 300.0 nan\n winds (date, pres) float64 nan nan nan nan nan ... 21.0 14.0 10.0 nan\n flag_int (date, pres) float64 1.0 1.0 1.0 1.0 1.0 ... 0.0 0.0 0.0 0.0 1.0\n Attributes:\n ident: AUM00011035\n source: NOAA NCDC\n dataset: IGRAv2\n processed: UNIVIE, IMG\n interpolated: to pres levs (#16)\n>>> station\n<xarray.Dataset>\n Dimensions: (date: 78059)\n Coordinates:\n * date (date) datetime64[ns] 1949-02-08T04:00:00 ... 2019-10-01\n Data variables:\n numlev (date) int64 14 10 12 13 10 15 15 15 ... 37 41 38 136 131 39 105\n lat (date) float64 48.25 48.25 48.25 48.25 ... 48.25 48.25 48.25 48.25\n lon (date) float64 16.36 16.36 16.36 16.36 ... 16.36 16.36 16.36 16.36\n Attributes:\n ident: AUM00011035\n source: NOAA NCDC\n dataset: IGRAv2\n processed: UNIVIE, IMG\n\n```\n\n## Download a UADB Station\n\na radiosonde station with the `wmo` from station list into `tmp` directory\n\nHowever, you need to register for this data at RDA UCAR. You will need to enter your Email address and password to download the files.\n```python\n>>> igra.download.uadb(\"78861\", \"/tmp\", \"EMail-Adress\", \"Password\")\nhttps://rda.ucar.edu/cgi-bin/login to /tmp/uadb_trhc_78861.txt\n100.000 % Completed\nDownloaded: /tmp/uadb_trhc_78861.txt\n```\n## Read a UADB Station\n\nThe downloaded station file can be read to standard pressure levels (default) or table like with all significant levels (different amount of levels per sounding) using `return_table=True`. It is also possible to interpolate to different standard pressure levels with `levels=...`.\n\nUsually the standard pressure levels need to be reported, thus no interpolation should be required.\n\n```python\n>>> data, station = igra.read.uadb(\"078861\",\"/tmp/uadb_trhc_78861.txt\") \n[078861] [2019-12-09T13:22:15.726180] 078861 [1000.0, 2000.0, 3000.0, 5000.0, 7000.0, 10000.0, 15000.0, 20000.0, 25000.0, 30000.0, 40000.0, 50000.0, 70000.0, 85000.0, 92500.0, 100000.0]\n[078861] [2019-12-09T13:22:15.726269] Reading ascii data into dataframes\n[078861] [2019-12-09T13:22:17.448871] UADB Lines read: 579008 skipped: 0 Header: 8747\n[078861] [2019-12-09T13:22:18.708963] Missing pressure values 167254\n[078861] [2019-12-09T13:22:18.709015] Interpolating to standard pressure levels\n[078861] [2019-12-09T13:22:33.040262] (403008, 6) >> (414833, 7)\n[078861] [2019-12-09T13:22:33.040344] Converting to xarray\n[078861] [2019-12-09T13:22:33.043989] Adding Metadata\n[078861] [2019-12-09T13:22:33.044279] Converting temperature and humidity\n[078861] [2019-12-09T13:22:33.063961] Collecting Station information\n>>> data\n<xarray.Dataset>\nDimensions: (date: 8729, pres: 16)\nCoordinates:\n * date (date) datetime64[ns] 1961-04-01 1961-04-09 ... 1993-04-28T12:12:00\n * pres (pres) float64 1e+03 2e+03 3e+03 5e+03 ... 8.5e+04 9.25e+04 1e+05\nData variables:\n gph (date, pres) float64 nan nan nan nan ... 1.527e+03 797.0 124.0\n temp (date, pres) float64 nan nan nan nan ... 281.5 288.5 291.6 297.9\n rhumi (date, pres) float64 nan nan nan nan ... 0.313 0.674 0.9393 0.799\n windd (date, pres) float64 nan nan nan nan ... 220.0 140.0 134.8 130.0\n winds (date, pres) float64 nan nan nan nan nan ... 11.3 3.1 6.2 7.761 9.2\n flag_int (date, pres) float64 1.0 1.0 1.0 1.0 1.0 ... 0.0 0.0 0.0 nan 0.0\nAttributes:\n ident: 078861\n source: NCAR RSA\n dataset: UADB, ds370.1\n processed: UNIVIE, IMG\n interpolated: to pres levs (#16)\n>>> station\n<xarray.Dataset>\n Dimensions: (date: 8743)\n Coordinates:\n * date (date) datetime64[ns] 1961-04-01 1961-04-09 ... 1993-04-28T12:00:00\n Data variables:\n uid (date) int64 8041502 8041503 8041504 ... 58207091 58209344 58211593\n numlev (date) int64 17 42 20 43 54 20 56 47 ... 94 99 98 115 89 108 98 104\n lat (date) float64 17.12 17.12 17.12 17.12 ... 17.12 17.12 17.12 17.12\n lon (date) float64 298.2 298.2 298.2 298.2 ... 298.2 298.2 298.2 298.2\n alt (date) float64 3.0 3.0 3.0 3.0 3.0 3.0 ... 5.0 5.0 5.0 5.0 5.0 5.0\n stype (date) int64 3 3 3 3 3 3 3 3 3 3 3 3 3 ... 3 3 3 3 3 3 3 3 3 3 3 3\n Attributes:\n ident: 078861\n source: NCAR RSA\n dataset: UADB, ds370.1\n processed: UNIVIE, IMG)\n```\n\n## Interpolate to custom pressure levels\n\nFor example you could use the 32 lowest ERA-Interim pressure levels.\n```python\n>>> print(igra.era_plevels)\n[1000.0, 2000.0, 3000.0, 5000.0, 7000.0, 10000.0, 12500.0, 15000.0, 17500.0, 20000.0, 22500.0, 25000.0, 30000.0, \n35000.0, 40000.0, 45000.0, 50000.0, 55000.0, 60000.0, 65000.0, 70000.0, 75000.0, 77500.0, 80000.0, 82500.0, 85000.0, \n87500.0, 90000.0, 92500.0, 95000.0, 97500.0, 100000.0]\n\n>>> data, station = igra.read.uadb(\"078861\",\"/tmp/uadb_trhc_78861.txt\", levels=igra.era_plevels) \n```\n\n## Read as DataFrame \n\nThe data is available as ASCII, therefore the closest representation is a pandas DataFrame. This is how the data is read and in the above steps converted to an xarray representation. \n\n```python\n>>> data,station = igra.read.ascii_to_dataframe(\"/tmp/ACM00078861-data.txt.zip\") IGRAv2 Lines read: 508055 Header count: 13896\n>>> station\n numlev lat lon\ndate \n1947-01-08 01:00:00 10 17.117 -61.783\n1947-01-10 01:00:00 10 17.117 -61.783\n1947-01-11 02:00:00 5 17.117 -61.783\n1947-01-12 02:00:00 9 17.117 -61.783\n1947-01-13 03:00:00 4 17.117 -61.783\n... ... ... ...\n1993-04-20 12:00:00 93 17.117 -61.783\n1993-04-21 12:00:00 105 17.117 -61.783\n1993-04-22 12:00:00 82 17.117 -61.783\n1993-04-26 12:00:00 100 17.117 -61.783\n1993-04-27 12:00:00 87 17.117 -61.783\n1993-04-28 12:00:00 99 17.117 -61.783\n\n[13896 rows x 3 columns]\n\n>>> data\n pres gph temp rhumi dpd windd winds\ndate \n1947-01-08 01:00:00 101600.0 10.0 25.4 82.0 NaN 70.0 8.0\n1947-01-08 01:00:00 100000.0 156.0 24.3 83.0 NaN 70.0 9.0\n1947-01-08 01:00:00 85000.0 1559.0 14.5 93.0 NaN 80.0 10.0\n1947-01-08 01:00:00 70000.0 3187.0 8.8 13.0 NaN 110.0 6.0\n1947-01-08 01:00:00 50000.0 5894.0 -8.1 38.0 NaN 90.0 7.0\n1947-01-08 01:00:00 40000.0 7587.0 -20.0 18.0 NaN NaN NaN\n1947-01-08 01:00:00 30000.0 9666.0 -33.2 NaN NaN 80.0 20.0\n1947-01-08 01:00:00 25000.0 10926.0 -41.7 NaN NaN NaN NaN\n1947-01-08 01:00:00 20000.0 12416.0 -49.2 NaN NaN 10.0 7.0\n1947-01-08 01:00:00 15000.0 14263.0 -61.3 NaN NaN NaN NaN\n... ... ... ... ... ... ... ...\n1993-04-28 12:00:00 960.0 31525.0 -38.3 NaN NaN NaN NaN\n1993-04-28 12:00:00 900.0 31976.0 -34.9 NaN NaN NaN NaN\n1993-04-28 12:00:00 880.0 32100.0 NaN NaN NaN 95.0 18.5\n1993-04-28 12:00:00 810.0 32700.0 NaN NaN NaN 95.0 9.2\n1993-04-28 12:00:00 700.0 33730.0 -31.5 NaN 49.0 NaN NaN\n1993-04-28 12:00:00 680.0 NaN -31.9 NaN NaN NaN NaN\n\n[494160 rows x 7 columns]\n```\n\n# License\n\nMIT License\n\nCopyright (c) 2019 Michael Blaschek\n\n\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "IGRAv2 radiosonde tools",
"version": "23.5",
"project_urls": {
"Homepage": "https://github.com/MBlaschek/igra"
},
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "d4e6dc770917d62be77415dfcca86d01755e5086705f44224816d0f7b3897e61",
"md5": "b9dff40ea10885d888e7ea3a0f72c46e",
"sha256": "28015717356fa4b3a49f63c6b7dfdac4a4a32cfcf8646f3260256c1a1373fa4b"
},
"downloads": -1,
"filename": "igra-23.5-py3-none-any.whl",
"has_sig": false,
"md5_digest": "b9dff40ea10885d888e7ea3a0f72c46e",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.6",
"size": 24259,
"upload_time": "2023-05-08T12:25:14",
"upload_time_iso_8601": "2023-05-08T12:25:14.204143Z",
"url": "https://files.pythonhosted.org/packages/d4/e6/dc770917d62be77415dfcca86d01755e5086705f44224816d0f7b3897e61/igra-23.5-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "0397f2bc9cacd0ea9f5841dfdf8d8c530fb2941eb21a985a0a0bbcb8dd0083f4",
"md5": "8914d8fed963513f7f92e75ea894a4d1",
"sha256": "1b8c2f29ea981813b7b6aa251bbbb244dfaadadc04dcf7041ffb93758c399afb"
},
"downloads": -1,
"filename": "igra-23.5.tar.gz",
"has_sig": false,
"md5_digest": "8914d8fed963513f7f92e75ea894a4d1",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.6",
"size": 22654,
"upload_time": "2023-05-08T12:25:16",
"upload_time_iso_8601": "2023-05-08T12:25:16.422009Z",
"url": "https://files.pythonhosted.org/packages/03/97/f2bc9cacd0ea9f5841dfdf8d8c530fb2941eb21a985a0a0bbcb8dd0083f4/igra-23.5.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-05-08 12:25:16",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "MBlaschek",
"github_project": "igra",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "igra"
}
