معرفی شرکت ها
argopandas-0.0.5
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Locate, Download, and Read Argo Ocean Float Data
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | argopandas-0.0.5 |
| نام | argopandas |
| نسخه کتابخانه | 0.0.5 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Dewey Dunnington |
| ایمیل نویسنده | dewey.dunnington@dfo-mpo.gc.ca |
| آدرس صفحه اصلی | https://github.com/paleolimbot/argopandas |
| آدرس اینترنتی | https://pypi.org/project/argopandas/ |
| مجوز | - |
# argopandas
[](https://github.com/ArgoCanada/argopandas/actions/workflows/check.yaml)
[](https://codecov.io/gh/ArgoCanada/argopandas?branch=master)
[](https://pypi.org/project/argopandas/)
[](https://argocanada.github.io/argopandas/)
[](https://github.com/ArgoCanada/argopandas)
The goal of argopandas is to provide seamless access to Argo NetCDF files using a pandas `DataFrame`-based interface. It is a Python port of the [argodata package for R](https://github.com/ArgoCanada/argodata). The package is under heavy development and we would love feedback on the interface or anything else about the package!
## Installation
You can install the `argopandas` package using `pip`.
``` bash
pip install argopandas
```
The package depends on `pandas`, `numpy`, `netCDF4`, and `pyarrow`, which install automatically if using `pip` or you can install also your favourite Python package manager. The `argopandas` package requires Python 3.6 or later.
## Examples
The intended interface for most usage is contained in the `argopandas` module. You can import this as `argo` for pretty-looking syntax:
```python
import argopandas as argo
```
The global indexes are available via `argo.prof`, `argo.meta`, `argo.tech`, `argo.traj`, `argo.bio_prof`, `argo.synthetic_prof`, and `argo.bio_traj`.
```python
argo.meta.head(5)
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>file</th>
<th>profiler_type</th>
<th>institution</th>
<th>date_update</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>aoml/13857/13857_meta.nc</td>
<td>845</td>
<td>AO</td>
<td>2018-10-11 20:00:14+00:00</td>
</tr>
<tr>
<th>1</th>
<td>aoml/13858/13858_meta.nc</td>
<td>845</td>
<td>AO</td>
<td>2018-10-11 20:00:15+00:00</td>
</tr>
<tr>
<th>2</th>
<td>aoml/13859/13859_meta.nc</td>
<td>845</td>
<td>AO</td>
<td>2018-10-11 20:00:25+00:00</td>
</tr>
<tr>
<th>3</th>
<td>aoml/15819/15819_meta.nc</td>
<td>845</td>
<td>AO</td>
<td>2018-10-11 20:00:16+00:00</td>
</tr>
<tr>
<th>4</th>
<td>aoml/15820/15820_meta.nc</td>
<td>845</td>
<td>AO</td>
<td>2018-10-11 20:00:18+00:00</td>
</tr>
</tbody>
</table>
</div>
By defaut, downloads are lazily cached from the [Ifremer https mirror](https://data-argo.ifremer.fr). You can use `argo.url_mirror()` or `argo.file_mirror()` with `argo.set_default_mirror()` to point `argopandas` at your favourite copy of Argo.
To get Argo data from one or more NetCDF files, subset the indexes and use one of the table accessors to download, cache, and read variables aligned along common dimensions. The accessor you probably want is the `.levels` accessor from the `argo.prof` index:
```python
argo.prof.head(5).levels[['PRES', 'TEMP']]
```
Downloading 5 files from 'https://data-argo.ifremer.fr/dac/aoml/13857/profiles'
Reading 5 files
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th></th>
<th>PRES</th>
<th>TEMP</th>
</tr>
<tr>
<th>file</th>
<th>N_PROF</th>
<th>N_LEVELS</th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="5" valign="top">aoml/13857/profiles/R13857_001.nc</th>
<th rowspan="5" valign="top">0</th>
<th>0</th>
<td>11.900000</td>
<td>22.235001</td>
</tr>
<tr>
<th>1</th>
<td>17.000000</td>
<td>21.987000</td>
</tr>
<tr>
<th>2</th>
<td>22.100000</td>
<td>21.891001</td>
</tr>
<tr>
<th>3</th>
<td>27.200001</td>
<td>21.812000</td>
</tr>
<tr>
<th>4</th>
<td>32.299999</td>
<td>21.632000</td>
</tr>
<tr>
<th>...</th>
<th>...</th>
<th>...</th>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th rowspan="5" valign="top">aoml/13857/profiles/R13857_005.nc</th>
<th rowspan="5" valign="top">0</th>
<th>102</th>
<td>976.500000</td>
<td>4.527000</td>
</tr>
<tr>
<th>103</th>
<td>986.700012</td>
<td>4.527000</td>
</tr>
<tr>
<th>104</th>
<td>996.799988</td>
<td>4.533000</td>
</tr>
<tr>
<th>105</th>
<td>1007.000000</td>
<td>4.487000</td>
</tr>
<tr>
<th>106</th>
<td>1017.200012</td>
<td>4.471000</td>
</tr>
</tbody>
</table>
<p>551 rows × 2 columns</p>
</div>
You can get data from every variable in an Argo NetCDF file using one of these accessors. The variables grouped in each are aligned along the same dimensions and are documented together in the Argo user's manual.
- All indexes have a `.info` accessor that contains length-one variables that aren't aligned along any dimensions
- `argo.prof`: `argo.prof.levels`, `arog.prof.prof`, `argo.prof.calib`, `argo.prof.param`, and `argo.prof.history`
- `argo.traj`: `argo.traj.cycle`, `argo.traj.measurement`, `argo.traj.param`, and `argo.traj.history`
- `argo.tech`: `argo.tech.tech_param`
- `argo.meta`: `argo.meta.config_param`, `argo.meta.missions`, `argo.meta.trans_system`, `argo.meta.positioning_system`, `argo.meta.launch_config_param`, `argo.meta.sensor`, and `argo.meta.param`
Once you have a data frame you do anything you'd do with a regular `pd.DataFrame()`, like plot your data using the built-in plot method:
```python
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
for label, df in argo.prof.head(5).levels.groupby('file'):
df.plot(x='TEMP', y = 'PRES', ax=ax, label=label)
ax.invert_yaxis()
```
Reading 5 files
You can access all the index files for a particular float using `argo.float()`, which lazily filters all the indexes for a particular float ID.
```python
float_obj = argo.float(13857)
float_obj.meta.info
```
Downloading 'https://data-argo.ifremer.fr/ar_index_global_meta.txt.gz'
Downloading 'https://data-argo.ifremer.fr/dac/aoml/13857/13857_meta.nc'
Reading 1 file
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th>DATA_TYPE</th>
<th>FORMAT_VERSION</th>
<th>HANDBOOK_VERSION</th>
<th>DATE_CREATION</th>
<th>DATE_UPDATE</th>
<th>PLATFORM_NUMBER</th>
<th>PTT</th>
<th>PLATFORM_FAMILY</th>
<th>PLATFORM_TYPE</th>
<th>PLATFORM_MAKER</th>
<th>...</th>
<th>LAUNCH_QC</th>
<th>START_DATE</th>
<th>START_DATE_QC</th>
<th>STARTUP_DATE</th>
<th>STARTUP_DATE_QC</th>
<th>DEPLOYMENT_PLATFORM</th>
<th>DEPLOYMENT_CRUISE_ID</th>
<th>DEPLOYMENT_REFERENCE_STATION_ID</th>
<th>END_MISSION_DATE</th>
<th>END_MISSION_STATUS</th>
</tr>
<tr>
<th>file</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>aoml/13857/13857_meta.nc</th>
<th>0</th>
<td>Argo meta-data</td>
<td>3.1</td>
<td>1.2</td>
<td>20181011200014</td>
<td>20181011200014</td>
<td>13857</td>
<td>09335 ...</td>
<td>FLOAT ...</td>
<td>PALACE</td>
<td>WRC ...</td>
<td>...</td>
<td>b'1'</td>
<td>19970719163000</td>
<td>b'1'</td>
<td>19970719103000</td>
<td>b'1'</td>
<td>R/V Seward Johnson</td>
<td>97-03</td>
<td>CTD 108 ...</td>
<td></td>
<td>NaN</td>
</tr>
</tbody>
</table>
<p>1 rows × 43 columns</p>
</div>
نیازمندی
| مقدار | نام |
|---|---|
| - | netCDF4 |
| - | numpy |
| - | pandas |
| - | pyarrow |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl argopandas-0.0.5:
pip install argopandas-0.0.5.whl
نصب پکیج tar.gz argopandas-0.0.5:
pip install argopandas-0.0.5.tar.gz