path: root/python-wasdi.spec

%global _empty_manifest_terminate_build 0
Name:		python-wasdi
Version:	0.8.0.2
Release:	1
Summary:	The WASDI Python library
License:	GNU General Public License v3 (GPLv3)
URL:		https://www.wasdi.net
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/3e/d2/6e90269162e7bd43e8f8e2f75c3496bf9cdf591924ac8aea8755e042b9f0/wasdi-0.8.0.2.tar.gz
BuildArch:	noarch

Requires:	python3-requests

%description
## Python tutorial
WASPY is the **WAS**DI **Py**thon Library. 
**GET UPDATED DOCUMENTATION, TUTORIALS AND MORE [here](https://wasdi.readthedocs.io/en/latest/index.html)**
### Prerequisites:
mandatory:
- a [WASDI](http://www.wasdi.net) registered user (with a username/password, google users are not supported yet)
- at least one workspace
- some EO products in your workspace
### Installation
To start working with WASPY, just install the library using:
```bash
pip install wasdi
```
To quickly check if the installation worked correctly, try running the following code:
```python
import wasdi
print(wasdi.hello())
```
You should see this kind of output:
```json
{"boolValue":null,"doubleValue":null,"intValue":null,"stringValue":"Hello Wasdi!!"}
```
### Configuration
Create a `config.json` file. It is a standard json file, which is used to store the credentials of the user and some other settings. The syntax is:
```json
“VARIABLE_NAME”: value
```
Hint: exploit an editor which can check the syntax (there are many which can be accessed online for free)
The minimal configuration to begin working with WASPY is:
```json
{
  "USER": "yourUser@wasdi.net",
  "PASSWORD": "yourPasswordHere",
  "WORKSPACE": "nameOfTheWorkspaceYouWantToUse"
}
```
For the other available parameters please refer to the Documentation.
### Start WASPY
To start WASPY and check if everything is working, run the following code:
```python
wasdi.init('./config.json')
```
(Adapt the path if the file is not located in your working directory)
The Lib will read the configuration file, load the user and password, log the user in, and then open the workspace specified in the configuration file. To check if everything is working, try to get the list of workspaces available for the user:
```python
wasdi.getWorkspaces()
```
You should be able to see a result similar to the following one:
```python
[{u'ownerUserId': u'yourUser@wasdi.net',
  u'sharedUsers': [],
  u'workspaceId': u'23ab54f3-b453-2b3e-284a-b6a4243f0f2c',
  u'workspaceName': u'nameOfTheWorkspaceYouWantToUse'},
 {u'ownerUserId': u'yourUser@wasdi.net',
  u'sharedUsers': [],
  u'workspaceId': u'103fbf01-2e68-22d3-bd45-2cf95665dac2',
  u'workspaceName': u'theNameOfAnotherWorkspace'}]
```
The configured Workspace is already opened.  The use can open another workspace using:
```python
wasdi.openWorkspace('theNameOfAnotherWorkspace')
```
and the lib replies showing the workspace unique id:
```python
u'9ce787d4-1d59-4146-8df7-3fc9516d4eb3'
```
To get the list of the products available in the workspace, call
```python
wasdi.getProductsByWorkspace('nameOfTheWorkspaceYouWantToUse')
```
and the lib returns a list of the products in the given workspace:
```python
[u'S1A_IW_GRDH_1SDV_20190517T053543_20190517T053608_027263_0312F1_F071.zip',
u'S1B_IW_RAW__0SDV_20190506T052631_20190506T052703_016119_01E53A_D2AD.zip', u'S1A_IW_GRDH_1SDV_20190517T053608_20190517T053633_027263_0312F1_3382.zip']
```
Now try something more, let's search for some Sentinel 1 images. Let's assume we are interested in images taken from "2018-09-01" to "2018-09-02". Also, we'd better specify a bounding box. Assume we're interested in images with *latitude* in `[43, 44]` and *longitude* in `[11, 12]`. We can think of these coordinates as a rectangle limited by the upper left corner `(44, 11)` and the lower right corner`(43, 12)`. 
 The corresponding code is:
```python
wasdi.wasdiLog('Let\'s search some images')
aoImages = wasdi.searchEOImages("S1", "2018-09-01", "2018-09-02", 44, 11, 43, 12, None, None, None, None)
wasdi.wasdiLog('Found ' + str(len(aoImages)))
```
The output should be similar to this:
```
 Let's search some images
[INFO] waspy.searchEOImages: search results:
[{
		'footprint': 'POLYGON ((8.8724 45.3272, 8.4505 43.3746, 11.4656 43.0981, 11.9901 45.0472, 8.8724 45.3272, 8.8724 45.3272))',
		'id': 'cba6c104-3006-4af7-a2d1-cbd55f58b939',
		'link': 'https://catalogue.onda-dias.eu/dias-catalogue/Products(cba6c104-3006-4af7-a2d1-cbd55f58b939)/$value',
		'preview': None,
		'properties': {
			'offline': 'false',
			'downloadable': '',
			'filename': 'S1A_IW_RAW__0SDV_20180902T052727_20180902T052759_023515_028F75_7325.zip',
			'size': '1.54 GB',
			'pseudopath': 'RADAR/LEVEL-0/2018/09/02, S1/1A/SAR-C/LEVEL-0/IW_RAW__0S/2018/09/02, S1/1A/LEVEL-0/IW_RAW__0S/2018/09/02, S1/SAR-C/LEVEL-0/IW_RAW__0S/2018/09/02, S1/LEVEL-0/IW_RAW__0S/2018/09/02, 2014-016A/SAR-C/LEVEL-0/IW_RAW__0S/2018/09/02, 2014-016A/LEVEL-0/IW_RAW__0S/2018/09/02',
			'link': 'https://catalogue.onda-dias.eu/dias-catalogue/Products(cba6c104-3006-4af7-a2d1-cbd55f58b939)/$value',
			'format': 'application/zip',
			'creationDate': '2018-09-03T05:12:37.000Z'
		},
		'provider': 'ONDA',
		'summary': 'Date: 2018-09-03T05:12:37.000Z, Instrument: null, Mode: null, Satellite: null, Size: 1.54 GB',
		'title': 'S1A_IW_RAW__0SDV_20180902T052727_20180902T052759_023515_028F75_7325'
},
{'(...7 more results similar to this one, omitted for brevity)'}]
Found 8
```
Now we can import one of those products in WASDI: let's download the first one:
```python
sImportWithDict = wasdi.importProduct(None, None, aoImages[0])
```
We can see a list of the products in the workspace as follows:
```python
asProducts = wasdi.getProductsByActiveWorkspace()
wasdi.wasdiLog(asProducts)
```
The second line logs the list of products
### Running an existing workflow
If you wish to run an existing SNAP workflow you can use `wasdi.executeWorkflow`. For example, if you wish to execute a workflow that calibrates and corrects the georeference of a Sentinel 1 image, you may use the workflow called `LISTSinglePreproc` in this way:
```python
asProducts = wasdi.getProductsByActiveWorkspace()
sStatus = wasdi.executeWorkflow([asProducts[0]], ['lovelyOutput'], 'LISTSinglePreproc')
```
Here the first line gets the list of products and the second calls the workflow `LISTSinglePreproc` on the first product of the workspace and creates another product called `lovelyOutput`.
### A more complete example
Now put everything back together. Create a file called [`myProcessor.py`](https://github.com/fadeoutsoftware/WASDI/blob/develop/libraries/waspy/examples/myProcessor.py) (follow the link to download the file) with the following content:
```python
import wasdi
def run(parameters, processId):
    wasdi.wasdiLog('Here\'s the list of your workspaces:')
    aoWorkspaces = wasdi.getWorkspaces()
    wasdi.wasdiLog(aoWorkspaces)
    wasdi.wasdiLog('The ID of currently selected workspace is:')
    sActiveWorkspace = wasdi.getActiveWorkspaceId()
    wasdi.wasdiLog(sActiveWorkspace)
    wasdi.wasdiLog('Let\'s search some images...')
    aoImages = wasdi.searchEOImages("S1", "2018-09-01", "2018-09-02", 44, 11, 43, 12, sProductType='GRD')
    wasdi.wasdiLog('Found ' + str(len(aoImages)) + ' images')
    wasdi.wasdiLog('Download the first one passing the dictionary...')
    sImportWithDict = wasdi.importProduct(None, None, aoImages[0])
    wasdi.wasdiLog('Import with dict returned: ' + sImportWithDict)
    wasdi.wasdiLog('Now, these are the products in your workspace: ')
    asProducts = wasdi.getProductsByActiveWorkspace()
    wasdi.wasdiLog(asProducts)
    wasdi.wasdiLog('Let\'s run a workflow on the first image to rectify its georeference...')
    sStatus = wasdi.executeWorkflow([asProducts[0]], ['lovelyOutput'], 'LISTSinglePreproc')
    if sStatus == 'DONE':
        wasdi.wasdiLog('The product is now in your workspace, look at it on the website')
    wasdi.wasdiLog('It\'s over!')
def WasdiHelp():
    sHelp = "Wasdi Tutorial"
    return sHelp
```
Then create another file to start the processor. Let's call it [`tutorial.py`](https://github.com/fadeoutsoftware/WASDI/blob/develop/libraries/waspy/examples/tutorial.py) (follow the link to download the file), with the following content:
```python
import myProcessor
import wasdi
bInitResult = wasdi.init('config.json')
if bInitResult:
    myProcessor.run(wasdi.getParametersDict(), '')
```
Now, if you run `tutorial.py`, it will call `myProcessor.py`, which will go through the instructions we saw above. Pro tip: keep the browser open in wasdi.net (make sure you are logged in) and open the workspace you are using, to see the evolution of the script in real time.
### Deploy your processor on WASDI
Finally, to deply our processor on WASDI, you need first to create a text file called [`pip.txt`](https://github.com/fadeoutsoftware/WASDI/blob/develop/libraries/waspy/examples/pip.txt) (follow the link to download the file) containg the packages we imported in `myProcessor.py`, one per line. Since we just imported `wasdi`, it should look like this:
```
wasdi
```
Now, create a zip file containing these two files:
- `myProcessor.py`
- `pip.txt`
You can now upload the zip file on wasdi.net from *Edit* -> *Processor* -> *New WASDI App* by giving it a name and completing the other details. You will need to do this just once.

%package -n python3-wasdi
Summary:	The WASDI Python library
Provides:	python-wasdi
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-wasdi
## Python tutorial
WASPY is the **WAS**DI **Py**thon Library. 
**GET UPDATED DOCUMENTATION, TUTORIALS AND MORE [here](https://wasdi.readthedocs.io/en/latest/index.html)**
### Prerequisites:
mandatory:
- a [WASDI](http://www.wasdi.net) registered user (with a username/password, google users are not supported yet)
- at least one workspace
- some EO products in your workspace
### Installation
To start working with WASPY, just install the library using:
```bash
pip install wasdi
```
To quickly check if the installation worked correctly, try running the following code:
```python
import wasdi
print(wasdi.hello())
```
You should see this kind of output:
```json
{"boolValue":null,"doubleValue":null,"intValue":null,"stringValue":"Hello Wasdi!!"}
```
### Configuration
Create a `config.json` file. It is a standard json file, which is used to store the credentials of the user and some other settings. The syntax is:
```json
“VARIABLE_NAME”: value
```
Hint: exploit an editor which can check the syntax (there are many which can be accessed online for free)
The minimal configuration to begin working with WASPY is:
```json
{
  "USER": "yourUser@wasdi.net",
  "PASSWORD": "yourPasswordHere",
  "WORKSPACE": "nameOfTheWorkspaceYouWantToUse"
}
```
For the other available parameters please refer to the Documentation.
### Start WASPY
To start WASPY and check if everything is working, run the following code:
```python
wasdi.init('./config.json')
```
(Adapt the path if the file is not located in your working directory)
The Lib will read the configuration file, load the user and password, log the user in, and then open the workspace specified in the configuration file. To check if everything is working, try to get the list of workspaces available for the user:
```python
wasdi.getWorkspaces()
```
You should be able to see a result similar to the following one:
```python
[{u'ownerUserId': u'yourUser@wasdi.net',
  u'sharedUsers': [],
  u'workspaceId': u'23ab54f3-b453-2b3e-284a-b6a4243f0f2c',
  u'workspaceName': u'nameOfTheWorkspaceYouWantToUse'},
 {u'ownerUserId': u'yourUser@wasdi.net',
  u'sharedUsers': [],
  u'workspaceId': u'103fbf01-2e68-22d3-bd45-2cf95665dac2',
  u'workspaceName': u'theNameOfAnotherWorkspace'}]
```
The configured Workspace is already opened.  The use can open another workspace using:
```python
wasdi.openWorkspace('theNameOfAnotherWorkspace')
```
and the lib replies showing the workspace unique id:
```python
u'9ce787d4-1d59-4146-8df7-3fc9516d4eb3'
```
To get the list of the products available in the workspace, call
```python
wasdi.getProductsByWorkspace('nameOfTheWorkspaceYouWantToUse')
```
and the lib returns a list of the products in the given workspace:
```python
[u'S1A_IW_GRDH_1SDV_20190517T053543_20190517T053608_027263_0312F1_F071.zip',
u'S1B_IW_RAW__0SDV_20190506T052631_20190506T052703_016119_01E53A_D2AD.zip', u'S1A_IW_GRDH_1SDV_20190517T053608_20190517T053633_027263_0312F1_3382.zip']
```
Now try something more, let's search for some Sentinel 1 images. Let's assume we are interested in images taken from "2018-09-01" to "2018-09-02". Also, we'd better specify a bounding box. Assume we're interested in images with *latitude* in `[43, 44]` and *longitude* in `[11, 12]`. We can think of these coordinates as a rectangle limited by the upper left corner `(44, 11)` and the lower right corner`(43, 12)`. 
 The corresponding code is:
```python
wasdi.wasdiLog('Let\'s search some images')
aoImages = wasdi.searchEOImages("S1", "2018-09-01", "2018-09-02", 44, 11, 43, 12, None, None, None, None)
wasdi.wasdiLog('Found ' + str(len(aoImages)))
```
The output should be similar to this:
```
 Let's search some images
[INFO] waspy.searchEOImages: search results:
[{
		'footprint': 'POLYGON ((8.8724 45.3272, 8.4505 43.3746, 11.4656 43.0981, 11.9901 45.0472, 8.8724 45.3272, 8.8724 45.3272))',
		'id': 'cba6c104-3006-4af7-a2d1-cbd55f58b939',
		'link': 'https://catalogue.onda-dias.eu/dias-catalogue/Products(cba6c104-3006-4af7-a2d1-cbd55f58b939)/$value',
		'preview': None,
		'properties': {
			'offline': 'false',
			'downloadable': '',
			'filename': 'S1A_IW_RAW__0SDV_20180902T052727_20180902T052759_023515_028F75_7325.zip',
			'size': '1.54 GB',
			'pseudopath': 'RADAR/LEVEL-0/2018/09/02, S1/1A/SAR-C/LEVEL-0/IW_RAW__0S/2018/09/02, S1/1A/LEVEL-0/IW_RAW__0S/2018/09/02, S1/SAR-C/LEVEL-0/IW_RAW__0S/2018/09/02, S1/LEVEL-0/IW_RAW__0S/2018/09/02, 2014-016A/SAR-C/LEVEL-0/IW_RAW__0S/2018/09/02, 2014-016A/LEVEL-0/IW_RAW__0S/2018/09/02',
			'link': 'https://catalogue.onda-dias.eu/dias-catalogue/Products(cba6c104-3006-4af7-a2d1-cbd55f58b939)/$value',
			'format': 'application/zip',
			'creationDate': '2018-09-03T05:12:37.000Z'
		},
		'provider': 'ONDA',
		'summary': 'Date: 2018-09-03T05:12:37.000Z, Instrument: null, Mode: null, Satellite: null, Size: 1.54 GB',
		'title': 'S1A_IW_RAW__0SDV_20180902T052727_20180902T052759_023515_028F75_7325'
},
{'(...7 more results similar to this one, omitted for brevity)'}]
Found 8
```
Now we can import one of those products in WASDI: let's download the first one:
```python
sImportWithDict = wasdi.importProduct(None, None, aoImages[0])
```
We can see a list of the products in the workspace as follows:
```python
asProducts = wasdi.getProductsByActiveWorkspace()
wasdi.wasdiLog(asProducts)
```
The second line logs the list of products
### Running an existing workflow
If you wish to run an existing SNAP workflow you can use `wasdi.executeWorkflow`. For example, if you wish to execute a workflow that calibrates and corrects the georeference of a Sentinel 1 image, you may use the workflow called `LISTSinglePreproc` in this way:
```python
asProducts = wasdi.getProductsByActiveWorkspace()
sStatus = wasdi.executeWorkflow([asProducts[0]], ['lovelyOutput'], 'LISTSinglePreproc')
```
Here the first line gets the list of products and the second calls the workflow `LISTSinglePreproc` on the first product of the workspace and creates another product called `lovelyOutput`.
### A more complete example
Now put everything back together. Create a file called [`myProcessor.py`](https://github.com/fadeoutsoftware/WASDI/blob/develop/libraries/waspy/examples/myProcessor.py) (follow the link to download the file) with the following content:
```python
import wasdi
def run(parameters, processId):
    wasdi.wasdiLog('Here\'s the list of your workspaces:')
    aoWorkspaces = wasdi.getWorkspaces()
    wasdi.wasdiLog(aoWorkspaces)
    wasdi.wasdiLog('The ID of currently selected workspace is:')
    sActiveWorkspace = wasdi.getActiveWorkspaceId()
    wasdi.wasdiLog(sActiveWorkspace)
    wasdi.wasdiLog('Let\'s search some images...')
    aoImages = wasdi.searchEOImages("S1", "2018-09-01", "2018-09-02", 44, 11, 43, 12, sProductType='GRD')
    wasdi.wasdiLog('Found ' + str(len(aoImages)) + ' images')
    wasdi.wasdiLog('Download the first one passing the dictionary...')
    sImportWithDict = wasdi.importProduct(None, None, aoImages[0])
    wasdi.wasdiLog('Import with dict returned: ' + sImportWithDict)
    wasdi.wasdiLog('Now, these are the products in your workspace: ')
    asProducts = wasdi.getProductsByActiveWorkspace()
    wasdi.wasdiLog(asProducts)
    wasdi.wasdiLog('Let\'s run a workflow on the first image to rectify its georeference...')
    sStatus = wasdi.executeWorkflow([asProducts[0]], ['lovelyOutput'], 'LISTSinglePreproc')
    if sStatus == 'DONE':
        wasdi.wasdiLog('The product is now in your workspace, look at it on the website')
    wasdi.wasdiLog('It\'s over!')
def WasdiHelp():
    sHelp = "Wasdi Tutorial"
    return sHelp
```
Then create another file to start the processor. Let's call it [`tutorial.py`](https://github.com/fadeoutsoftware/WASDI/blob/develop/libraries/waspy/examples/tutorial.py) (follow the link to download the file), with the following content:
```python
import myProcessor
import wasdi
bInitResult = wasdi.init('config.json')
if bInitResult:
    myProcessor.run(wasdi.getParametersDict(), '')
```
Now, if you run `tutorial.py`, it will call `myProcessor.py`, which will go through the instructions we saw above. Pro tip: keep the browser open in wasdi.net (make sure you are logged in) and open the workspace you are using, to see the evolution of the script in real time.
### Deploy your processor on WASDI
Finally, to deply our processor on WASDI, you need first to create a text file called [`pip.txt`](https://github.com/fadeoutsoftware/WASDI/blob/develop/libraries/waspy/examples/pip.txt) (follow the link to download the file) containg the packages we imported in `myProcessor.py`, one per line. Since we just imported `wasdi`, it should look like this:
```
wasdi
```
Now, create a zip file containing these two files:
- `myProcessor.py`
- `pip.txt`
You can now upload the zip file on wasdi.net from *Edit* -> *Processor* -> *New WASDI App* by giving it a name and completing the other details. You will need to do this just once.

%package help
Summary:	Development documents and examples for wasdi
Provides:	python3-wasdi-doc
%description help
## Python tutorial
WASPY is the **WAS**DI **Py**thon Library. 
**GET UPDATED DOCUMENTATION, TUTORIALS AND MORE [here](https://wasdi.readthedocs.io/en/latest/index.html)**
### Prerequisites:
mandatory:
- a [WASDI](http://www.wasdi.net) registered user (with a username/password, google users are not supported yet)
- at least one workspace
- some EO products in your workspace
### Installation
To start working with WASPY, just install the library using:
```bash
pip install wasdi
```
To quickly check if the installation worked correctly, try running the following code:
```python
import wasdi
print(wasdi.hello())
```
You should see this kind of output:
```json
{"boolValue":null,"doubleValue":null,"intValue":null,"stringValue":"Hello Wasdi!!"}
```
### Configuration
Create a `config.json` file. It is a standard json file, which is used to store the credentials of the user and some other settings. The syntax is:
```json
“VARIABLE_NAME”: value
```
Hint: exploit an editor which can check the syntax (there are many which can be accessed online for free)
The minimal configuration to begin working with WASPY is:
```json
{
  "USER": "yourUser@wasdi.net",
  "PASSWORD": "yourPasswordHere",
  "WORKSPACE": "nameOfTheWorkspaceYouWantToUse"
}
```
For the other available parameters please refer to the Documentation.
### Start WASPY
To start WASPY and check if everything is working, run the following code:
```python
wasdi.init('./config.json')
```
(Adapt the path if the file is not located in your working directory)
The Lib will read the configuration file, load the user and password, log the user in, and then open the workspace specified in the configuration file. To check if everything is working, try to get the list of workspaces available for the user:
```python
wasdi.getWorkspaces()
```
You should be able to see a result similar to the following one:
```python
[{u'ownerUserId': u'yourUser@wasdi.net',
  u'sharedUsers': [],
  u'workspaceId': u'23ab54f3-b453-2b3e-284a-b6a4243f0f2c',
  u'workspaceName': u'nameOfTheWorkspaceYouWantToUse'},
 {u'ownerUserId': u'yourUser@wasdi.net',
  u'sharedUsers': [],
  u'workspaceId': u'103fbf01-2e68-22d3-bd45-2cf95665dac2',
  u'workspaceName': u'theNameOfAnotherWorkspace'}]
```
The configured Workspace is already opened.  The use can open another workspace using:
```python
wasdi.openWorkspace('theNameOfAnotherWorkspace')
```
and the lib replies showing the workspace unique id:
```python
u'9ce787d4-1d59-4146-8df7-3fc9516d4eb3'
```
To get the list of the products available in the workspace, call
```python
wasdi.getProductsByWorkspace('nameOfTheWorkspaceYouWantToUse')
```
and the lib returns a list of the products in the given workspace:
```python
[u'S1A_IW_GRDH_1SDV_20190517T053543_20190517T053608_027263_0312F1_F071.zip',
u'S1B_IW_RAW__0SDV_20190506T052631_20190506T052703_016119_01E53A_D2AD.zip', u'S1A_IW_GRDH_1SDV_20190517T053608_20190517T053633_027263_0312F1_3382.zip']
```
Now try something more, let's search for some Sentinel 1 images. Let's assume we are interested in images taken from "2018-09-01" to "2018-09-02". Also, we'd better specify a bounding box. Assume we're interested in images with *latitude* in `[43, 44]` and *longitude* in `[11, 12]`. We can think of these coordinates as a rectangle limited by the upper left corner `(44, 11)` and the lower right corner`(43, 12)`. 
 The corresponding code is:
```python
wasdi.wasdiLog('Let\'s search some images')
aoImages = wasdi.searchEOImages("S1", "2018-09-01", "2018-09-02", 44, 11, 43, 12, None, None, None, None)
wasdi.wasdiLog('Found ' + str(len(aoImages)))
```
The output should be similar to this:
```
 Let's search some images
[INFO] waspy.searchEOImages: search results:
[{
		'footprint': 'POLYGON ((8.8724 45.3272, 8.4505 43.3746, 11.4656 43.0981, 11.9901 45.0472, 8.8724 45.3272, 8.8724 45.3272))',
		'id': 'cba6c104-3006-4af7-a2d1-cbd55f58b939',
		'link': 'https://catalogue.onda-dias.eu/dias-catalogue/Products(cba6c104-3006-4af7-a2d1-cbd55f58b939)/$value',
		'preview': None,
		'properties': {
			'offline': 'false',
			'downloadable': '',
			'filename': 'S1A_IW_RAW__0SDV_20180902T052727_20180902T052759_023515_028F75_7325.zip',
			'size': '1.54 GB',
			'pseudopath': 'RADAR/LEVEL-0/2018/09/02, S1/1A/SAR-C/LEVEL-0/IW_RAW__0S/2018/09/02, S1/1A/LEVEL-0/IW_RAW__0S/2018/09/02, S1/SAR-C/LEVEL-0/IW_RAW__0S/2018/09/02, S1/LEVEL-0/IW_RAW__0S/2018/09/02, 2014-016A/SAR-C/LEVEL-0/IW_RAW__0S/2018/09/02, 2014-016A/LEVEL-0/IW_RAW__0S/2018/09/02',
			'link': 'https://catalogue.onda-dias.eu/dias-catalogue/Products(cba6c104-3006-4af7-a2d1-cbd55f58b939)/$value',
			'format': 'application/zip',
			'creationDate': '2018-09-03T05:12:37.000Z'
		},
		'provider': 'ONDA',
		'summary': 'Date: 2018-09-03T05:12:37.000Z, Instrument: null, Mode: null, Satellite: null, Size: 1.54 GB',
		'title': 'S1A_IW_RAW__0SDV_20180902T052727_20180902T052759_023515_028F75_7325'
},
{'(...7 more results similar to this one, omitted for brevity)'}]
Found 8
```
Now we can import one of those products in WASDI: let's download the first one:
```python
sImportWithDict = wasdi.importProduct(None, None, aoImages[0])
```
We can see a list of the products in the workspace as follows:
```python
asProducts = wasdi.getProductsByActiveWorkspace()
wasdi.wasdiLog(asProducts)
```
The second line logs the list of products
### Running an existing workflow
If you wish to run an existing SNAP workflow you can use `wasdi.executeWorkflow`. For example, if you wish to execute a workflow that calibrates and corrects the georeference of a Sentinel 1 image, you may use the workflow called `LISTSinglePreproc` in this way:
```python
asProducts = wasdi.getProductsByActiveWorkspace()
sStatus = wasdi.executeWorkflow([asProducts[0]], ['lovelyOutput'], 'LISTSinglePreproc')
```
Here the first line gets the list of products and the second calls the workflow `LISTSinglePreproc` on the first product of the workspace and creates another product called `lovelyOutput`.
### A more complete example
Now put everything back together. Create a file called [`myProcessor.py`](https://github.com/fadeoutsoftware/WASDI/blob/develop/libraries/waspy/examples/myProcessor.py) (follow the link to download the file) with the following content:
```python
import wasdi
def run(parameters, processId):
    wasdi.wasdiLog('Here\'s the list of your workspaces:')
    aoWorkspaces = wasdi.getWorkspaces()
    wasdi.wasdiLog(aoWorkspaces)
    wasdi.wasdiLog('The ID of currently selected workspace is:')
    sActiveWorkspace = wasdi.getActiveWorkspaceId()
    wasdi.wasdiLog(sActiveWorkspace)
    wasdi.wasdiLog('Let\'s search some images...')
    aoImages = wasdi.searchEOImages("S1", "2018-09-01", "2018-09-02", 44, 11, 43, 12, sProductType='GRD')
    wasdi.wasdiLog('Found ' + str(len(aoImages)) + ' images')
    wasdi.wasdiLog('Download the first one passing the dictionary...')
    sImportWithDict = wasdi.importProduct(None, None, aoImages[0])
    wasdi.wasdiLog('Import with dict returned: ' + sImportWithDict)
    wasdi.wasdiLog('Now, these are the products in your workspace: ')
    asProducts = wasdi.getProductsByActiveWorkspace()
    wasdi.wasdiLog(asProducts)
    wasdi.wasdiLog('Let\'s run a workflow on the first image to rectify its georeference...')
    sStatus = wasdi.executeWorkflow([asProducts[0]], ['lovelyOutput'], 'LISTSinglePreproc')
    if sStatus == 'DONE':
        wasdi.wasdiLog('The product is now in your workspace, look at it on the website')
    wasdi.wasdiLog('It\'s over!')
def WasdiHelp():
    sHelp = "Wasdi Tutorial"
    return sHelp
```
Then create another file to start the processor. Let's call it [`tutorial.py`](https://github.com/fadeoutsoftware/WASDI/blob/develop/libraries/waspy/examples/tutorial.py) (follow the link to download the file), with the following content:
```python
import myProcessor
import wasdi
bInitResult = wasdi.init('config.json')
if bInitResult:
    myProcessor.run(wasdi.getParametersDict(), '')
```
Now, if you run `tutorial.py`, it will call `myProcessor.py`, which will go through the instructions we saw above. Pro tip: keep the browser open in wasdi.net (make sure you are logged in) and open the workspace you are using, to see the evolution of the script in real time.
### Deploy your processor on WASDI
Finally, to deply our processor on WASDI, you need first to create a text file called [`pip.txt`](https://github.com/fadeoutsoftware/WASDI/blob/develop/libraries/waspy/examples/pip.txt) (follow the link to download the file) containg the packages we imported in `myProcessor.py`, one per line. Since we just imported `wasdi`, it should look like this:
```
wasdi
```
Now, create a zip file containing these two files:
- `myProcessor.py`
- `pip.txt`
You can now upload the zip file on wasdi.net from *Edit* -> *Processor* -> *New WASDI App* by giving it a name and completing the other details. You will need to do this just once.

%prep
%autosetup -n wasdi-0.8.0.2

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-wasdi -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 0.8.0.2-1
- Package Spec generated