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|
%global _empty_manifest_terminate_build 0
Name: python-openbci-stream
Version: 1.0.12
Release: 1
Summary: High level Python module for EEG/EMG/ECG acquisition and distributed streaming for OpenBCI Cyton board.
License: BSD-2-Clause
URL: https://pypi.org/project/openbci-stream/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/4b/25/e857af3c56c468968fc95420bc6354d7d3b0418aaee5c7135f1e172e212b/openbci-stream-1.0.12.tar.gz
BuildArch: noarch
Requires: python3-ntplib
Requires: python3-tables
Requires: python3-numpy
Requires: python3-mne
Requires: python3-requests
Requires: python3-colorama
Requires: python3-scipy
Requires: python3-kafka-python
Requires: python3-rpyc
Requires: python3-netifaces
Requires: python3-nmap
Requires: python3-pyserial
Requires: python3-systemd-service
%description
# OpenBCI-Stream
High level Python module for EEG/EMG/ECG acquisition and distributed streaming for OpenBCI Cyton board.
[](https://openbci-stream.readthedocs.io/en/latest/?badge=latest)
Comprise a set of scripts that deals with the configuration and connection with the board, also is compatible with both connection modes supported by [Cyton](https://shop.openbci.com/products/cyton-biosensing-board-8-channel?variant=38958638542): RFduino (Serial dongle) and Wi-Fi (with the OpenBCI Wi-Fi Shield). These drivers are a stand-alone library that can handle the board from three different endpoints: (i) a [Command-Line Interface](06-command_line_interface.ipynb) (CLI) with simple instructions configure, start and stop data acquisition, debug stream status, and register events markers; (ii) a [Python Module](03-data_acuisition.ipynb) with high-level instructions and asynchronous acquisition; (iii) an object-proxying using Remote Python Call (RPyC) for [distributed implementations](A4-server-based-acquisition.ipynb) that can manipulate the Python modules as if they were local, this last mode needs a daemon running in the remote host that will listen to connections and driving instructions.
The main functionality of the drivers live on to serve real-time and distributed access to data flow, even on single machine implementations, this is achieved by implementing [Kafka](https://kafka.apache.org/) and their capabilities to create multiple topics for classifying the streaming, these topics are used to separate the neurophysiological data from the [event markers](05-stream_markers), so the clients can subscribe to a specific topic for injecting or read content, this means that is possible to implement an event register in a separate process that stream markers for all clients in real-time without handle dense time-series data. A crucial issue that stays on [time synchronization](A4-server-based_acquisition.ipynb#Step-5---Configure-time-server), all systems components in the network should have the same real-time protocol (RTP) server reference.
## Main features
* **Asynchronous acquisition:** Acquisition and deserialization are done in uninterrupted parallel processes. In this way, the sampling rate keeps stable as long as possible.
* **Distributed streaming system:** The acquisition, processing, visualizations, and any other system that needs to be fed with EEG/EMG/ECG real-time data can run with their architecture.
* **Remote board handle:** Same code syntax for developing and debug Cython boards connected to any node in the distributed system.
* **Command-line interface:** A simple interface for handle the start, stop, and access to data stream directly from the command line.
* **Markers/Events handler:** Besides the marker boardmode available in Cyton, a stream channel for the reading and writing of markers is available for use in any development.
* **Multiple boards:** Is possible to use multiple OpenBCI boards just by adding multiple endpoints to the commands.
## Examples
```python
# Acquisition with blocking call
from openbci_stream.acquisition import Cyton
openbci = Cyton('serial', endpoint='/dev/ttyUSB0', capture_stream=True)
# blocking call
openbci.stream(15) # collect data for 15 seconds
# openbci.eeg_time_series
# openbci.aux_time_series
# openbci.timestamp_time_series
```
```python
# Acquisition with asynchronous call
from openbci_stream.acquisition import Cyton
openbci = Cyton('wifi', endpoint='192.68.1.113', capture_stream=True)
openbci.stream(15) # collect data for 15 seconds
# asynchronous call
openbci.start_stream()
time.sleep(15) # collect data for 15 seconds
openbci.stop_stream()
```
```python
# Remote acquisition
from openbci_stream.acquisition import Cyton
openbci = Cyton('serial', endpoint='/dev/ttyUSB0', host='192.168.1.1', capture_stream=True)
# blocking call
openbci.stream(15) # collect data for 15 seconds
```
```python
# Consumer for active streamming
from openbci_stream.acquisition import OpenBCIConsumer
with OpenBCIConsumer() as stream:
for i, message in enumerate(stream):
if message.topic == 'eeg':
print(f"received {message.value['samples']} samples")
if i == 9:
break
```
```python
# Create stream then consume data
from openbci_stream.acquisition import OpenBCIConsumer
with OpenBCIConsumer(mode='serial', endpoint='/dev/ttyUSB0', streaming_package_size=250) as (stream, openbci):
t0 = time.time()
for i, message in enumerate(stream):
if message.topic == 'eeg':
print(f"{i}: received {message.value['samples']} samples")
t0 = time.time()
if i == 9:
break
```
```python
# Acquisition with multiple boards
from openbci_stream.acquisition import Cyton
openbci = Cyton('wifi', endpoint=['192.68.1.113', '192.68.1.185'], capture_stream=True)
openbci.stream(15) # collect data for 15 seconds
# asynchronous call
openbci.start_stream()
time.sleep(15) # collect data for 15 seconds
openbci.stop_stream()
```
%package -n python3-openbci-stream
Summary: High level Python module for EEG/EMG/ECG acquisition and distributed streaming for OpenBCI Cyton board.
Provides: python-openbci-stream
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-openbci-stream
# OpenBCI-Stream
High level Python module for EEG/EMG/ECG acquisition and distributed streaming for OpenBCI Cyton board.
[](https://openbci-stream.readthedocs.io/en/latest/?badge=latest)
Comprise a set of scripts that deals with the configuration and connection with the board, also is compatible with both connection modes supported by [Cyton](https://shop.openbci.com/products/cyton-biosensing-board-8-channel?variant=38958638542): RFduino (Serial dongle) and Wi-Fi (with the OpenBCI Wi-Fi Shield). These drivers are a stand-alone library that can handle the board from three different endpoints: (i) a [Command-Line Interface](06-command_line_interface.ipynb) (CLI) with simple instructions configure, start and stop data acquisition, debug stream status, and register events markers; (ii) a [Python Module](03-data_acuisition.ipynb) with high-level instructions and asynchronous acquisition; (iii) an object-proxying using Remote Python Call (RPyC) for [distributed implementations](A4-server-based-acquisition.ipynb) that can manipulate the Python modules as if they were local, this last mode needs a daemon running in the remote host that will listen to connections and driving instructions.
The main functionality of the drivers live on to serve real-time and distributed access to data flow, even on single machine implementations, this is achieved by implementing [Kafka](https://kafka.apache.org/) and their capabilities to create multiple topics for classifying the streaming, these topics are used to separate the neurophysiological data from the [event markers](05-stream_markers), so the clients can subscribe to a specific topic for injecting or read content, this means that is possible to implement an event register in a separate process that stream markers for all clients in real-time without handle dense time-series data. A crucial issue that stays on [time synchronization](A4-server-based_acquisition.ipynb#Step-5---Configure-time-server), all systems components in the network should have the same real-time protocol (RTP) server reference.
## Main features
* **Asynchronous acquisition:** Acquisition and deserialization are done in uninterrupted parallel processes. In this way, the sampling rate keeps stable as long as possible.
* **Distributed streaming system:** The acquisition, processing, visualizations, and any other system that needs to be fed with EEG/EMG/ECG real-time data can run with their architecture.
* **Remote board handle:** Same code syntax for developing and debug Cython boards connected to any node in the distributed system.
* **Command-line interface:** A simple interface for handle the start, stop, and access to data stream directly from the command line.
* **Markers/Events handler:** Besides the marker boardmode available in Cyton, a stream channel for the reading and writing of markers is available for use in any development.
* **Multiple boards:** Is possible to use multiple OpenBCI boards just by adding multiple endpoints to the commands.
## Examples
```python
# Acquisition with blocking call
from openbci_stream.acquisition import Cyton
openbci = Cyton('serial', endpoint='/dev/ttyUSB0', capture_stream=True)
# blocking call
openbci.stream(15) # collect data for 15 seconds
# openbci.eeg_time_series
# openbci.aux_time_series
# openbci.timestamp_time_series
```
```python
# Acquisition with asynchronous call
from openbci_stream.acquisition import Cyton
openbci = Cyton('wifi', endpoint='192.68.1.113', capture_stream=True)
openbci.stream(15) # collect data for 15 seconds
# asynchronous call
openbci.start_stream()
time.sleep(15) # collect data for 15 seconds
openbci.stop_stream()
```
```python
# Remote acquisition
from openbci_stream.acquisition import Cyton
openbci = Cyton('serial', endpoint='/dev/ttyUSB0', host='192.168.1.1', capture_stream=True)
# blocking call
openbci.stream(15) # collect data for 15 seconds
```
```python
# Consumer for active streamming
from openbci_stream.acquisition import OpenBCIConsumer
with OpenBCIConsumer() as stream:
for i, message in enumerate(stream):
if message.topic == 'eeg':
print(f"received {message.value['samples']} samples")
if i == 9:
break
```
```python
# Create stream then consume data
from openbci_stream.acquisition import OpenBCIConsumer
with OpenBCIConsumer(mode='serial', endpoint='/dev/ttyUSB0', streaming_package_size=250) as (stream, openbci):
t0 = time.time()
for i, message in enumerate(stream):
if message.topic == 'eeg':
print(f"{i}: received {message.value['samples']} samples")
t0 = time.time()
if i == 9:
break
```
```python
# Acquisition with multiple boards
from openbci_stream.acquisition import Cyton
openbci = Cyton('wifi', endpoint=['192.68.1.113', '192.68.1.185'], capture_stream=True)
openbci.stream(15) # collect data for 15 seconds
# asynchronous call
openbci.start_stream()
time.sleep(15) # collect data for 15 seconds
openbci.stop_stream()
```
%package help
Summary: Development documents and examples for openbci-stream
Provides: python3-openbci-stream-doc
%description help
# OpenBCI-Stream
High level Python module for EEG/EMG/ECG acquisition and distributed streaming for OpenBCI Cyton board.
[](https://openbci-stream.readthedocs.io/en/latest/?badge=latest)
Comprise a set of scripts that deals with the configuration and connection with the board, also is compatible with both connection modes supported by [Cyton](https://shop.openbci.com/products/cyton-biosensing-board-8-channel?variant=38958638542): RFduino (Serial dongle) and Wi-Fi (with the OpenBCI Wi-Fi Shield). These drivers are a stand-alone library that can handle the board from three different endpoints: (i) a [Command-Line Interface](06-command_line_interface.ipynb) (CLI) with simple instructions configure, start and stop data acquisition, debug stream status, and register events markers; (ii) a [Python Module](03-data_acuisition.ipynb) with high-level instructions and asynchronous acquisition; (iii) an object-proxying using Remote Python Call (RPyC) for [distributed implementations](A4-server-based-acquisition.ipynb) that can manipulate the Python modules as if they were local, this last mode needs a daemon running in the remote host that will listen to connections and driving instructions.
The main functionality of the drivers live on to serve real-time and distributed access to data flow, even on single machine implementations, this is achieved by implementing [Kafka](https://kafka.apache.org/) and their capabilities to create multiple topics for classifying the streaming, these topics are used to separate the neurophysiological data from the [event markers](05-stream_markers), so the clients can subscribe to a specific topic for injecting or read content, this means that is possible to implement an event register in a separate process that stream markers for all clients in real-time without handle dense time-series data. A crucial issue that stays on [time synchronization](A4-server-based_acquisition.ipynb#Step-5---Configure-time-server), all systems components in the network should have the same real-time protocol (RTP) server reference.
## Main features
* **Asynchronous acquisition:** Acquisition and deserialization are done in uninterrupted parallel processes. In this way, the sampling rate keeps stable as long as possible.
* **Distributed streaming system:** The acquisition, processing, visualizations, and any other system that needs to be fed with EEG/EMG/ECG real-time data can run with their architecture.
* **Remote board handle:** Same code syntax for developing and debug Cython boards connected to any node in the distributed system.
* **Command-line interface:** A simple interface for handle the start, stop, and access to data stream directly from the command line.
* **Markers/Events handler:** Besides the marker boardmode available in Cyton, a stream channel for the reading and writing of markers is available for use in any development.
* **Multiple boards:** Is possible to use multiple OpenBCI boards just by adding multiple endpoints to the commands.
## Examples
```python
# Acquisition with blocking call
from openbci_stream.acquisition import Cyton
openbci = Cyton('serial', endpoint='/dev/ttyUSB0', capture_stream=True)
# blocking call
openbci.stream(15) # collect data for 15 seconds
# openbci.eeg_time_series
# openbci.aux_time_series
# openbci.timestamp_time_series
```
```python
# Acquisition with asynchronous call
from openbci_stream.acquisition import Cyton
openbci = Cyton('wifi', endpoint='192.68.1.113', capture_stream=True)
openbci.stream(15) # collect data for 15 seconds
# asynchronous call
openbci.start_stream()
time.sleep(15) # collect data for 15 seconds
openbci.stop_stream()
```
```python
# Remote acquisition
from openbci_stream.acquisition import Cyton
openbci = Cyton('serial', endpoint='/dev/ttyUSB0', host='192.168.1.1', capture_stream=True)
# blocking call
openbci.stream(15) # collect data for 15 seconds
```
```python
# Consumer for active streamming
from openbci_stream.acquisition import OpenBCIConsumer
with OpenBCIConsumer() as stream:
for i, message in enumerate(stream):
if message.topic == 'eeg':
print(f"received {message.value['samples']} samples")
if i == 9:
break
```
```python
# Create stream then consume data
from openbci_stream.acquisition import OpenBCIConsumer
with OpenBCIConsumer(mode='serial', endpoint='/dev/ttyUSB0', streaming_package_size=250) as (stream, openbci):
t0 = time.time()
for i, message in enumerate(stream):
if message.topic == 'eeg':
print(f"{i}: received {message.value['samples']} samples")
t0 = time.time()
if i == 9:
break
```
```python
# Acquisition with multiple boards
from openbci_stream.acquisition import Cyton
openbci = Cyton('wifi', endpoint=['192.68.1.113', '192.68.1.185'], capture_stream=True)
openbci.stream(15) # collect data for 15 seconds
# asynchronous call
openbci.start_stream()
time.sleep(15) # collect data for 15 seconds
openbci.stop_stream()
```
%prep
%autosetup -n openbci-stream-1.0.12
%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-openbci-stream -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.12-1
- Package Spec generated
|
