| # Run this script in the directory where you want to save the conda-lock file at | |
| # Activate env to make a `conda-lock.yml` file of | |
| # conda activate venv | |
| # Export env spec list using conda/mamba as `environment.yml` | |
| conda env export > environment.yml | |
| # Make conda-lock file | |
| # Edit platform tag `-p` | |
| # For multiple platforms just add more, e.g. `-p osx-64 -p linux-64` | |
| conda-lock -f environment.yml -p linux-64 | |
| # When new packages are installed, the conda environment changes and |
| # FIRST, Activate env to add to Jupyter, e.g. venv | |
| # conda activate venv | |
| # Then run this script | |
| # Edit ENV_NAME variable to name the kernel | |
| # Kernel names can only contain ASCII letters and numbers and these separators: - . _ (hyphen, period, and underscore). | |
| # This will be the label in JupyterLab | |
| ENV_NAME=Conda | |
| # Install ipykernel via conda/mamba | |
| conda install ipykernel | |
| # Add current active environment |
| version: '3.3' | |
| services: | |
| lab: | |
| ports: | |
| - '10000:8888' | |
| container_name: datascience-notebook | |
| volumes: | |
| - '${PWD}:/home/jovyan/work' | |
| image: jupyter/datascience-notebook | |
| # On remote (c24.local) | |
| jupyter notebook --no-browser --port=10000 | |
| # On current computer | |
| # create a tunnel that runs in the bg | |
| # first set of name:port is local, second set is remote | |
| ssh -f -N -L localhost:10024:localhost:10000 kent@c24.local |
| three_to_one_aa = { | |
| 'Ala': 'A', | |
| 'Arg': 'R', | |
| 'Asn': 'N', | |
| 'Asp': 'D', | |
| 'Asx': 'B', | |
| 'Cys': 'C', | |
| 'Glu': 'E', | |
| 'Gln': 'Q', | |
| 'Glx': 'Z', |
| [Down] o:50403.85 h:50444.31 l:50222.59 c:50303.48 hao:50410.95 hac:50343.5575 vol:4420.06 | |
| [Down] o:50302.05 h:50313.53 l:50120.15 c:50226.84 hao:50352.95 hac:50240.6425 vol:5617.008 | |
| [Down] o:50226.84 h:50309.2 l:50151 c:50209.99 hao:50264.445 hac:50224.25749999999 vol:3995.061 | |
| [Up] o:50210 h:50393.25 l:50150 c:50160.01 hao:50218.42 hac:50228.315 vol:6494.194 | |
| [Down] o:50160 h:50173.02 l:49730 c:49769.26 hao:50185 hac:49958.07 vol:26268.227 | |
| [Down] o:49769.26 h:49950 l:49612.18 c:49772.69 hao:49964.630000000005 hac:49776.0325 vol:13958.26 | |
| [Down] o:49772.69 h:49789.86 l:49314.14 c:49571.64 hao:49770.975000000006 hac:49612.082500000004 vol:18062.925 | |
| [Down] o:49571.64 h:49680 l:49477.44 c:49660.64 hao:49672.165 hac:49597.43000000001 vol:6384.345 | |
| [Up] o:49660.64 h:49787 l:49410 c:49756.91 hao:49616.14 hac:49653.637500000004 vol:887 |
| [Up] HeikinAshiKlineSummary { open_time: 1629733620000, open: 49660.65, high: 49679.19, low: 49623.77, close: 49653.49, volume: 340.212, close_time: 1629733679999, quote_asset_volume: 16891095.04434, number_of_trades: 3343, taker_buy_base_asset_volume: 126.96, taker_buy_quote_asset_volume: 6303688.27145, ha_open: 49647.985, ha_high: inf, ha_low: 49623.77, ha_close: 49654.274999999994 } | |
| [Down] HeikinAshiKlineSummary { open_time: 1629733680000, open: 49653.49, high: 49677.32, low: 49635.24, close: 49660.0, volume: 212.477, close_time: 1629733739999, quote_asset_volume: 10551853.58003, number_of_trades: 2612, taker_buy_base_asset_volume: 117.889, taker_buy_quote_asset_volume: 5854917.85542, ha_open: 49657.07, ha_high: inf, ha_low: 49635.24, ha_close: 49656.5125 } | |
| [Down] HeikinAshiKlineSummary { open_time: 1629733740000, open: 49659.82, high: 49674.26, low: 49629.0, close: 49630.01, volume: 169.779, close_time: 1629733799999, quote_asset_volume: 8428987.70956, number_of_trades: 2096, taker_buy_base_asset_vol |
The following instructions have worked for me running Mac OS 11.4.0 X64 with Node.js 16.4.0, Rust 1.53 and Tauri 1.0.0-beta.4.
First, cd to parent directory where the root directory of the app will be located, ie: /home/me/src.
Use the following command to generate the react directory structure:
| def carrier_square_wave_generator(gpio, frequency, signal_length): | |
| """ | |
| Generate carrier square wave. | |
| """ | |
| waveform = [] | |
| # frequency is number of cycles per second, usually 38 kHz | |
| micro_per_cycle = 1000.0 / frequency # 1 / kHz is millisecond, * 1000 is microsecond | |
| # number of cycles during the signal | |
| # signal_length is in microseconds | |
| num_cycles = int(round(signal_length / micro_per_cycle)) |
| # Add .asoundrc file | |
| Copied from https://gist.github.com/AfzalivE/54214bf437ca1775b5b8e7934cc137db | |
| ``` | |
| # ps3 eye + 3.5mm speaker Raspberry Pi | |
| ## Suggested by http://julius.sourceforge.jp/forum/viewtopic.php?f=9&t=66 | |
| ## Modified by https://github.com/afzalive | |
| pcm.jack { | |
| type hw | |
| card ALSA |