| Name | magma-multigas JSON |
| Version |
1.3.0
JSON |
| download |
| home_page | None |
| Summary | MultiGas package for internal use. A collaboration between CVGHM and USGS |
| upload_time | 2024-08-08 11:29:18 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | None |
| keywords |
gas
volcano
multigas
usgs
cvghm
pvmbg
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# magma-multigas
Python package for multigas sensor.
## Installation
---
Make sure you have at least Python 3.10 installed. You can install the package using `pip`
```python
pip install magma-multigas
```
## How to use
---
Import the package first:
```python
from magma_multigas import MultiGas
```
Then lets add all the required files. Please change the files location with your multi gas data. At the moment we are excluding the `span` data files. Will add later in future development.
```python
two_seconds = 'D:\\Projects\\magma-multigas\\input\\TANG_RTU_ChemData_Sec2.dat'
six_hours = 'D:\\Projects\\magma-multigas\\input\\TANG_RTU_Data_6Hr.dat'
one_minute = 'D:\\Projects\\magma-multigas\\input\\TANG_RTU_Wx_Min1.dat'
zero = 'D:\\Projects\\magma-multigas\\input\\TANG_RTU_Zero_Data.dat'
```
Initiate MultiGas module with the code below. This code will correct "NAN" data, and create a new file into your current poject directory. The default location is `<your project directory>/output/normalize` .
```python
multigas = MultiGas(
two_seconds=two_seconds,
six_hours=six_hours,
one_minute=one_minute,
zero=zero
)
```
By initiating the module, we can check the output after run the code.
```markdown
💾 New file saved to D:\Projects\magma-multigas\output\normalize\TANG_RTU_ChemData_Sec2.dat
💾 New file saved to D:\Projects\magma-multigas\output\normalize\TANG_RTU_Data_6Hr.dat
💾 New file saved to D:\Projects\magma-multigas\output\normalize\TANG_RTU_Wx_Min1.dat
💾 New file saved to D:\Projects\magma-multigas\output\normalize\TANG_RTU_Zero_Data.dat
```
## Slicing and export for specific time
---
We can filter the data we have by using the code below. At this example we will select data between `start_date = 2024-05-17` and `end_date = 2024-06-18`.
```python
data_filtered = multigas.where_date_between(start_date='2024-05-17', end_date='2024-06-18').save(file_type='excel')
```
We can also save the filtering results. Only `excel`,`xls`,`xlsx` and `csv` are supported.
```python
data_filtered.save(file_type='excel')
```
All files would be saved into `<your project directory>/output/<file_type>`. You can also check the save location after run the `save()` command.
```markdown
✅ Data saved to: D:\Projects\magma-multigas\output\excel\two_seconds_2024-05-17_2024-06-18_TANG_RTU_ChemData_Sec2.xlsx
✅ Data saved to: D:\Projects\magma-multigas\output\excel\six_hours_2024-05-17_2024-06-18_TANG_RTU_Data_6Hr.xlsx
✅ Data saved to: D:\Projects\magma-multigas\output\excel\one_minute_2024-05-17_2024-06-18_TANG_RTU_Wx_Min1.xlsx
✅ Data saved to: D:\Projects\magma-multigas\output\excel\zero_2024-05-17_2024-06-18_TANG_RTU_Zero_Data.xlsx
```
## Selecting Data
---
For now, this package only support 4 type of data.
1. `two_seconds`
2. `six_hours`
3. `one_minute`
4. `zero`
To working on specific dataset, we can do it like this. To choose `two_seconds` data:
```python
two_seconds_data = multigas.two_seconds
```
or:
```python
two_seconds_data = multigas.select('two_seconds').get()
```
Both method will result the same. You can change the `two_seconds` parameter with the available type of data.
## Data Preview
---
After selecting, we can do a quick review by calling `df` parameter:
```python
two_seconds_data.df
```
For anyone not familiar with `df` abbreviation, it's stand for _dataframe_. Just imagine it as an excel with header, but it is in python.
You can also see the columns name:
```python
two_seconds_data.columns
```
It will show all the columns name:
## Filtering
We can do fluent filtering data by using this code below. And it also supports chaining filtering.
```python
filtered_two_seconds = (two_seconds_data
.select_columns(column_names=['H2O','CO2','SO2','H2S','S_total'])
.where_date_between(start_date='2024-06-12', end_date='2024-06-18')
.where('Status_Flag', '==', 0)
.where_values_between(column_name='SO2', start_value=-0.129, end_value=-0.127)
.where_values_between(column_name='H2O', start_value=-260, end_value=-228)
)
```
We can read the above code as is:
> By using `two_seconds_data`, let's select specific columns, such as: `H2O, CO2, SO2, S_Total` where the `Status_Flag` should have a `0` value. And the `SO2` columns must be between `-0.129` and `-0.127`. In addition `H2O` values would be filtered between `-260` and `-228`.
To see the results, please run the `get()` method:
```python
filtered_two_seconds.get()
```
You can see the example of the results below. Here we only found 1 result based on our query filtering.
To check and count the results:
```python
filtered_two_seconds.count()
```
## Save Filtering Results
---
We can save it, using `save_as()` method. You can change the `file_type` parameter between `excel`,`xls`,`xlsx`, or `csv`.
```python
filtered_two_seconds.save_as(file_type='excel')
```
You will get the information where your file is saved. By default, it should be in your `output\<file_type>` directory. Here is the example of the output:
```markdown
✅ Data saved to: D:\Projects\magma-multigas\output\excel\two_seconds_2024-06-16_2024-06-16_TANG_RTU_ChemData_Sec2.xlsx
```
## Plot
---
This package provide some basic functionality to plot some data. For the simplicity, we will use `six_hours` data as an example. We will do all the basic things above, from extracting, selecting, and filtering.
#### Selecting Six Hours
```python
six_hours_data = multigas.select('six_hours').get()
```
#### Date Filtering
```python
filtered_six_hours = six_hours_data.where_date_between(start_date='2024-05-17', end_date='2024-06-18')
```
#### Plot Initiating
Using `plot()` method to initiate
```python
plot_six_hours = filtered_six_hours.plot()
```
#### Plot Avg. CO2, H2S, and SO2
This package has some default plotting method. We will use `plot_co2_so2_h2s()` as an example:
```python
plot_six_hours.plot_co2_so2_h2s()
```
You can see the result here:
From the plot above we can see there ae some anomaly values (below 400 for Av. CO2 Lowpass). We can re-filter it once again to optimize the result. In this case we will select column `Avg_CO2_lowpass` which value greater than or equal to `250`
```python
filtered_six_hours = filtered_six_hours.where('Avg_CO2_lowpass', '>=', 250)
```
Then plot it:
```python
filtered_six_hours.plot().plot_co2_so2_h2s()
```
Result:
We can also plot as an individual by adding parameter `plot_as_individual=True`
```python
filtered_six_hours.plot().plot_co2_so2_h2s(
plot_as_individual=True
)
```
And the result:
### Plot Specific Column(s)
Select columns to plot:
```python
columns_to_plot = ['Avg_Wind_Speed', 'Avg_Wind_Direction', 'Avg_H2O', 'Avg_CO2_lowpass']
```
Then plot it:
```python
filtered_six_hours.plot(height=2).plot_columns(columns_to_plot)
```
Results:
Raw data
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"description": "# magma-multigas\nPython package for multigas sensor.\n\n## Installation\n\n---\n\nMake sure you have at least Python 3.10 installed. You can install the package using `pip`\n```python\npip install magma-multigas\n```\n\n## How to use\n\n---\n\nImport the package first:\n```python\nfrom magma_multigas import MultiGas\n```\n\nThen lets add all the required files. Please change the files location with your multi gas data. At the moment we are excluding the `span` data files. Will add later in future development.\n```python\ntwo_seconds = 'D:\\\\Projects\\\\magma-multigas\\\\input\\\\TANG_RTU_ChemData_Sec2.dat'\nsix_hours = 'D:\\\\Projects\\\\magma-multigas\\\\input\\\\TANG_RTU_Data_6Hr.dat'\none_minute = 'D:\\\\Projects\\\\magma-multigas\\\\input\\\\TANG_RTU_Wx_Min1.dat'\nzero = 'D:\\\\Projects\\\\magma-multigas\\\\input\\\\TANG_RTU_Zero_Data.dat'\n```\n\nInitiate MultiGas module with the code below. This code will correct \"NAN\" data, and create a new file into your current poject directory. The default location is `<your project directory>/output/normalize` . \n```python\nmultigas = MultiGas(\n two_seconds=two_seconds,\n six_hours=six_hours,\n one_minute=one_minute,\n zero=zero\n)\n```\n\nBy initiating the module, we can check the output after run the code.\n```markdown\n\ud83d\udcbe New file saved to D:\\Projects\\magma-multigas\\output\\normalize\\TANG_RTU_ChemData_Sec2.dat\n\ud83d\udcbe New file saved to D:\\Projects\\magma-multigas\\output\\normalize\\TANG_RTU_Data_6Hr.dat\n\ud83d\udcbe New file saved to D:\\Projects\\magma-multigas\\output\\normalize\\TANG_RTU_Wx_Min1.dat\n\ud83d\udcbe New file saved to D:\\Projects\\magma-multigas\\output\\normalize\\TANG_RTU_Zero_Data.dat\n```\n\n## Slicing and export for specific time\n\n---\n\nWe can filter the data we have by using the code below. At this example we will select data between `start_date = 2024-05-17` and `end_date = 2024-06-18`.\n```python\ndata_filtered = multigas.where_date_between(start_date='2024-05-17', end_date='2024-06-18').save(file_type='excel')\n```\n\nWe can also save the filtering results. Only `excel`,`xls`,`xlsx` and `csv` are supported.\n```python\ndata_filtered.save(file_type='excel')\n```\n\nAll files would be saved into `<your project directory>/output/<file_type>`. You can also check the save location after run the `save()` command.\n```markdown\n\u2705 Data saved to: D:\\Projects\\magma-multigas\\output\\excel\\two_seconds_2024-05-17_2024-06-18_TANG_RTU_ChemData_Sec2.xlsx\n\u2705 Data saved to: D:\\Projects\\magma-multigas\\output\\excel\\six_hours_2024-05-17_2024-06-18_TANG_RTU_Data_6Hr.xlsx\n\u2705 Data saved to: D:\\Projects\\magma-multigas\\output\\excel\\one_minute_2024-05-17_2024-06-18_TANG_RTU_Wx_Min1.xlsx\n\u2705 Data saved to: D:\\Projects\\magma-multigas\\output\\excel\\zero_2024-05-17_2024-06-18_TANG_RTU_Zero_Data.xlsx\n```\n\n## Selecting Data\n\n---\n\nFor now, this package only support 4 type of data.\n1. `two_seconds`\n2. `six_hours`\n3. `one_minute`\n4. `zero`\n\nTo working on specific dataset, we can do it like this. To choose `two_seconds` data:\n```python\ntwo_seconds_data = multigas.two_seconds\n```\nor:\n```python\ntwo_seconds_data = multigas.select('two_seconds').get()\n```\n\nBoth method will result the same. You can change the `two_seconds` parameter with the available type of data.\n\n## Data Preview\n\n---\n\nAfter selecting, we can do a quick review by calling `df` parameter:\n```python\ntwo_seconds_data.df\n```\nFor anyone not familiar with `df` abbreviation, it's stand for _dataframe_. Just imagine it as an excel with header, but it is in python.\n\n\n\nYou can also see the columns name:\n```python\ntwo_seconds_data.columns\n```\nIt will show all the columns name:\n\n\n## Filtering\nWe can do fluent filtering data by using this code below. And it also supports chaining filtering. \n```python\nfiltered_two_seconds = (two_seconds_data\n .select_columns(column_names=['H2O','CO2','SO2','H2S','S_total'])\n .where_date_between(start_date='2024-06-12', end_date='2024-06-18')\n .where('Status_Flag', '==', 0)\n .where_values_between(column_name='SO2', start_value=-0.129, end_value=-0.127)\n .where_values_between(column_name='H2O', start_value=-260, end_value=-228)\n )\n```\nWe can read the above code as is:\n> By using `two_seconds_data`, let's select specific columns, such as: `H2O, CO2, SO2, S_Total` where the `Status_Flag` should have a `0` value. And the `SO2` columns must be between `-0.129` and `-0.127`. In addition `H2O` values would be filtered between `-260` and `-228`.\n\nTo see the results, please run the `get()` method:\n```python\nfiltered_two_seconds.get()\n```\n\nYou can see the example of the results below. Here we only found 1 result based on our query filtering.\n\n\nTo check and count the results:\n```python\nfiltered_two_seconds.count()\n```\n\n## Save Filtering Results\n\n---\n\nWe can save it, using `save_as()` method. You can change the `file_type` parameter between `excel`,`xls`,`xlsx`, or `csv`.\n```python\nfiltered_two_seconds.save_as(file_type='excel')\n```\n\nYou will get the information where your file is saved. By default, it should be in your `output\\<file_type>` directory. Here is the example of the output:\n```markdown\n\u2705 Data saved to: D:\\Projects\\magma-multigas\\output\\excel\\two_seconds_2024-06-16_2024-06-16_TANG_RTU_ChemData_Sec2.xlsx\n```\n\n## Plot\n\n---\n\nThis package provide some basic functionality to plot some data. For the simplicity, we will use `six_hours` data as an example. We will do all the basic things above, from extracting, selecting, and filtering.\n\n#### Selecting Six Hours\n```python\nsix_hours_data = multigas.select('six_hours').get()\n```\n\n#### Date Filtering\n```python\nfiltered_six_hours = six_hours_data.where_date_between(start_date='2024-05-17', end_date='2024-06-18')\n```\n\n#### Plot Initiating\nUsing `plot()` method to initiate\n```python\nplot_six_hours = filtered_six_hours.plot()\n```\n\n#### Plot Avg. CO2, H2S, and SO2\nThis package has some default plotting method. We will use `plot_co2_so2_h2s()` as an example:\n```python\nplot_six_hours.plot_co2_so2_h2s()\n```\n\nYou can see the result here:\n\n\nFrom the plot above we can see there ae some anomaly values (below 400 for Av. CO2 Lowpass). We can re-filter it once again to optimize the result. In this case we will select column `Avg_CO2_lowpass` which value greater than or equal to `250`\n```python\nfiltered_six_hours = filtered_six_hours.where('Avg_CO2_lowpass', '>=', 250)\n```\n\nThen plot it:\n```python\nfiltered_six_hours.plot().plot_co2_so2_h2s()\n```\n\nResult:\n\n\nWe can also plot as an individual by adding parameter `plot_as_individual=True`\n```python\nfiltered_six_hours.plot().plot_co2_so2_h2s(\n plot_as_individual=True\n)\n```\n\nAnd the result:\n\n\n### Plot Specific Column(s)\nSelect columns to plot:\n```python\ncolumns_to_plot = ['Avg_Wind_Speed', 'Avg_Wind_Direction', 'Avg_H2O', 'Avg_CO2_lowpass']\n```\n\nThen plot it:\n```python\nfiltered_six_hours.plot(height=2).plot_columns(columns_to_plot)\n```\n\nResults:\n\n",
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