semvec/pypi-packages huggingface dataset:
gtempco2 0.0.9
A package for displaying global temperature and co2
Requires Python: >=3.8

# gtempco2

gtempco2 is a PyPI application to display global temperature and co2 of land and ocean with NOAA datasets.

NOAA monthly datasets on the global temperature of land and ocean and the global co2 will be automatically downloaded. 

gtempco2 is to plot a graph for a user-specified time period with two lines representing global temperature and global CO2 levels. The graph includes first-order regression lines with coefficients, p-value and R-squared values.

To install gtempco2, use the following pip command.

$ pip install gtempco2

To run gtempco2, the user-specified time period such as start-date and end-date is needed to plot a graph of the global temperature and co2 from start-date to end-date. 

The format of them is as follows.

For example, 1958-03 indicates March 1958 when NOAA started the measurement of the global co2.

<pre>
$ gtempco2
enter start_date: e.g. 1960-04
co2 measurement started from 1958-03
yyyy-mo: 1958-03
enter end_date: e.g. 2023-04
yyyy-mo: 1965-03
start_date= 1958-03 end_date= 1965-03
</pre>

The system will ask the start-date and end-date. The result will be shown and saved in the directory as follows.

<img src='https://github.com/y-takefuji/gtempco2/raw/main/1958-03_1965-03.png' height=450 width=600>
fixreprod 0.0.11
A package for showing all seeds to be fixed
Requires Python: >=3.8

# fixreprod
This is under review.

It is still undergoing improvement.

fixreprod is an indispensable tool to solve reproducibility problems in pseudorandom numbers by fixing all seeds and deterministic algorithms used in libraries of AI systems.
The current version can take care of torch, tensorflow, gym and gymnasium with numpy, random and scipy. The program is expandable.

To run fixreprod, install it by the following command. ($) indicates the prompt from the system in the terminal.

$ pip install fixreprod

# How to run fixreprod

For example, assume reinforcement_q_learning.py is a source code. Use grep command to generate test.py.
test.py contains imported libraries which will be checked by fixreprod. The sed command sed '/^"""/,/^"""/d' removes comment lines from reinforcement_q_learning.py and saves it in reinfo.py. The first grep command removes comment lines with the first character of "#" from reinfo.py and save it in rein.py. The second grep command extracts imported libraries from rein.py and save it in test.py.

$ grep import reinforcement_q_learning.py >test.py

or To see the code only, run the following commands

$ sed '/^"""/,/^"""/d' reinforcement_q_learning.py >reinfo.py

$ grep -v '^#' reinfo.py >rein.py

$ grep import rein.py >test.py

Before running fixreprod, you must install all libraries used in the source code.
In reinforcement_q_learning.py, gym, random, numpy, and torch must be installed.

<pre>
Run fixrepdod and enter "test.py" for checking.
fixreprod will show the all seeds to be fixed to eliminate 
reproducibility problems from your code. 
9 seeds and 1 fixing deterministic algorithms are found 
  in this example.
  
$ fixreprod
Enter the source code file name: test.py
env.action_space.seed(0)
env.reset(seed=0)
np.random.seed(0)
random.Random().seed(0)
random.seed(0)
torch.Generator().manual_seed(0)        
torch.cuda.manual_seed(0)
torch.cuda.manual_seed_all(0)
torch.manual_seed(0)
torch.use_deterministic_algorithms(True)

</pre>
These generated codes should be embedded in the rein.py or reinforcement_q_learning.py. The following figure shows the reproducible result. When changing seed numbers, the different reproducible result can be obtained.

<img src='https://github.com/y-takefuji/fixreprod/raw/main/result.png' width=540 hight=480>
tsico2 0.0.4
A package for visualizing TSI and CO2 with r2 and p-value
Requires Python: >=3.8

# tsico2
This is under review.

tsico2 is a tool available on PyPI that allows users to visualize monthly values of Total Solar Irradiance (TSI) and global CO2 levels. Users can specify a start and end date for the period they wish to visualize, and the tool will display a graph with four lines representing the raw and linear regression values for both TSI and CO2. 

Additionally, the calculated R-squared and p-value for the linear regression lines are also displayed on the graph. This provides users with an easy and convenient way to analyze trends in TSI and CO2 levels over time.

# How to run tsico2

Before running tsico2, you must install it by pip command.

$ pip install tsico2

$ tsico2

Enter start-date for March 2007 in this example

Enter the beginning year-month (e.g. 1978-11): 2007-3

Enter end-date for March 2014

Enter the end year-month (e.g. 2023-3): 2014-3

The result will be popped on the screen. 
The solid lines are the original and regression lines for CO2 and the dotted lines are the original and regression lines for TSI.

<img src="https://github.com/y-takefuji/tsico2/raw/main/2007-3-2014-3.png" width=640 height=480>