Metadata-Version: 2.1
Name: mlo_co2
Version: 0.3
Summary: Scrape CO2 data from Mauna Loa Observatory off of NOAA Earth Science Research Lab
Home-page: https://github.com/kylepollina/mlo_co2
Author: Kyle Pollina
Author-email: kylepollina@pm.me
License: UNKNOWN
Description: # Mauna Loa Observatory Carbon Dioxide Data Scraper
        
        This Python package includes a script to scrape the NOAA Earth Science Research Lab for Carbon Dioxide (CO2) readings from the Mauna Loa Observatory in Hawai'i.
        You can access this data here: [https://www.esrl.noaa.gov/gmd/ccgg/trends/mlo.html](https://www.esrl.noaa.gov/gmd/ccgg/trends/mlo.html)
        
        ## Installation
        
        ```
        pip install git+https://github.com/kylepollina/mlo_co2.git
        ```
        
        ## Features
        
        ### monthly_mean()
        ```
        # Data from March 1958 through April 1974 have been obtained by C. David Keeling
        # of the Scripps Institution of Oceanography (SIO) and were obtained from the
        # Scripps website (scrippsco2.ucsd.edu).
        # Monthly mean CO2 constructed from daily mean values
        # Scripps data downloaded from http://scrippsco2.ucsd.edu/data/atmospheric_co2
        # Monthly values are corrected to center of month based on average seasonal
        # cycle. Missing days can be asymmetric which would produce a high or low bias.
        # Missing months have been interpolated, for NOAA data indicated by negative stdev
        # and uncertainty. We have no information for SIO data about Ndays, stdv, unc
        # so that they are also indicated by negative numbers
        ```
        
        Optional start date and end date parameters. Scraped from this url: [https://www.esrl.noaa.gov/gmd/webdata/ccgg/trends/co2/co2_mm_mlo.txt](https://www.esrl.noaa.gov/gmd/webdata/ccgg/trends/co2/co2_mm_mlo.txt)
        
        ```python
        >>> from mlo_co2 import monthly_mean
        >>> mean = monthly_mean(start=datetime(year=1985, day=1, month=1), end=datetime(year=2014, day=1, month=1))
        >>> mean.keys()
        dict_keys(['url', 'license', 'description', 'headers', 'raw', 'data'])
        >>> mean['data'].keys()
        dict_keys(['yr', 'mon', 'decimal', 'monthly average (ppm)', 'de-seasonalized (ppm)', '#days', 'st.dev of days', 'unc. of mon mean'])
        ```
        
        ### annual_mean()
        ```
        # Data from March 1958 through April 1974 have been obtained by C. David Keeling
        # of the Scripps Institution of Oceanography (SIO) and were obtained from the
        # Scripps website (scrippsco2.ucsd.edu).
        #
        # The estimated uncertainty in the annual mean is the standard deviation
        # of the differences of annual mean values determined independently by
        # NOAA/ESRL and the Scripps Institution of Oceanography.
        #
        # NOTE: In general, the data presented for the last year are subject to change,
        # depending on recalibration of the reference gas mixtures used, and other quality
        # control procedures. Occasionally, earlier years may also be changed for the same
        # reasons.  Usually these changes are minor.
        #
        # CO2 expressed as a mole fraction in dry air, micromol/mol, abbreviated as ppm
        ```
        
        Optional start date and end date parameters. Scraped from this url: [https://www.esrl.noaa.gov/gmd/webdata/ccgg/trends/co2/co2_annmean_mlo.txt](https://www.esrl.noaa.gov/gmd/webdata/ccgg/trends/co2/co2_annmean_mlo.txt)
        
        ```python
        >>> from mlo_co2 import annual_mean
        >>> mean = annual_mean(start=datetime(year=1985, day=1, month=1), end=datetime(year=2014, day=1, month=1))
        >>> mean.keys()
        dict_keys(['url', 'license', 'description', 'headers', 'raw', 'data'])
        >>> mean['data'].keys()
        dict_keys(['yr', 'mean (ppm)', 'unc'])
        ```
        
        ### annual_mean_increase()
        ```
        # Data from March 1958 through April 1974 have been obtained by C. David Keeling
        # of the Scripps Institution of Oceanography (SIO) and were obtained from the
        # Scripps website (scrippsco2.ucsd.edu).
        #
        # Annual CO2 mole fraction increase (ppm) from Jan 1 through Dec 31.
        #
        # The uncertainty in the Mauna Loa annual mean growth rate is estimated
        # from the standard deviation of the differences between monthly mean
        # values determined independently by the Scripps Institution of Oceanography
        # and by NOAA/ESRL.
        #
        # NOTE: In general, the data presented for the last year are subject to change,
        # depending on recalibration of the reference gas mixtures used, and other quality
        # control procedures. Occasionally, earlier years may also be changed for the same
        # reasons.  Usually these changes are minor.
        #
        # CO2 expressed as a mole fraction in dry air, micromol/mol, abbreviated as ppm
        ```
        
        Optional start date and end date parameters. Scraped from this url: [https://www.esrl.noaa.gov/gmd/webdata/ccgg/trends/co2/co2_gr_mlo.txt](https://www.esrl.noaa.gov/gmd/webdata/ccgg/trends/co2/co2_gr_mlo.txt)
        
        ```python
        >>> from mlo_co2 import annual_mean_increase
        >>> mean = annual_mean_increase()
        >>> mean.keys()
        dict_keys(['url', 'license', 'description', 'headers', 'raw', 'data'])
        >>> mean['data'].keys()
        dict_keys(['yr', 'ann inc', 'unc'])
        ```
        
        ### weekly_mean()
        ```
        # NOTE: DATA FOR THE LAST SEVERAL MONTHS ARE PRELIMINARY, ARE STILL SUBJECT
        # TO QUALITY CONTROL PROCEDURES.
        # NOTE: The week "1 yr ago" is exactly 365 days ago, and thus does not run from
        # Sunday through Saturday. 365 also ignores the possibility of a leap year.
        # The week "10 yr ago" is exactly 10*365 days +3 days (for leap years) ago.
        ```
        
        Optional start date and end date parameters. Scraped from this url: [https://www.esrl.noaa.gov/gmd/webdata/ccgg/trends/co2/co2_weekly_mlo.txt](https://www.esrl.noaa.gov/gmd/webdata/ccgg/trends/co2/co2_weekly_mlo.txt)
        
        ```python
        >>> from mlo_co2 import weekly_mean
        >>> mean = weekly_mean()
        >>> mean.keys()
        dict_keys(['url', 'license', 'description', 'headers', 'raw', 'data'])
        >>> mean['data'].keys()
        dict_keys(['yr', 'mon', 'day', 'decimal', 'ppm', '#days', '1 yr ago', '10 yr ago', 'since 1800'])
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.8
Description-Content-Type: text/markdown
