pyco2stats

Contents:

  • Introduction and Citation
    • Introduction
    • Citation
  • Installation
    • Step 1: Installing Anaconda
    • Step 2: Installing PyCO2stats
    • Step 2.1: Installing from PyPi
    • Step 2.2: Installing from source
    • Troubleshooting
  • Notebooks
    • Gaussian Mixture Models (GMM)
      • Generation of a set of synthetic populations
      • Visualizing the populations
      • GMMs fitting
      • Visualizing the fitting
      • Sinclair-like visualization
      • Q-Q plot visualization
    • Estimating Mean and Confidence Interval
    • Propagate_Errors
      • Montecarlo error propagation
        • Plot of the Montecarlo error propagation
  • PyCO2stats Functions
    • Gaussian mixtures
      • GMM
        • GMM.gaussian_mixture_em()
        • GMM.gaussian_mixture_sklearn()
        • GMM.constrained_gaussian_mixture()
        • GMM.gaussian_mixture_pdf()
        • GMM.sample_from_gmm()
    • Sinclair
      • Sinclair
        • Sinclair.cumulative_to_sigma()
        • Sinclair.sigma_to_cumulative()
        • Sinclair.get_raw_data()
        • Sinclair.calculate_combined_population()
    • Statistics
      • Stats
        • Stats.lognormal_median_ci()
        • Stats.bootstrap_mean_ci()
        • Stats.median()
        • Stats.mad()
        • Stats.mad_std()
        • Stats.sigma_clip()
        • Stats.sigma_clipped_stats()
        • Stats.biweight_location()
        • Stats.biweight_scale()
        • Stats.trim()
        • Stats.trimmed_mean()
        • Stats.trimmed_std()
        • Stats.trimboth()
        • Stats.trimtail()
        • Stats.winsorize()
        • Stats.winsorized_mean()
        • Stats.winsorized_std()
        • Stats.Huber()
        • Stats.lognormal_estimator()
        • Stats.umvue_finney_lognormal_estimator()
        • Stats.umvue_sichel_lognormal_estimator()
        • Stats.finneys_g()
        • Stats.psi_table
        • Stats.lookup_psi()
        • Stats.ci_standard_approx()
        • Stats.ci_lnorm_zou()
        • Stats.ci_cox()
        • Stats.lands_cond_t_prop_density_polar()
        • Stats.lands_cond_t_prop_density()
        • Stats.qlands_t()
        • Stats.lands_C_old()
        • Stats.lands_C()
        • Stats.ci_land()
        • Stats.ci_lnorm_land()
    • Error propagation
      • Propagate_Errors
        • Propagate_Errors.propagate_em_error()
        • Propagate_Errors.propagate_sklearn_error()
        • Propagate_Errors.propagate_constrained_error()
        • Propagate_Errors.elaborate_results()
    • Matplotlib visualization
      • Visualize_Mpl
        • Visualize_Mpl.pp_raw_data()
        • Visualize_Mpl.pp_combined_population()
        • Visualize_Mpl.pp_single_populations()
        • Visualize_Mpl.pp_one_population()
        • Visualize_Mpl.pp_add_sigma_grid()
        • Visualize_Mpl.pp_add_percentiles()
        • Visualize_Mpl.qq_plot()
        • Visualize_Mpl.plot_gmm_pdf()
    • Plotly visualization
      • Visualize_Plotly
        • Visualize_Plotly.pp_raw_data()
        • Visualize_Plotly.pp_one_population()
        • Visualize_Plotly.pp_single_populations()
        • Visualize_Plotly.pp_combined_population()
        • Visualize_Plotly.pp_add_percentiles()
        • Visualize_Plotly.plot_gmm_pdf()
        • Visualize_Plotly.qq_plot()
pyco2stats
  • Welcome to pyco2stats’s documentation!
  • Edit on GitHub

Welcome to pyco2stats’s documentation!

In this documentation you will found:

Contents:

  • Introduction and Citation
    • Introduction
    • Citation
  • Installation
    • Step 1: Installing Anaconda
    • Step 2: Installing PyCO2stats
    • Step 2.1: Installing from PyPi
    • Step 2.2: Installing from source
    • Troubleshooting
  • Notebooks
    • Gaussian Mixture Models (GMM)
    • Estimating Mean and Confidence Interval
    • Propagate_Errors
  • PyCO2stats Functions
    • Gaussian mixtures
    • Sinclair
    • Statistics
    • Error propagation
    • Matplotlib visualization
    • Plotly visualization
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