import matplotlib.patheffects as path_effects import seaborn as sns import bebi103.viz import srep.viz from srep.utils import load_FISH_by_promoter # Set PBoC plotting style srep.viz.plotting_style() # %% repo = Repo("./", search_parent_directories=True) # repo_rootdir holds the absolute path to the top-level of our repo repo_rootdir = repo.working_tree_dir # Import sm-FISH data along with promoter information df_unreg, df_energies, = load_FISH_by_promoter(("unreg", "energies")) df_energies.sort_values('Energy (kT)', inplace=True) # %% # Group FISH data by promoters df_group = df_unreg.groupby("experiment") # Initialize dataframe to save mean expression and Fano factor names = ["promoter", "mean", "var", "fano", "energy_kT"] df_summary = pd.DataFrame([], columns=names) # Loop through promoters computing mean expression and Fano for prom, data in df_group: # Compute mean expression mean_m = data["mRNA_cell"].mean() # Compute variance
import numpy as np import pandas as pd import cmdstanpy import arviz as az from bebi103.stan import disable_logging as be_quiet_stan from bebi103.stan import check_all_diagnostics from srep.utils import load_FISH_by_promoter repo = Repo("./", search_parent_directories=True) # repo_rootdir holds the absolute path to the top-level of our repo repo_rootdir = repo.working_tree_dir # first load data using module util df_unreg, = load_FISH_by_promoter(("unreg", )) # pull out one specific promoter for convenience for prior pred check & SBC df_UV5 = df_unreg[df_unreg["experiment"] == "UV5"] sm = cmdstanpy.CmdStanModel( stan_file=f"{repo_rootdir}/code/stan/constit_post_inf.stan", compile=True, ) all_samples = {} for gene in df_unreg['experiment'].unique(): temp_df = df_unreg[df_unreg['experiment'] == gene] stan_data = dict( N=len(temp_df), mRNA_counts=temp_df["mRNA_cell"].values.astype(int), ppc=0 # if you produce ppc samples, the InferenceData obj is HUGE