timothydmorton

from pathlib import Path
from difflib import SequenceMatcher
import re

# Some example dataset templates from policy/datasets.yaml in obs_base
templates = {'deepCoadd_obj': 'deepCoadd-results/merged/%(tract)d/%(patch)s/obj-%(tract)d-%(patch)s.parq',
            'deepCoadd_qa': 'deepCoadd-results/qa/%(tract)d/%(patch)s/qa-%(tract)d-%(patch)s.parq',
            'analysisVisitTable': 'plots/%(filter)s/tract-%(tract)d/visit-%(visit)d/%(tract)d_%(visit)d.parq',
            'analysisVisitTable_commonZp': 'plots/%(filter)s/tract-%(tract)d/visit-%(visit)d/%(tract)d_%(visit)d_commonZp.parq',
            'analysisCoaddTable_forced': 'plots/%(filter)s/tract-%(tract)d/%(tract)d_forced.parq',

timothydmorton

# Need to run this with 'bolo' branch from github.com/timothydmorton/isochrones

from isochrones.mist.isochrone import MIST_BasicIsochrone
from isochrones import StarModel

iso = MIST_BasicIsochrone()
iso.initialize()  # This will take a few minutes the first time you run this. 

mags = {'J': (10.14, 0.02),
        'H': (9.81, 0.02),

timothydmorton

import numpy as np

import holoviews as hv

from bokeh.io import curdoc
from bokeh.layouts import layout
from bokeh.models.widgets import Panel, Tabs

import holoviews as hv
renderer = hv.renderer('bokeh').instance(mode='server')

timothydmorton

import holoviews as hv
hv.notebook_extension('bokeh')

import param
from holoviews.operation.datashader import datashade, dynspread
import colorcet as cc
import datashader

import numpy as np
import pandas as pd

timothydmorton

import numpy as np
import holoviews as hv
import pandas as pd

from bokeh.application.handlers import FunctionHandler
from bokeh.application import Application
from bokeh.io import show
from bokeh.layouts import layout
from bokeh.plotting import curdoc

timothydmorton

from scipy.optimize import root
import numpy as np
from scipy.special import spherical_jn
import matplotlib.pyplot as plt

def spherical_jn_sensible_grid(n, m, ngrid=100):
    """Returns a grid of x values that should contain the first m zeros, but not too many.
    """
    return np.linspace(n, n + 2*m*(np.pi * (np.log(n)+1)), ngrid)
    

timothydmorton

def model(p, x):
    """
    p are parameters of the model, in this case C, alpha
    
    where y = C*x^alpha
    """
    return p[0]*x**p[1]
    
def lnlike(p, xdata, ydata, yerr=1.):