# -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>
# <codecell>
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn
from matplotlib import rcParams
rcParams["figure.figsize"] = (14, 8)
rcParams["xtick.labelsize"] = 12
rcParams["ytick.labelsize"] = 12
rcParams["font.size"] = 14
rcParams["axes.titlesize"] = 16
rcParams["text.usetex"] = False
rcParams["font.family"] = "Serif"
rcParams["figure.dpi"] = 600
a = pd.read_csv("../data/villagebay_population.csv")
b = pd.read_csv("../data/exposure.csv")
fig, (ax, ax2) = plt.subplots(2, 1, sharex=True)
#ax = plt.subplot(211)
ax.plot(a.Year, a.VillageBay, c=seaborn.color_palette("deep", 8)[0], lw=3)
ax.scatter(a.Year, a.VillageBay, c=seaborn.color_palette("deep", 8)[0], s=50)
ax.set_title("Village Bay Population")
ax.set_ylim([180, 700])
#ax2 = plt.subplot(212, sharex=ax)
ax2.plot(b.BirthYear, b.AvgOfLambWS, c=seaborn.color_palette("deep", 8)[2], lw=3)
ax2.scatter(b.BirthYear, b.AvgOfLambWS, c=seaborn.color_palette("deep", 8)[2], s=50)
ax2.set_title("Lamb Winter Survival")
ax2.set_xlim([1984.5, 2013.5])
ax2.set_ylim([0, 0.8])
plt.savefig("figures/population3.png", dpi=300)
# <headingcell level=1>
# Robust Extraction of Quantitative Information from Histology Images
# <markdowncell>
# **Quentin Caudron**
# <markdowncell>
# <img src="figures/graphics/soay.jpg" />
# <markdowncell>
# <img src="figures/graphics/population2.jpg" width=1200px/>
# <markdowncell>
# <img src="figures/graphics/lit1.jpg" />
# <markdowncell>
# <img src="figures/graphics/lit2.jpg" />
# <markdowncell>
# <img src="figures/graphics/lit4.jpg" />
# <headingcell level=2>
# Outline
# <markdowncell>
# - Methods and data collection
# - Image processing
# - Extracted measures
# - Preliminary analysis
# - Future directions
# <headingcell level=2>
# Data
# <markdowncell>
# **In the field, winter of 2011 - 2012 :**
#
# - Daily study area monitoring for deaths
# - 143 liver samples collected within a day of death
# <markdowncell>
# **In the lab :**
#
# - Sectioning after paraffin treatment
# - H&E staining of about 1000 slides
# <markdowncell>
# **Analysis :**
#
# - Pathology standard : semi-quantitative scoring
# - Image processing
# <headingcell level=3>
# The Field
# <markdowncell>
# Sweat-and-blood-collected in cold, cold Scotland.
# <markdowncell>
# Eight physical measurements :
# - Age at death
# - Weight
# - Sex
# - Limb length
# - Environmental "stress"
# <headingcell level=3>
# Clinical Pathology
# <markdowncell>
# Operator-driven visual analysis of 98 slides under microscopy.
# <markdowncell>
# Eleven discrete and continuous measures :
#
# - Inflammation
# - Necrosis
# - Apoptosis
# - Hyperplasia
# - Fibrosis
# - Hepatitis
# <headingcell level=3>
# Image Processing
# <markdowncell>
# Automated analysis of 4430 images of slides representing 143 sheep.
# <markdowncell>
# Seven structural and textural measures with varying levels of biological interpretation :
#
# - Inflammation
# - Hyperplasia / tissue density
# - Best-guess proxies for "generic degeneration"
# <headingcell level=2>
# Image Processing
# <markdowncell>
# <img src="figures/graphics/sheep.jpg"></img>
# <markdowncell>
# <img src="figures/graphics/processed.jpg"></img>
# <headingcell level=3>
# The Challenge
# <markdowncell>
# **Information extraction must be**
# - automagical - no operator input
# - reasonably quick - restricted computing time
# - robust - invariant to slicing, staining, field-related variation
# - unbiased - same algorithms for everyone
# <markdowncell>
# 
# <markdowncell>
# 
# <markdowncell>
# 
# <markdowncell>
# 
# <markdowncell>
# <img src="figures/graphics/gif.gif"></img>
# <headingcell level=2>
# Structural and Textural Measures
# <markdowncell>
# - characteristic **scale** of sinusoid widths
# - **directional** amplitude of preferred sinusoid alignment
# - **tissue to sinusoid** ratio
# - **count** of inflammatory foci per image
# - **mean size** of inflammatory foci per image
# - information **entropy** of sinusoid distribution
# - **lacunarity** ( clustering ) of sinusoids
# <markdowncell>
# 
# <markdowncell>
# 
# <headingcell level=2>
# Exploratory Analysis
# <markdowncell>
# by individual
# <markdowncell>
# <img src="figures/regressions/BDHyperplasia/lm-0.png" />
# <markdowncell>
# <img src="figures/regressions/PortalInflammation/lm-0.png" />
# <markdowncell>
# <img src="figures/regressions/PortalInflammation/lm-1.png" />
# <headingcell level=2>
# Exploratory Analysis
# <markdowncell>
# controlled for age / cohort
# <headingcell level=2>
# <img src="figures/regressions/PortalInflammation/mm_0.png" />
# <markdowncell>
# <img src="figures/regressions/BDHyperplasia/mm_0.png" />
# <markdowncell>
# <img src="figures/regressions/BDHyperplasia/mm_1.png" />
# <markdowncell>
# <img src="figures/regressions/TawfikTotal/mm_0.png" />
# <markdowncell>
# <img src="figures/regressions/Fibrosis/mm_0.png" />
# <markdowncell>
# <img src="figures/regressions/PortalInflammation/mm_0.png" />
# <markdowncell>
# <img src="figures/regressions/Hindleg/mm_0.png" />
# <markdowncell>
# <img src="figures/regressions/Weight/mm_0.png" />
# <headingcell level=2>
# Further analysis
# <markdowncell>
# Age or cohort effect ?
# <markdowncell>
# <img src="figures/regressions/BDHyperplasia/mm_coefs_color_E.png" />
# <markdowncell>
# <img src="figures/regressions/BDHyperplasia/mm_coefs_color_CES.png" />
# <markdowncell>
# <img src="figures/regressions/BDHyperplasia/mm_coefs_color_RES.png" />
# <headingcell level=2>
# Conclusions
# <markdowncell>
# - our image measures capture **relevant** and **useful** information
# - a number of correlations can be **explained** biologically
# - underlying **structure** in the data needs thought
# - still no **map** from image or histological measures to condition of individual
# <headingcell level=2>
# Future directions
# <headingcell level=3>
# Further exploration of the dataset
# <markdowncell>
# - 145 sheep ( 89 females )
# - 12 age classes
# - potential redundancy in various measures
# <markdowncell>
# - 4460 entries across 27 variables
# - 3330 with full image and histological information
# - 1196 for which **complete** information is available
# <headingcell level=3>
# More data
# <markdowncell>
# - nutritional information
# - immunity data
# <headingcell level=3>
# Narrow-field images
# <markdowncell>
# - 12536 images
# - spatial distribution of nuclei
# <markdowncell>
# 
# <markdowncell>
# 
# <markdowncell>
# 
# <markdowncell>
# <img src="figures/graphics/10x.png" width=100%></src>
# <headingcell level=2>
# With thanks to
# <markdowncell>
# Romain Garnier
#
# Andrea Graham
#
# Tawfik Aboellail (CSU)
#
# Bryan Grenfell
Datasets
Models
