content stringlengths 1 1.05M | input_ids listlengths 1 883k | ratio_char_token float64 1 22.9 | token_count int64 1 883k |
|---|---|---|---|
from django.contrib import admin
from .models import Coder, Level
admin.site.register(Coder, CoderAdmin)
admin.site.register(Level, LevelAdmin)
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# The `Environment` class represents the dynamic environment of McCarthy's original Lisp. The creation of
# this class is actually an interesting story. As many of you probably know, [Paul Graham wrote a paper and
# code for McCarthy's original Lisp](http://www.paulgraham.com/rootsoflisp.html) and it was my first exposure to
# the stark simplicity of the language. The simplicity is breath-taking!
#
# However, while playing around with the code I found that in using the core functions (i.e. `null.`, `not.`, etc.)
# I was not experiencing the full effect of the original. That is, the original Lisp was dynamically scoped, but
# the Common Lisp used to implement and run (CLisp in the latter case) Graham's code was lexically scoped. Therefore,
# by attempting to write high-level functions using only the magnificent 7 and Graham's core functions in the Common Lisp
# I was taking advantage of lexical scope; something not available to McCarthy and company. Of course, the whole reason
# that Graham wrote `eval.` was to enforce dynamic scoping (he used a list of symbol-value pairs where the dynamic variables
# were added to its front when introduced). However, that was extremely cumbersome to use:
#
# (eval. 'a '((a 1) (a 2)))
# ;=> 1
#
# So I then implemented a simple REPL in Common Lisp that fed input into `eval.` and maintained the current environment list.
# That was fun, but I wasn't sure that I was learning anything at all. Therefore, years later I came across the simple
# REPL and decided to try to implement my own core environment for the magnificent 7 to truly get a feel for what it took
# to build a simple language up from scratch. I suppose if I were a real manly guy then I would have found an IBM 704, but
# that would be totally insane. (email me if you have one that you'd like to sell for cheap)
#
# Anyway, the point of this is that I needed to start with creating an `Environment` that provided dynamic scoping, and the
# result is this.
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... | 3.974155 | 503 |
import subprocess
import aiostream
import pytest
from vdirsyncer.storage.filesystem import FilesystemStorage
from vdirsyncer.vobject import Item
from . import StorageTests
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909... | 3.52 | 50 |
import pandas as pd
from datetime import timedelta, date
import matplotlib.pyplot as plt
#default region is Sicily
nuovi_positivi = getAll('nuovi_positivi', 'Sicilia')
#deceduti = getAll('deceduti', 'Sicilia')
#dimessi_guariti = getAll('dimessi_guariti', 'Sicilia')
nuovi_positivi = pd.Series(nuovi_positivi, index=pd.date_range('2/24/2020', periods=len(nuovi_positivi)))
#deceduti = pd.Series(deceduti, index=pd.date_range('2/24/2020', periods=len(deceduti)))
#dimessi_guariti = pd.Series(dimessi_guariti, index=pd.date_range('2/24/2020', periods=len(dimessi_guariti)))
plt.figure();
ax = nuovi_positivi.plot()
#deceduti.plot(ax=ax)
#dimessi_guariti.plot(ax=ax)
plt.show()
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"""
Model select class1 single allele models.
"""
import argparse
import os
import signal
import sys
import time
import traceback
import random
from functools import partial
from pprint import pprint
import numpy
import pandas
from scipy.stats import kendalltau, percentileofscore, pearsonr
from sklearn.metrics import roc_auc_score
import tqdm # progress bar
tqdm.monitor_interval = 0 # see https://github.com/tqdm/tqdm/issues/481
from .class1_affinity_predictor import Class1AffinityPredictor
from .common import normalize_allele_name
from .encodable_sequences import EncodableSequences
from .common import configure_logging, random_peptides
from .local_parallelism import worker_pool_with_gpu_assignments_from_args, add_local_parallelism_args
from .regression_target import from_ic50
# To avoid pickling large matrices to send to child processes when running in
# parallel, we use this global variable as a place to store data. Data that is
# stored here before creating the thread pool will be inherited to the child
# processes upon fork() call, allowing us to share large data with the workers
# via shared memory.
GLOBAL_DATA = {}
parser = argparse.ArgumentParser(usage=__doc__)
parser.add_argument(
"--data",
metavar="FILE.csv",
required=False,
help=(
"Model selection data CSV. Expected columns: "
"allele, peptide, measurement_value"))
parser.add_argument(
"--exclude-data",
metavar="FILE.csv",
required=False,
help=(
"Data to EXCLUDE from model selection. Useful to specify the original "
"training data used"))
parser.add_argument(
"--models-dir",
metavar="DIR",
required=True,
help="Directory to read models")
parser.add_argument(
"--out-models-dir",
metavar="DIR",
required=True,
help="Directory to write selected models")
parser.add_argument(
"--out-unselected-predictions",
metavar="FILE.csv",
help="Write predictions for validation data using unselected predictor to "
"FILE.csv")
parser.add_argument(
"--unselected-accuracy-scorer",
metavar="SCORER",
default="combined:mass-spec,mse")
parser.add_argument(
"--unselected-accuracy-scorer-num-samples",
type=int,
default=1000)
parser.add_argument(
"--unselected-accuracy-percentile-threshold",
type=float,
metavar="X",
default=95)
parser.add_argument(
"--allele",
default=None,
nargs="+",
help="Alleles to select models for. If not specified, all alleles with "
"enough measurements will be used.")
parser.add_argument(
"--combined-min-models",
type=int,
default=8,
metavar="N",
help="Min number of models to select per allele when using combined selector")
parser.add_argument(
"--combined-max-models",
type=int,
default=1000,
metavar="N",
help="Max number of models to select per allele when using combined selector")
parser.add_argument(
"--combined-min-contribution-percent",
type=float,
default=1.0,
metavar="X",
help="Use only model selectors that can contribute at least X %% to the "
"total score. Default: %(default)s")
parser.add_argument(
"--mass-spec-min-measurements",
type=int,
metavar="N",
default=1,
help="Min number of measurements required for an allele to use mass-spec model "
"selection")
parser.add_argument(
"--mass-spec-min-models",
type=int,
default=8,
metavar="N",
help="Min number of models to select per allele when using mass-spec selector")
parser.add_argument(
"--mass-spec-max-models",
type=int,
default=1000,
metavar="N",
help="Max number of models to select per allele when using mass-spec selector")
parser.add_argument(
"--mse-min-measurements",
type=int,
metavar="N",
default=1,
help="Min number of measurements required for an allele to use MSE model "
"selection")
parser.add_argument(
"--mse-min-models",
type=int,
default=8,
metavar="N",
help="Min number of models to select per allele when using MSE selector")
parser.add_argument(
"--mse-max-models",
type=int,
default=1000,
metavar="N",
help="Max number of models to select per allele when using MSE selector")
parser.add_argument(
"--scoring",
nargs="+",
default=["mse", "consensus"],
help="Scoring procedures to use in order")
parser.add_argument(
"--consensus-min-models",
type=int,
default=8,
metavar="N",
help="Min number of models to select per allele when using consensus selector")
parser.add_argument(
"--consensus-max-models",
type=int,
default=1000,
metavar="N",
help="Max number of models to select per allele when using consensus selector")
parser.add_argument(
"--consensus-num-peptides-per-length",
type=int,
default=10000,
help="Num peptides per length to use for consensus scoring")
parser.add_argument(
"--mass-spec-regex",
metavar="REGEX",
default="mass[- ]spec",
help="Regular expression for mass-spec data. Runs on measurement_source col."
"Default: %(default)s.")
parser.add_argument(
"--verbosity",
type=int,
help="Keras verbosity. Default: %(default)s",
default=0)
add_local_parallelism_args(parser)
if __name__ == '__main__':
run()
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10141... | 2.773083 | 1,917 |
import time
from adafruit_circuitplayground.express import cpx
import simpleio
cpx.pixels.auto_write = False
cpx.pixels.brightness = 0.3
# Set these based on your ambient temperature for best results!
minimum_temp = 24
maximum_temp = 30
while True:
# temperature value remapped to pixel position
peak = simpleio.map_range(cpx.temperature, minimum_temp, maximum_temp, 0, 10)
print(cpx.temperature)
print(int(peak))
for i in range(0, 10, 1):
if i <= peak:
cpx.pixels[i] = (0, 255, 255)
else:
cpx.pixels[i] = (0, 0, 0)
cpx.pixels.show()
time.sleep(0.05)
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11... | 2.377863 | 262 |
num = 1
items = []
while True:
line_input = input()
if line_input == 'go go go':
break
topic, course_name, judge_contest_link, all_problems = list(line_input.split(' -> '))
problems = all_problems.split(', ')
items.append(Exercises(topic, course_name, judge_contest_link, problems))
for i in items:
print(i.get_info())
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... | 2.503497 | 143 |
############################################################
# Copyright 2019 Michael Betancourt
# Licensed under the new BSD (3-clause) license:
#
# https://opensource.org/licenses/BSD-3-Clause
############################################################
############################################################
#
# Initial setup
#
############################################################
import matplotlib.pyplot as plot
import scipy.stats as stats
import numpy
import math
light = "#DCBCBC"
light_highlight = "#C79999"
mid = "#B97C7C"
mid_highlight = "#A25050"
dark = "#8F2727"
dark_highlight = "#7C0000"
green = "#00FF00"
# To facilitate the computation of Markov chain Monte Carlo estimators
# let's define a _Welford accumulator_ that computes empirical summaries
# of a sample in a single pass
# We can then use the Welford accumulator output to compute the
# Markov chain Monte Carlo estimators and their properties
# To generate our samples we'll use numpy's pseudo random number
# generator which needs to be seeded to achieve reproducible
# results
numpy.random.seed(seed=8675309)
# To ensure accurate results let's generate pretty large samples
N = 10000
# To see how results scale with dimension we'll consider
# behavior one thorugh ten dimensions
Ds = [ n + 1 for n in range(10) ]
idxs = [ idx for idx in range(Ds[-1]) for r in range(2) ]
plot_Ds = [ D + delta for D in Ds for delta in [-0.5, 0.5]]
############################################################
#
# How does the Random Walk Metropolis algorithm perform
# on a target distribution with a two-dimensional Gaussian
# density function?
#
############################################################
# Target density
# Tune proposal density
sigma = 1.4
# A place to store our Markov chain
# D columns for the parameters and one extra column
# for the Metropolis acceptance probability
D = 2
mcmc_samples = [[0] * (D + 1) for _ in range(N)]
# Randomly seed the initial state
mcmc_samples[0][0] = stats.norm.rvs(0, 3)
mcmc_samples[0][1] = stats.norm.rvs(0, 3)
mcmc_samples[0][2] = 1
for n in range(1, N):
x0 = [ mcmc_samples[n - 1][0], mcmc_samples[n - 1][1]]
xp = [ stats.norm.rvs(x0[0], sigma), stats.norm.rvs(x0[1], sigma) ]
# Compute acceptance probability
accept_prob = 1
if target_lpdf(xp) < target_lpdf(x0):
accept_prob = math.exp(target_lpdf(xp) - target_lpdf(x0))
mcmc_samples[n][D] = accept_prob
# Apply Metropolis correction
u = stats.uniform.rvs(0, 1)
if accept_prob > u:
mcmc_samples[n][0] = xp[0]
mcmc_samples[n][1] = xp[1]
else:
mcmc_samples[n][0] = x0[0]
mcmc_samples[n][1] = x0[1]
# Compute MCMC estimator statistics, leaving
# out the first 100 samples as warmup
compute_mcmc_stats([ s[0] for s in mcmc_samples[100:] ])
compute_mcmc_stats([ s[1] for s in mcmc_samples[100:] ])
# Plot convergence of MCMC estimators for each parameter
stride = 250
M = N / stride
iters = [ stride * (i + 1) for i in range(N / stride) ]
x1_mean = [0] * M
x1_se = [0] * M
x2_mean = [0] * M
x2_se = [0] * M
for m in range(M):
running_samples = [ s[0] for s in mcmc_samples[100:iters[m]] ]
mcmc_stats = compute_mcmc_stats(running_samples)
x1_mean[m] = mcmc_stats[0]
x1_se[m] = mcmc_stats[1]
running_samples = [ s[1] for s in mcmc_samples[100:iters[m]] ]
mcmc_stats = compute_mcmc_stats(running_samples)
x2_mean[m] = mcmc_stats[0]
x2_se[m] = mcmc_stats[1]
plot.fill_between(iters,
[ x1_mean[m] - 2 * x1_se[m] for m in range(M) ],
[ x1_mean[m] + 2 * x1_se[m] for m in range(M) ],
facecolor=light, color=light)
plot.plot(iters, x1_mean, color=dark)
plot.plot([iters[0], iters[-1]], [1, 1], color='grey', linestyle='--')
plot.gca().set_xlim([0, N])
plot.gca().set_xlabel("Iteration")
plot.gca().set_ylim([-2, 2])
plot.gca().set_ylabel("Monte Carlo Estimator")
plot.show()
plot.fill_between(iters,
[ x2_mean[m] - 2 * x2_se[m] for m in range(M) ],
[ x2_mean[m] + 2 * x2_se[m] for m in range(M) ],
facecolor=light, color=light)
plot.plot(iters, x2_mean, color=dark)
plot.plot([iters[0], iters[-1]], [-1, -1], color='grey', linestyle='--')
plot.gca().set_xlim([0, N])
plot.gca().set_xlabel("Iteration")
plot.gca().set_ylim([-2, 2])
plot.gca().set_ylabel("Monte Carlo Estimator")
plot.show()
############################################################
#
# How does the Random Walk Metropolis algorithm perform
# on a target distribution with a funnel density function?
#
############################################################
# Target density
# Tune proposal density
sigma = 1.4
# A place to store our Markov chain
# D columns for the parameters and one extra column
# for the Metropolis acceptance probability
D = 3
mcmc_samples = [[0] * (D + 1) for _ in range(N)]
# Randomly seed the initial state
mcmc_samples[0][0] = stats.norm.rvs(0, 3)
mcmc_samples[0][1] = stats.norm.rvs(0, 3)
mcmc_samples[0][2] = stats.norm.rvs(0, 3)
mcmc_samples[0][3] = 1
for n in range(1, N):
x0 = [ mcmc_samples[n - 1][0],
mcmc_samples[n - 1][1],
mcmc_samples[n - 1][2]]
xp = [ stats.norm.rvs(x0[0], sigma),
stats.norm.rvs(x0[1], sigma),
stats.norm.rvs(x0[2], sigma) ]
# Compute acceptance probability
accept_prob = 1
if target_lpdf(xp) < target_lpdf(x0):
accept_prob = math.exp(target_lpdf(xp) - target_lpdf(x0))
mcmc_samples[n][D] = accept_prob
# Apply Metropolis correction
u = stats.uniform.rvs(0, 1)
if accept_prob > u:
mcmc_samples[n][0] = xp[0]
mcmc_samples[n][1] = xp[1]
mcmc_samples[n][2] = xp[2]
else:
mcmc_samples[n][0] = x0[0]
mcmc_samples[n][1] = x0[1]
mcmc_samples[n][2] = x0[2]
# Compute MCMC estimator statistics, leaving
# out the first 100 samples as warmup
compute_mcmc_stats([ s[0] for s in mcmc_samples[100:] ])
compute_mcmc_stats([ s[1] for s in mcmc_samples[100:] ])
compute_mcmc_stats([ s[2] for s in mcmc_samples[100:] ])
# Plot convergence of MCMC estimators for each parameter
stride = 250
M = N / stride
iters = [ stride * (i + 1) for i in range(N / stride) ]
mu_mean = [0] * M
mu_se = [0] * M
log_tau_mean = [0] * M
log_tau_se = [0] * M
for m in range(M):
running_samples = [ s[0] for s in mcmc_samples[100:iters[m]] ]
mcmc_stats = compute_mcmc_stats(running_samples)
mu_mean[m] = mcmc_stats[0]
mu_se[m] = mcmc_stats[1]
running_samples = [ s[1] for s in mcmc_samples[100:iters[m]] ]
mcmc_stats = compute_mcmc_stats(running_samples)
log_tau_mean[m] = mcmc_stats[0]
log_tau_se[m] = mcmc_stats[1]
plot.fill_between(iters,
[ mu_mean[m] - 2 * mu_se[m] for m in range(M) ],
[ mu_mean[m] + 2 * mu_se[m] for m in range(M) ],
facecolor=light, color=light)
plot.plot(iters, mu_mean, color=dark)
plot.plot([iters[0], iters[-1]], [0, 0], color='grey', linestyle='--')
plot.gca().set_xlim([0, N])
plot.gca().set_xlabel("Iteration")
plot.gca().set_ylim([-1, 1])
plot.gca().set_ylabel("Monte Carlo Estimator")
plot.show()
plot.fill_between(iters,
[ log_tau_mean[m] - 2 * log_tau_se[m] for m in range(M) ],
[ log_tau_mean[m] + 2 * log_tau_se[m] for m in range(M) ],
facecolor=light, color=light)
plot.plot(iters, log_tau_mean, color=dark)
plot.plot([iters[0], iters[-1]], [0, 0], color='grey', linestyle='--')
plot.gca().set_xlim([0, N])
plot.gca().set_xlabel("Iteration")
plot.gca().set_ylim([-1, 8])
plot.gca().set_ylabel("Monte Carlo Estimator")
plot.show()
############################################################
#
# How does the effective sample size of a Random Walk
# Metropolis Markov chain vary with the dimension of
# the target distribution?
#
############################################################
############################################################
# First let's use a constant Markov transition
############################################################
accept_prob_means = [0] * len(Ds)
accept_prob_ses = [0] * len(Ds)
ave_eff_sample_sizes = [0] * len(Ds)
# Tune proposal density
sigma = 1.4
for D in Ds:
# A place to store our Markov chain
# D columns for the parameters and one extra column
# for the Metropolis acceptance probability
mcmc_samples = [[0] * (D + 1) for _ in range(N)]
# Seeding the initial state with an exact sample
# from the target distribution ensures that we
# start in the typical set and avoid having to
# worry about warmup.
for d in range(D):
mcmc_samples[0][d] = stats.norm.rvs(0, 3)
mcmc_samples[0][D] = 1
for n in range(1, N):
x0 = [ mcmc_samples[n - 1][d] for d in range(D) ]
xp = [ stats.norm.rvs(x0[d], sigma) for d in range(D) ]
# Compute acceptance probability
accept_prob = 1
if target_lpdf(xp) < target_lpdf(x0):
accept_prob = math.exp(target_lpdf(xp) - target_lpdf(x0))
mcmc_samples[n][D] = accept_prob
# Apply Metropolis correction
u = stats.uniform.rvs(0, 1)
if accept_prob > u:
mcmc_samples[n][0:D] = xp
else:
mcmc_samples[n][0:D] = x0
# Estimate average acceptance probability
# Compute MCMC estimator statistics
mcmc_stats = compute_mcmc_stats([ s[D] for s in mcmc_samples])
accept_prob_means[D - 1] = mcmc_stats[0]
accept_prob_ses[D - 1] = mcmc_stats[1]
# Estimate effective sample size
eff_sample_sizes = [ compute_mcmc_stats([ s[d] for s in mcmc_samples])[3] \
for d in range(D) ]
ave_eff_sample_sizes[D - 1] = sum(eff_sample_sizes) / D
f, axarr = plot.subplots(1, 2)
axarr[0].set_title("")
axarr[0].fill_between(plot_Ds,
[ accept_prob_means[idx] - 2 * accept_prob_ses[idx] for idx in idxs ],
[ accept_prob_means[idx] + 2 * accept_prob_ses[idx] for idx in idxs ],
facecolor=dark, color=dark)
axarr[0].plot(plot_Ds, [ accept_prob_means[idx] for idx in idxs], color=dark_highlight)
axarr[0].set_xlim([Ds[0], Ds[-1]])
axarr[0].set_xlabel("Dimension")
axarr[0].set_ylim([0, 1])
axarr[0].set_ylabel("Average Acceptance Probability")
axarr[1].set_title("")
axarr[1].plot(plot_Ds, [ ave_eff_sample_sizes[idx] / N for idx in idxs],
color=dark_highlight)
axarr[1].set_xlim([Ds[0], Ds[-1]])
axarr[1].set_xlabel("Dimension")
axarr[1].set_ylim([0, 0.3])
axarr[1].set_ylabel("Average Effective Sample Size Per Iteration")
plot.show()
############################################################
# Now let's use an (approximately) optimally tuned Markov
# transition for each dimension
############################################################
accept_prob_means = [0] * len(Ds)
accept_prob_ses = [0] * len(Ds)
ave_eff_sample_sizes = [0] * len(Ds)
# Approximately optimal proposal tuning
opt_sigmas = [2.5, 1.75, 1.5, 1.2, 1.15, 1.0, 0.95, 0.85, 0.8, 0.75]
# Tune proposal density
sigma = 1.4
for D in Ds:
# A place to store our Markov chain
# D columns for the parameters and one extra column
# for the Metropolis acceptance probability
mcmc_samples = [[0] * (D + 1) for _ in range(N)]
# Seeding the initial state with an exact sample
# from the target distribution ensures that we
# start in the typical set and avoid having to
# worry about warmup.
for d in range(D):
mcmc_samples[0][d] = stats.norm.rvs(0, 3)
mcmc_samples[0][D] = 1
for n in range(1, N):
x0 = [ mcmc_samples[n - 1][d] for d in range(D) ]
xp = [ stats.norm.rvs(x0[d], opt_sigmas[D - 1]) for d in range(D) ]
# Compute acceptance probability
accept_prob = 1
if target_lpdf(xp) < target_lpdf(x0):
accept_prob = math.exp(target_lpdf(xp) - target_lpdf(x0))
mcmc_samples[n][D] = accept_prob
# Apply Metropolis correction
u = stats.uniform.rvs(0, 1)
if accept_prob > u:
mcmc_samples[n][0:D] = xp
else:
mcmc_samples[n][0:D] = x0
# Estimate average acceptance probability
# Compute MCMC estimator statistics
mcmc_stats = compute_mcmc_stats([ s[D] for s in mcmc_samples])
accept_prob_means[D - 1] = mcmc_stats[0]
accept_prob_ses[D - 1] = mcmc_stats[1]
# Estimate effective sample size
eff_sample_sizes = [ compute_mcmc_stats([ s[d] for s in mcmc_samples])[3] \
for d in range(D) ]
ave_eff_sample_sizes[D - 1] = sum(eff_sample_sizes) / D
f, axarr = plot.subplots(1, 2)
axarr[0].set_title("")
axarr[0].fill_between(plot_Ds,
[ accept_prob_means[idx] - 2 * accept_prob_ses[idx] for idx in idxs ],
[ accept_prob_means[idx] + 2 * accept_prob_ses[idx] for idx in idxs ],
facecolor=dark, color=dark)
axarr[0].plot(plot_Ds, [ accept_prob_means[idx] for idx in idxs], color=dark_highlight)
axarr[0].set_xlim([Ds[0], Ds[-1]])
axarr[0].set_xlabel("Dimension")
axarr[0].set_ylim([0, 1])
axarr[0].set_ylabel("Average Acceptance Probability")
axarr[1].set_title("")
axarr[1].plot(plot_Ds, [ ave_eff_sample_sizes[idx] / N for idx in idxs],
color=dark_highlight)
axarr[1].set_xlim([Ds[0], Ds[-1]])
axarr[1].set_xlabel("Dimension")
axarr[1].set_ylim([0, 0.3])
axarr[1].set_ylabel("Average Effective Sample Size Per Iteration")
plot.show()
| [
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12... | 2.359052 | 5,612 |
#!/usr/bin/env python3
import os
import filecmp
import tempfile
from opendbc.generator.generator import create_all, opendbc_root
test_generator()
| [
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6... | 3.020408 | 49 |
import vigorish.database as db
from vigorish.enums import DataSet, ScrapeCondition
from vigorish.scrape.brooks_pitchfx.parse_html import parse_pitchfx_log
from vigorish.scrape.scrape_task import ScrapeTaskABC
from vigorish.status.update_status_brooks_pitchfx import update_status_brooks_pitchfx_log
from vigorish.util.dt_format_strings import DATE_ONLY_2
from vigorish.util.result import Result
| [
11748,
10539,
273,
680,
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79,
2007,
21373,
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... | 3 | 132 |
from collections import namedtuple
import io
import json
from furl import furl
from django.core.handlers.wsgi import WSGIRequest
from django.http.request import QueryDict
from django.template import Variable, VariableDoesNotExist
from django.test.client import MULTIPART_CONTENT
from django.urls import resolve
from django.urls.exceptions import Resolver404
from mayan.apps.organizations.settings import setting_organization_url_base_path
from mayan.apps.templating.classes import Template
from .literals import API_VERSION
RenderedContent = namedtuple(
typename='RenderedContent', field_names=(
'body', 'include', 'method', 'name', 'url'
)
)
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6738,
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... | 3.236715 | 207 |
import numpy as np
from torch import nn
def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
"""
Simple function to init layers
"""
nn.init.orthogonal_(layer.weight, std)
nn.init.constant_(layer.bias, bias_const)
return layer
| [
11748,
299,
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355,
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198,
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28034,
1330,
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198,
220,
220,
220,
37227,
198,
... | 2.466019 | 103 |
"""
Test script for src=9 provisioning
Below are some odd examples and notes:
Adding a class
{
'src': '9',
'uln': 'Githens',
'ufn': 'Steven',
'aid': '56021',
'utp': '2',
'said': '56021',
'fid': '2',
'username': 'swgithen',
'ctl': 'CourseTitleb018b622-b425-4af7-bb3d-d0d2b4deb35c',
'diagnostic': '0',
'encrypt': '0',
'uem': 'swgithen@mtu.edu',
'cid': 'CourseTitleb018b622-b425-4af7-bb3d-d0d2b4deb35c',
'fcmd': '2'
}
{rmessage=Successful!, userid=17463901, classid=2836785, rcode=21}
Adding an assignment
{
'fid': '4',
'diagnostic': '0',
'ufn': 'Steven',
'uln': 'Githens',
'username': 'swgithen',
'assignid': 'AssignmentTitlec717957d-254f-4d6d-a64c-952e630db872',
'aid': '56021',
'src': '9',
'cid': 'CourseTitleb018b622-b425-4af7-bb3d-d0d2b4deb35c', 'said': '56021', 'dtstart': '20091225', 'encrypt': '0', 'assign': 'AssignmentTitlec717957d-254f-4d6d-a64c-952e630db872', 'uem': 'swgithen@mtu.edu', 'utp': '2', 'fcmd': '2', 'ctl': 'CourseTitleb018b622-b425-4af7-bb3d-d0d2b4deb35c', 'dtdue': '20100101'}
{rmessage=Successful!, userid=17463901, classid=2836785, assignmentid=7902977, rcode=41}
Adding an assignment with another inst
{'fid': '4', 'diagnostic': '0', 'ufn': 'StevenIU', 'uln': 'GithensIU', 'username': 'sgithens', 'assignid': 'AssignmentTitle5ae51e10-fd60-4720-931b-ed4f58057d00', 'aid': '56021', 'src': '9', 'cid': '2836785', 'said': '56021', 'dtstart': '20091225', 'encrypt': '0', 'assign': 'AssignmentTitle5ae51e10-fd60-4720-931b-ed4f58057d00', 'uem': 'sgithens@iupui.edu', 'utp': '2', 'fcmd': '2', 'ctl': 'CourseTitleb018b622-b425-4af7-bb3d-d0d2b4deb35c', 'dtdue': '20100101'}
{rmessage=Successful!, userid=17463902, classid=2836786, assignmentid=7902978, rcode=41}
Adding a class
{'src': '9', 'uln': 'Githens', 'ufn': 'Steven', 'aid': '56021', 'utp': '2', 'said': '56021', 'fid': '2', 'username': 'swgithen', 'ctl': 'CourseTitle46abd163-7464-4d21-a2c0-90c5af3312ab', 'diagnostic': '0', 'encrypt': '0', 'uem': 'swgithen@mtu.edu', 'fcmd': '2'}
{rmessage=Successful!, userid=17259618, classid=2836733, rcode=21}
Adding an assignment
{'fid': '4', 'diagnostic': '0', 'ufn': 'Steven', 'uln': 'Githens', 'username': 'swgithen', 'assignid': 'AssignmentTitlec4f211c1-2c38-4daf-86dc-3c57c6ef5b7b', 'aid': '56021', 'src': '9', 'cid': '2836733', 'said': '56021', 'dtstart': '20091225', 'encrypt': '0', 'assign': 'AssignmentTitlec4f211c1-2c38-4daf-86dc-3c57c6ef5b7b', 'uem': 'swgithen@mtu.edu', 'utp': '2', 'fcmd': '2', 'ctl': 'CourseTitle46abd163-7464-4d21-a2c0-90c5af3312ab', 'dtdue': '20100101'}
{rmessage=Successful!, userid=17463581, classid=2836734, assignmentid=7902887, rcode=41}
Adding an assignment with another inst
{'fid': '4', 'diagnostic': '0', 'ufn': 'StevenIU', 'uln': 'GithensIU', 'username': 'sgithens', 'assignid': 'AssignmentTitle2650fcca-b96e-42bd-926e-63660076d2ad', 'aid': '56021', 'src': '9', 'cid': '2836733', 'said': '56021', 'dtstart': '20091225', 'encrypt': '0', 'assign': 'AssignmentTitle2650fcca-b96e-42bd-926e-63660076d2ad', 'uem': 'sgithens@iupui.edu', 'utp': '2', 'fcmd': '2', 'ctl': 'CourseTitle46abd163-7464-4d21-a2c0-90c5af3312ab', 'dtdue': '20100101'}
{rmessage=Successful!, userid=17463581, classid=2836734, assignmentid=7902888, rcode=41}
"""
import unittest
import random
import sys
from org.sakaiproject.component.cover import ComponentManager
from java.net import InetSocketAddress, Proxy, InetAddress
from java.util import HashMap
debug_proxy = Proxy(Proxy.Type.HTTP, InetSocketAddress(InetAddress.getByName("127.0.0.1"),8008))
tiireview_serv = ComponentManager.get("org.sakaiproject.contentreview.service.ContentReviewService")
uuid = SakaiUuid()
defaults = {
"aid": "56021",
"said": "56021",
"diagnostic": "0",
"encrypt": "0",
"src": "9"
}
userdummy = {
"uem": "swgithenaabb1234124@mtu.edu",
"ufn": "Stevenaabb1234",
"uln": "Githensaaabb234",
"utp": "2",
"uid": "1979092312341234124aabb",
"username": "swgithenaabb1234124"
}
user = {
"uem": "swgithen@mtu.edu",
"ufn": "Steven",
"uln": "Githens",
"utp": "2",
#"uid": "19790923",
"username": "swgithen"
}
user2 = {
"uem": "sgithens@iupui.edu",
"ufn": "StevenIU",
"uln": "GithensIU",
"utp": "2",
"username": "sgithens"
}
adduser = {
"fcmd" : "2",
"fid" : "1"
}
def callTIIReviewServ(params):
"""Use the Sakai Turnitin Service to make a raw call to TII with the
dictionary of parameters. Returns the API results in map/dict form."""
return tiireview_serv.callTurnitinWDefaultsReturnMap(getJavaMap(params))
def makeNewCourseTitle():
"Make and return a new random title to use for integration test courses"
return "CourseTitle"+str(uuid.uuid1())
def makeNewAsnnTitle():
"Make and return a new random title to use for integration test assignments"
return "AssignmentTitle"+str(uuid.uuid1())
def addSampleInst():
"""This will add/update a user to Turnitin. A successful return looks as
follows:
{rmessage=Successful!, userid=17259618, rcode=11}
It important to note that the userid returned is the userid of whoever made
this API call, and not necessarily the user that was just added.
"""
adduser_cmd = {}
adduser_cmd.update(adduser)
adduser_cmd.update(user)
adduser_cmd.update(defaults)
return callTIIReviewServ(adduser_cmd)
def addSampleClass():
"""Add a simple class using Sakai Source 9 parameters.
Successful results should look as follows:
{rmessage=Successful!, userid=17259618, classid=2833470, rcode=21}
"""
addclass_cmd = {}
addclass_cmd.update(user)
addclass_cmd.update(defaults)
addclass_cmd.update({
"ctl": makeNewCourseTitle(),
"utp":"2",
"fid":"2",
"fcmd":"2"
})
return callTIIReviewServ(addclass_cmd)
def addSampleAssignment():
"""Add a simple assignment."""
course_title = makeNewCourseTitle()
addclass_cmd = {}
addclass_cmd.update(user)
addclass_cmd.update(defaults)
addclass_cmd.update({
"ctl": course_title,
"cid": course_title,
"utp":"2",
"fid":"2",
"fcmd":"2"
})
print("Adding a class\n"+str(addclass_cmd))
addclass_results = callTIIReviewServ(addclass_cmd)
print(addclass_results)
cid = addclass_results["classid"]
asnn_title = makeNewAsnnTitle()
addasnn_cmd = {}
addasnn_cmd.update(user)
addasnn_cmd.update(defaults)
addasnn_cmd.update({
"fid":"4",
"fcmd":"2",
"ctl":course_title,
"assign":asnn_title,
"assignid":asnn_title,
"utp":"2",
"dtstart":"20091225",
"dtdue":"20100101",
"cid":course_title
#"ced":"20110101"
})
print("Adding an assignment\n"+str(addasnn_cmd))
print(callTIIReviewServ(addasnn_cmd))
# Trying with a second instructor now
asnn_title = makeNewAsnnTitle()
addasnn_cmd = {}
addasnn_cmd.update(user2)
addasnn_cmd.update(defaults)
addasnn_cmd.update({
"fid":"4",
"fcmd":"2",
"ctl":course_title,
"assign":asnn_title,
"assignid":asnn_title,
"utp":"2",
"dtstart":"20091225",
"dtdue":"20100101",
"cid":cid
#"ced":"20110101"
})
print("Adding an assignment with another inst\n"+str(addasnn_cmd))
print(callTIIReviewServ(addasnn_cmd))
# Temporarily change to straight HTTP so I can intercept with WebScarab to get a parameter dump
#tiiresult = tiireview_serv.callTurnitinReturnMap("http://www.turnitin.com/api.asp?",
# getJavaMap(adduser_cmd), "sakai123", debug_proxy
# );
if __name__ == "__main__":
main(sys.argv[1:])
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8287,
278,
198,
198,
21106,
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290,
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198,
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257,
1398,
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198,
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705,
24,
3256,
220,
198,
220,
705,
377,
77,
10354,
705,
38,
... | 2.205514 | 3,518 |
print("RUnning!!!")
print("Updated!!!")
| [
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768,
3228,
2474,
8,
198,
4798,
7203,
17354,
3228,
2474,
8,
198
] | 2.5 | 16 |
input = """
% This is a synthetic example documenting a bug in an early version of DLV's
% backjumping algorithm.
% The abstract computation tree looks as follows (choice order should be fixed
% by disabling heuristics with -OH-):
%
% o
% a / \ -a
% / \_..._
% o \
% b / \ -b {-a,-b,f}
% / \
% o o
% incons incons based on a and b
% based
% only
% on b
%
% The backjumping algorithm wrongly determined that in the bottom left
% subtree both inconsistencies are based only on the choice of b and
% therefore stopped the entire search, missing the model on the right.
a | -a.
b | -b.
% taking b causes inconsistency
x :- b.
y :- b.
:- x,y.
% taking -b causes m1 to be MBT, but only with a
% taking -b unconditionally causes d to be false
:- -b, a, not m1.
:- -b, d.
% the constraint is violated if m1 is MBT and d is false
% the reasons are obviously the choice for b and the choice for a
:- m1, not d.
% give m1 a chance to be true
% if not allow a model with f
m1 | f.
% avoid d to be always false
% and allow a model with f
d | f.
"""
output = """
% This is a synthetic example documenting a bug in an early version of DLV's
% backjumping algorithm.
% The abstract computation tree looks as follows (choice order should be fixed
% by disabling heuristics with -OH-):
%
% o
% a / \ -a
% / \_..._
% o \
% b / \ -b {-a,-b,f}
% / \
% o o
% incons incons based on a and b
% based
% only
% on b
%
% The backjumping algorithm wrongly determined that in the bottom left
% subtree both inconsistencies are based only on the choice of b and
% therefore stopped the entire search, missing the model on the right.
a | -a.
b | -b.
% taking b causes inconsistency
x :- b.
y :- b.
:- x,y.
% taking -b causes m1 to be MBT, but only with a
% taking -b unconditionally causes d to be false
:- -b, a, not m1.
:- -b, d.
% the constraint is violated if m1 is MBT and d is false
% the reasons are obviously the choice for b and the choice for a
:- m1, not d.
% give m1 a chance to be true
% if not allow a model with f
m1 | f.
% avoid d to be always false
% and allow a model with f
d | f.
"""
| [
15414,
796,
37227,
198,
4,
770,
318,
257,
18512,
1672,
33045,
257,
5434,
287,
281,
1903,
2196,
286,
23641,
53,
338,
201,
198,
4,
736,
73,
25218,
11862,
13,
201,
198,
201,
198,
4,
383,
12531,
29964,
5509,
3073,
355,
5679,
357,
25541,... | 2.537363 | 910 |
from __future__ import division
from random import *
#...(former location of probability as a FN GLOBAL)
#OUR SUPERCOOL GENETIC MUTANT NINJA TURTALGORITHM
#The algorithm which dictates what our hand does
#bustThreshold is the determinant for whether we hit or stay
#returns a list with [highest probable dealer hand value, percentage of getting that value]
#Returns a float that is the chance of busting
#returns the total number of cards in the pile
#creates a list of hands incl dealer and initializes the non-dealer hands
#Give it a pile, hand, and the amount of cards to deal
#Returns an array where the index is the value of the hand and the value is the chance of getting it
#changable algorithm default to soft 17 hit, updating dealer's hand / dealer decision algorithm
#chooses a random card from the pile, value 0-12
#DON'T TOUCH
#removes a card from the pile, value 0-12
#adds a card to hand
#calculates value of a hand
#figure out how to deal with an Ace (card = 0)
#Given threshold, returns True to hit, False to stay
#need to make it possible to get probability of going over
#calculates probability of drawing a card from the pile
#returns the number of a specific kind of card in the pile
#checks each hand in handList to see if it has busted, returns true if over.
| [
6738,
11593,
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834,
1330,
7297,
198,
6738,
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198,
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286,
12867,
355,
257,
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33088,
8220,
3535,
24700,
2767,
2149,
337,
3843,
8643,
399,
1268,
37... | 3.647059 | 357 |
import logging
from collections import Generator
from typing import Dict
from spanner import ems_spanner_client
from tenacity import retry, stop_after_attempt, wait_fixed
| [
11748,
18931,
198,
6738,
17268,
1330,
35986,
198,
6738,
19720,
1330,
360,
713,
198,
198,
6738,
11506,
1008,
1330,
795,
82,
62,
12626,
1008,
62,
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198,
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3478,
4355,
1330,
1005,
563,
11,
2245,
62,
8499,
62,
1078,
1791,
11,
404... | 3.844444 | 45 |
from typing import Any
from click import echo, style
| [
6738,
19720,
1330,
4377,
198,
6738,
3904,
1330,
9809,
11,
3918,
628,
198
] | 4.230769 | 13 |
from django.db.models.signals import m2m_changed
from django.dispatch import receiver
from .models import Image
| [
6738,
42625,
14208,
13,
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13,
27530,
13,
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874,
1330,
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76,
62,
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14208,
13,
6381,
17147,
1330,
9733,
198,
6738,
764,
27530,
1330,
7412,
198
] | 3.5 | 32 |
import random
from typing import List, Union
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as F
from PIL import Image
def _get_image_size(img: Union[Image.Image, torch.Tensor]):
if isinstance(img, torch.Tensor):
return _get_tensor_image_size(img)
elif isinstance(img, Image.Image):
return img.size
raise TypeError("Unexpected input type")
def _is_tensor_a_torch_image(x: torch.Tensor) -> bool:
return x.ndim >= 2
def _get_tensor_image_size(img: torch.Tensor) -> List[int]:
"""Returns (w, h) of tensor image"""
if _is_tensor_a_torch_image(img):
return [img.shape[-1], img.shape[-2]]
raise TypeError("Unexpected input type")
| [
11748,
4738,
198,
198,
6738,
19720,
1330,
7343,
11,
4479,
198,
198,
11748,
28034,
198,
11748,
28034,
10178,
13,
7645,
23914,
355,
309,
198,
11748,
28034,
10178,
13,
7645,
23914,
13,
45124,
355,
376,
198,
198,
6738,
350,
4146,
1330,
7412... | 2.609929 | 282 |
import pandas as pd
# Wczytaj do DataFrame arkusz z narodzinami dzieci
# w Polsce dostpny pod adresem
df = pd.read_csv('Imiona_dzieci_2000-2019.csv')
| [
11748,
19798,
292,
355,
279,
67,
198,
198,
2,
370,
66,
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83,
1228,
466,
6060,
19778,
610,
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89,
1976,
30083,
375,
42140,
6277,
288,
49746,
979,
198,
2,
266,
2165,
82,
344,
288,
455,
79,
3281,
24573,
512,
260,
43616,
220,
... | 2.25 | 68 |
\
import os
from keras import applications
import keras
import tensorflow as tf
import time
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
keras.backend.tensorflow_backend.set_session(tf.Session(config=config))
from keras.models import Model
from keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import Adam,SGD
from keras.callbacks import ModelCheckpoint,CSVLogger
from keras import backend as k
DATASET_PATH = '/deepLearning/jamccomb/chest_xray/'
IMAGE_SIZE = (150,150)
NUM_CLASSES = 2
BATCH_SIZE = 32 # try reducing batch size or freeze more layers if your GPU runs out of memory
NUM_EPOCHS = 35
WEIGHTS_FINAL = 'model-transfer-Chest-MobileNet-000001--final.h5'
train_datagen = ImageDataGenerator( rescale=1.0 / 255.0,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
channel_shift_range=10,
horizontal_flip=True,
fill_mode='nearest')
train_batches = train_datagen.flow_from_directory(DATASET_PATH + '/train',
target_size=IMAGE_SIZE,
interpolation='bicubic',
class_mode='categorical',
shuffle=True,
batch_size=BATCH_SIZE)
valid_datagen = ImageDataGenerator(rescale=1.0/255.0)
valid_batches = valid_datagen.flow_from_directory(DATASET_PATH + '/test',
target_size=IMAGE_SIZE,
interpolation='bicubic',
class_mode='categorical',
shuffle=False,
batch_size=BATCH_SIZE)
lrelu = lambda x: tensorflow.keras.activations.relu(x, alpha=0.1)
# Load VGG16 model architecture with the ImageNet weights
model = applications.VGG16(weights = "imagenet", include_top=False, input_shape=[150,150,3])
# Freeze the layers which you don't want to train. Here I am freezing the first 5 layers.
for layer in model.layers[:14]:
layer.trainable = False
# Build classifier
x = model.output
x = Flatten()(x)
x = Dense(32, activation="sigmoid")(x)
predictions = Dense(2, activation="softmax")(x)
#Use Adam optimizer (instead of plain SGD), set learning rate to explore.
adam = Adam(lr=.00001)
#instantiate model
model = Model(input=model.input, output=predictions)
#Compile model
model.compile(optimizer = adam, loss='categorical_crossentropy', metrics=['accuracy'])
#Print layers for resulting model
model.summary()
#Log training data into csv file
csv_logger = CSVLogger(filename="vgg16-imagenet-log.csv")
checkpointer = ModelCheckpoint(filepath='MobileNet/000001//weights.{epoch:02d}-{val_acc:.2f}.hdf5',monitor='val_loss', verbose=1, save_best_only=True, mode='min')
cblist = [csv_logger, checkpointer]
# train the model
model.fit_generator(train_batches,
steps_per_epoch = train_batches.samples // BATCH_SIZE,
validation_data = valid_batches,
validation_steps = valid_batches.samples // BATCH_SIZE,
epochs = NUM_EPOCHS,
callbacks=cblist)
# save trained model and weights
model.save(WEIGHTS_FINAL)
| [
59,
198,
198,
11748,
28686,
198,
6738,
41927,
292,
1330,
5479,
198,
11748,
41927,
292,
198,
11748,
11192,
273,
11125,
355,
48700,
198,
11748,
640,
198,
198,
11250,
796,
48700,
13,
16934,
2964,
1462,
3419,
198,
11250,
13,
46999,
62,
2581... | 1.463715 | 4,065 |
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
# See COPYING file distributed along with the PyMVPA package for the
# copyright and license terms.
#
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
"""Unit tests for PyMVPA Procrustean mapper"""
import unittest
import numpy as np
import itertools
from numpy.linalg import norm
from mvpa2.base import externals
from mvpa2.datasets.base import dataset_wizard
from mvpa2.testing import *
from mvpa2.testing.datasets import *
from mvpa2.mappers.procrustean import ProcrusteanMapper
svds = ["numpy"]
if externals.exists("liblapack.so"):
svds += ["dgesvd"]
if externals.exists("scipy"):
svds += ["scipy"]
if __name__ == "__main__": # pragma: no cover
from . import runner
runner.run()
| [
2,
795,
16436,
25,
532,
9,
12,
4235,
25,
21015,
26,
12972,
12,
521,
298,
12,
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25,
604,
26,
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12,
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12,
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198,
2,
25357,
25,
900,
10117,
28,
29412,
39747,
28,
19,
40379,
28,
19,
15... | 2.744868 | 341 |
# -*- coding: utf-8 -*-
# @Time : 2020/12/1 11:24
# @Author :
# @File : production.py
# @Software: Pycharm
from configs.default import DefaultConfig
production_config = ProductionConfig()
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
2,
2488,
7575,
220,
1058,
12131,
14,
1065,
14,
16,
1367,
25,
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198,
2,
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220,
198,
2,
2488,
8979,
1058,
3227,
13,
9078,
198,
2,
2488,
25423,
25,... | 2.797101 | 69 |
from unittest import TestCase
from glider.modules.glider_radio import GliderRadio
| [
6738,
555,
715,
395,
1330,
6208,
20448,
198,
198,
6738,
1278,
1304,
13,
18170,
13,
4743,
1304,
62,
37004,
1330,
2671,
1304,
26093,
198
] | 3.458333 | 24 |
# Generated by Django 2.2.7 on 2019-12-15 12:15
from django.db import migrations, models
| [
2,
2980,
515,
416,
37770,
362,
13,
17,
13,
22,
319,
13130,
12,
1065,
12,
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1105,
25,
1314,
198,
198,
6738,
42625,
14208,
13,
9945,
1330,
15720,
602,
11,
4981,
628
] | 2.84375 | 32 |
#!/pxrpythonsubst
#
# Copyright 2016 Pixar
#
# Licensed under the Apache License, Version 2.0 (the "Apache License")
# with the following modification; you may not use this file except in
# compliance with the Apache License and the following modification to it:
# Section 6. Trademarks. is deleted and replaced with:
#
# 6. Trademarks. This License does not grant permission to use the trade
# names, trademarks, service marks, or product names of the Licensor
# and its affiliates, except as required to comply with Section 4(c) of
# the License and to reproduce the content of the NOTICE file.
#
# You may obtain a copy of the Apache License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the Apache License with the above modification is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the Apache License for the specific
# language governing permissions and limitations under the Apache License.
#
from pxr import Tf
import logging
import unittest
if __name__ == '__main__':
unittest.main()
| [
2,
48443,
8416,
81,
79,
5272,
684,
549,
301,
198,
2,
198,
2,
15069,
1584,
46706,
198,
2,
198,
2,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
1169,
366,
25189,
4891,
13789,
4943,
198,
2,
351,
262,
1708,
17613,
26,
... | 3.701258 | 318 |
#!/usr/bin/python
import subprocess
import sys
import cgi
import datetime
import re
import requests
validMac = False
ERROR = False
form = cgi.FieldStorage()
user = "READONLY_USER_HERE"
pwd = "PASSWORD"
OUI = form.getvalue('OUI')
host = form.getvalue('HOST')
fOUI = formatOUI(OUI)
webCmd = "show ip arp | i {}".format(OUI[0:7])
printHeader()
validMac = checkInput()
if validMac == False:
print "<CENTER><h3>{} OUI not formatted correctly, please use xxxx.xx (Cisco format).</h3></CENTER>".format(OUI)
else:
try:
lookup(fOUI)
except:
ERROR = True
print "<CENTER>OUI not found in database!<br>Check and try again</CENTER>"
if ERROR == False:
executeCmd(host)
| [
2,
48443,
14629,
14,
8800,
14,
29412,
201,
198,
11748,
850,
14681,
201,
198,
11748,
25064,
201,
198,
11748,
269,
12397,
201,
198,
11748,
4818,
8079,
201,
198,
11748,
302,
201,
198,
11748,
7007,
201,
198,
201,
198,
12102,
14155,
796,
1... | 2.140162 | 371 |
#!/usr/bin/env python3
import csv
import glob
import os.path
from collections import deque
from tqdm import tqdm
if __name__ == '__main__':
main('../data/hmong/extracted_elabs/elabs_extracted.csv',
'../data/hmong/sch_corpus2_conll',
'../data/hmong/sch_corpus2_elab') | [
2,
48443,
14629,
14,
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18,
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269,
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11748,
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390,
4188,
198,
198,
6738,
256,
80,
36020,
1330,
256,
80,
36020,
198,
198,
361,
1159... | 2.369748 | 119 |
# Copyright 2020 Google LLC.
# This software is provided as-is, without warranty or representation for any use or purpose.
# Your use of it is subject to your agreement with Google.
from apache_beam import DoFn, pvalue
from apache_beam.metrics import Metrics
from ..model import data_classes
from ..model.data_classes import Record
| [
2,
220,
15069,
12131,
3012,
11419,
13,
198,
2,
220,
770,
3788,
318,
2810,
355,
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271,
11,
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393,
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329,
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198,
2,
220,
3406,
779,
286,
340,
318,
2426,
284,
534,
4381,
351,
3012,
13,
198,
... | 3.818182 | 88 |
from __future__ import absolute_import
from sentry.integrations.client import ApiClient
from sentry.models import EventCommon
from sentry.api.serializers import serialize, ExternalEventSerializer
LEVEL_SEVERITY_MAP = {
"debug": "info",
"info": "info",
"warning": "warning",
"error": "error",
"fatal": "critical",
}
| [
6738,
11593,
37443,
834,
1330,
4112,
62,
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198,
198,
6738,
1908,
563,
13,
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1330,
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72,
11792,
198,
6738,
1908,
563,
13,
27530,
1330,
8558,
17227,
198,
6738,
1908,
563,
13,
15042,
13,
46911,
11341,
1330,
... | 2.964912 | 114 |
# coding: utf-8
# Copyright (c) 2016, 2020, Oracle and/or its affiliates. All rights reserved.
# This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license.
from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401
from oci.decorators import init_model_state_from_kwargs
| [
2,
19617,
25,
3384,
69,
12,
23,
198,
2,
15069,
357,
66,
8,
1584,
11,
12131,
11,
18650,
290,
14,
273,
663,
29116,
13,
220,
1439,
2489,
10395,
13,
198,
2,
770,
3788,
318,
10668,
12,
36612,
284,
345,
739,
262,
14499,
2448,
33532,
1... | 3.097561 | 164 |
import bpy
import numpy as np
import math
import mathutils
import time
import os
| [
11748,
275,
9078,
201,
198,
11748,
299,
32152,
355,
45941,
201,
198,
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10688,
201,
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11748,
10688,
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201,
198,
11748,
640,
201,
198,
11748,
28686,
201,
198,
201,
198,
201,
198,
201,
198,
201,
198,
201,
198
] | 2.487179 | 39 |
# -*- coding: utf-8 -*-
"""
Created on Fri Feb 6 17:38:00 2015
@author: dbwrigh3
"""
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
37811,
198,
41972,
319,
19480,
3158,
220,
718,
1596,
25,
2548,
25,
405,
1853,
198,
198,
31,
9800,
25,
20613,
18351,
394,
18,
198,
37811,
628
] | 2.2 | 40 |
# -*- coding: utf-8 -*-
from setuptools import setup, find_packages
with open('requirements.txt') as f:
install_requires = f.read().strip().split('\n')
# get version from __version__ variable in proceso/__init__.py
from proceso import __version__ as version
setup(
name='proceso',
version=version,
description='A customization app for Proceso',
author='Lewin Villar',
author_email='lewinvillar@tzcode.tech',
packages=find_packages(),
zip_safe=False,
include_package_data=True,
install_requires=install_requires
)
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
6738,
900,
37623,
10141,
1330,
9058,
11,
1064,
62,
43789,
198,
198,
4480,
1280,
10786,
8897,
18883,
13,
14116,
11537,
355,
277,
25,
198,
197,
17350,
62,
47911,
796,
277,... | 2.955056 | 178 |
# this project is licensed under the WTFPLv2, see COPYING.txt for details
import logging
from weakref import ref
from PyQt5.QtCore import QEventLoop
from PyQt5.QtWidgets import QPlainTextEdit, QLabel, QWidget, QRubberBand, QApplication
from ..app import qApp
from ..qt import Slot, Signal
from .helpers import WidgetMixin
__all__ = ('LogWidget', 'PositionIndicator', 'WidgetPicker', 'interactiveWidgetPick')
def interactiveWidgetPick():
"""Let user peek a widget by clicking on it.
The user can point at open EYE widgets and click on one. Return the widget that was clicked
by the user.
"""
w = WidgetPicker()
return w.run()
| [
2,
428,
1628,
318,
11971,
739,
262,
41281,
5837,
29507,
17,
11,
766,
27975,
45761,
13,
14116,
329,
3307,
198,
198,
11748,
18931,
198,
6738,
4939,
5420,
1330,
1006,
198,
198,
6738,
9485,
48,
83,
20,
13,
48,
83,
14055,
1330,
1195,
923... | 3.242424 | 198 |
# -*- coding: utf-8 -*-
# Copyright (c) 2013 Tomasz Wjcik <tomek@bthlabs.pl>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
"""
envelopes.envelope
==================
This module contains the Envelope class.
"""
import sys
if sys.version_info[0] == 2:
from email import Encoders as email_encoders
elif sys.version_info[0] == 3:
from email import encoders as email_encoders
basestring = str
else:
raise RuntimeError('Unsupported Python version: %d.%d.%d' % (
sys.version_info[0], sys.version_info[1], sys.version_info[2]
))
from email.header import Header
from email.mime.base import MIMEBase
from email.mime.multipart import MIMEMultipart
from email.mime.application import MIMEApplication
from email.mime.audio import MIMEAudio
from email.mime.image import MIMEImage
from email.mime.text import MIMEText
import mimetypes
import os
import re
from .conn import SMTP
from .compat import encoded
def clear_cc_addr(self):
"""Clears list of CC addresses."""
self._cc = []
def add_bcc_addr(self, bcc_addr):
"""Adds a BCC address."""
self._bcc.append(bcc_addr)
def clear_bcc_addr(self):
"""Clears list of BCC addresses."""
self._bcc = []
def _addr_tuple_to_addr(self, addr_tuple):
addr = ''
if len(addr_tuple) == 2 and addr_tuple[1]:
addr = self._addr_format % (
self._header(addr_tuple[1] or ''),
addr_tuple[0] or ''
)
elif addr_tuple[0]:
addr = addr_tuple[0]
return addr
def add_header(self, key, value):
"""Adds a custom header."""
self._headers[key] = value
def clear_headers(self):
"""Clears custom headers."""
self._headers = {}
def to_mime_message(self):
"""Returns the envelope as
:py:class:`email.mime.multipart.MIMEMultipart`."""
msg = MIMEMultipart('alternative')
msg['Subject'] = self._header(self._subject or '')
msg['From'] = self._encoded(self._addrs_to_header([self._from]))
msg['To'] = self._encoded(self._addrs_to_header(self._to))
if self._cc:
msg['CC'] = self._addrs_to_header(self._cc)
if self._headers:
for key, value in self._headers.items():
msg[key] = self._header(value)
for part in self._parts:
type_maj, type_min = part[0].split('/')
if type_maj == 'text' and type_min in ('html', 'plain'):
msg.attach(MIMEText(part[1], type_min, self._charset))
else:
msg.attach(part[1])
return msg
def add_attachment(self, file_path, mimetype=None):
"""Attaches a file located at *file_path* to the envelope. If
*mimetype* is not specified an attempt to guess it is made. If nothing
is guessed then `application/octet-stream` is used."""
if not mimetype:
mimetype, _ = mimetypes.guess_type(file_path)
if mimetype is None:
mimetype = 'application/octet-stream'
type_maj, type_min = mimetype.split('/')
with open(file_path, 'rb') as fh:
part_data = fh.read()
part = MIMEBase(type_maj, type_min)
part.set_payload(part_data)
email_encoders.encode_base64(part)
part_filename = os.path.basename(self._encoded(file_path))
part.add_header('Content-Disposition', 'attachment; filename="%s"'
% part_filename)
self._parts.append((mimetype, part))
def send(self, *args, **kwargs):
"""Sends the envelope using a freshly created SMTP connection. *args*
and *kwargs* are passed directly to :py:class:`envelopes.conn.SMTP`
constructor.
Returns a tuple of SMTP object and whatever its send method returns."""
conn = SMTP(*args, **kwargs)
send_result = conn.send(self)
return conn, send_result
| [
2,
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9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
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2,
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66,
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13,
489,
29,
198,
2,
198,
2,
2448,
3411,
318,
29376,
7520,
11,... | 2.43881 | 2,051 |
import types
import tkinter
import Pmw
import sys
import collections
| [
11748,
3858,
198,
11748,
256,
74,
3849,
198,
11748,
350,
76,
86,
198,
11748,
25064,
198,
11748,
17268,
198
] | 3.631579 | 19 |
import os
import torch
import argparse
from util import util
| [
11748,
28686,
198,
11748,
28034,
198,
11748,
1822,
29572,
198,
6738,
7736,
1330,
7736,
198
] | 4.066667 | 15 |
import os
from pypdflite.pdflite import PDFLite
from pypdflite.pdfobjects.pdfcolor import PDFColor
def TableTest(test_dir):
""" Functional test for text, paragraph, and page
splitting.
"""
data = [["Heading1", "Heading2", "Heading3"],
["Cell a2", "Cell b2", "Cell c2"],
["Cell a3", "Cell b3", "Cell c3"]]
#Create PDFLITE object, initialize with path & filename.
writer = PDFLite(os.path.join(test_dir, "tests/TableTest.pdf"))
# If desired (in production code), set compression
# writer.setCompression(True)
# Set general information metadata
writer.set_information(title="Testing Table") # set optional information
# Use get_document method to get the generated document object.
document = writer.get_document()
document.set_cursor(100, 100)
document.set_font(family='arial', style='UB', size=12)
underline = document.get_font()
document.set_font(family='arial', size=12)
default_font = document.get_font()
# Example for adding short and long text and whitespaces
mytable = document.add_table(3, 3)
green = PDFColor(name='green')
default = document.add_cell_format({'font': default_font, 'align': 'left', 'border': (0, 1)})
justleft = document.add_cell_format({'left': (0, 1)})
header_format = document.add_cell_format({'font': underline, 'align': 'right', 'border': (0, 1)})
green_format = document.add_cell_format({'font': default_font, 'border': (0, 1), 'fill_color': green})
#mytable.set_column_width(1, 200)
#mytable.set_row_height(2, 200)
mytable.write_row(0, 0, data[0], header_format)
mytable.write_row(1, 0, data[1], justleft)
mytable.write_row(2, 0, data[2], green_format)
document.draw_table(mytable)
document.add_newline(4)
document.add_text("Testing followup text")
# Close writer
writer.close()
if __name__ == "__main__":
TableTest()
| [
11748,
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201,
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2704,
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201,
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12315,
8043,
1330,
14340,
4851,
45621,
201,
198,
201,
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201,
198,... | 2.504403 | 795 |
from typing import List, Optional
from pydantic import BaseModel
from pydantic import validator
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] | 3.814815 | 27 |
import unittest
from a816.parse.ast.expression import eval_expression_str
from a816.symbols import Resolver
| [
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628
] | 3.142857 | 35 |
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: syft_proto/frameworks/crypten/onnx_model.proto
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
from syft_proto.types.syft.v1 import id_pb2 as syft__proto_dot_types_dot_syft_dot_v1_dot_id__pb2
DESCRIPTOR = _descriptor.FileDescriptor(
name='syft_proto/frameworks/crypten/onnx_model.proto',
package='syft_proto.frameworks.torch.tensors.interpreters.v1',
syntax='proto3',
serialized_options=b'\n@org.openmined.syftproto.frameworks.torch.tensors.interpreters.v1',
create_key=_descriptor._internal_create_key,
serialized_pb=b'\n.syft_proto/frameworks/crypten/onnx_model.proto\x12\x33syft_proto.frameworks.torch.tensors.interpreters.v1\x1a!syft_proto/types/syft/v1/id.proto\"\x9a\x01\n\tOnnxModel\x12,\n\x02id\x18\x01 \x01(\x0b\x32\x1c.syft_proto.types.syft.v1.IdR\x02id\x12)\n\x10serialized_model\x18\x02 \x01(\x0cR\x0fserializedModel\x12\x12\n\x04tags\x18\x03 \x03(\tR\x04tags\x12 \n\x0b\x64\x65scription\x18\x04 \x01(\tR\x0b\x64\x65scriptionBB\n@org.openmined.syftproto.frameworks.torch.tensors.interpreters.v1b\x06proto3'
,
dependencies=[syft__proto_dot_types_dot_syft_dot_v1_dot_id__pb2.DESCRIPTOR,])
_ONNXMODEL = _descriptor.Descriptor(
name='OnnxModel',
full_name='syft_proto.frameworks.torch.tensors.interpreters.v1.OnnxModel',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='syft_proto.frameworks.torch.tensors.interpreters.v1.OnnxModel.id', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, json_name='id', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='serialized_model', full_name='syft_proto.frameworks.torch.tensors.interpreters.v1.OnnxModel.serialized_model', index=1,
number=2, type=12, cpp_type=9, label=1,
has_default_value=False, default_value=b"",
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, json_name='serializedModel', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='syft_proto.frameworks.torch.tensors.interpreters.v1.OnnxModel.tags', index=2,
number=3, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, json_name='tags', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='description', full_name='syft_proto.frameworks.torch.tensors.interpreters.v1.OnnxModel.description', index=3,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, json_name='description', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=139,
serialized_end=293,
)
_ONNXMODEL.fields_by_name['id'].message_type = syft__proto_dot_types_dot_syft_dot_v1_dot_id__pb2._ID
DESCRIPTOR.message_types_by_name['OnnxModel'] = _ONNXMODEL
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
OnnxModel = _reflection.GeneratedProtocolMessageType('OnnxModel', (_message.Message,), {
'DESCRIPTOR' : _ONNXMODEL,
'__module__' : 'syft_proto.frameworks.crypten.onnx_model_pb2'
# @@protoc_insertion_point(class_scope:syft_proto.frameworks.torch.tensors.interpreters.v1.OnnxModel)
})
_sym_db.RegisterMessage(OnnxModel)
DESCRIPTOR._options = None
# @@protoc_insertion_point(module_scope)
| [
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19653,
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268,
14,
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77,... | 2.371932 | 1,874 |
import numpy as np
import knapsack
| [
11748,
299,
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198,
11748,
638,
1686,
441,
628
] | 3.272727 | 11 |
#!/usr/bin/env python
# -*- coding: iso-8859-15 -*-
######################## -*- coding: utf-8 -*-
"""Usage: plotres.py variable INPUTFILE(S)
"""
import sys
from getopt import gnu_getopt as getopt
import matplotlib.pyplot as plt
import numpy as np
import datetime
# parse command-line arguments
try:
optlist,args = getopt(sys.argv[1:], ':', ['verbose'])
assert len(args) > 1
except (AssertionError):
sys.exit(__doc__)
files=[]
mystr=args[0]
if len(args)<2:
from glob import glob
for infile in glob(args[1]):
files.append(infile)
else:
files=args[1:]
#
def get_output (fnames, mystring):
"""parse fname and get some numbers out"""
timev = []
myvar = []
pp = []
for fname in fnames:
try:
f=open(fname)
except:
print(fname + " does not exist, continuing")
else:
# p = []
for line in f:
if "time_secondsf" in line:
ll = line.split()
# p.append(float(ll[-1].replace('D','e')))
# p.append(np.NaN)
timev.append(float(ll[-1].replace('D','e')))
myvar.append(np.NaN)
if mystring in line:
ll = line.split()
# p[1] = float(ll[-1].replace('D','e'))
# pp.append(p)
# p = []
myvar[-1] = float(ll[-1].replace('D','e'))
f.close()
timevs=np.asarray(timev)
myvars=np.asarray(myvar)
isort = np.argsort(timevs)
timevs=timevs[isort]
myvars=myvars[isort]
# ppp = sorted( pp, key = getKey )
# indx = sorted(range(len(timev)), key=lambda k: timev[k])
# myvars=[]
# timevs=[]
# for k in range(len(pp)):
# myvars.append(ppp[k][1])
# timevs.append(ppp[k][0])
return timevs, myvars
# done
fig = plt.figure(figsize=(12, 4))
ax=fig.add_subplot(111)
refdate = datetime.datetime(1,1,1,0,0)
#refdate = datetime.datetime(1979,1,1,0,0)
#refdate = datetime.datetime(1958,1,1,0,0)
# determine start date
with open(files[0]) as f:
for line in f:
if 'startDate_1' in line:
ll = line.strip().split('=')[-1]
refdate = datetime.datetime(int(ll[0:4]),int(ll[4:6]),int(ll[6:8]))
#refdate = datetime.datetime(2001,1,1)
timesec, h = get_output(files, mystr)
if np.all(np.isnan(h)): sys.exit("only nans in timeseries")
timeday = np.asarray(timesec)/86400.
#xdays = refdate + timeday * datetime.timedelta(days=1)
xdays = np.array([refdate + datetime.timedelta(days=i) for i in timeday])
# now plot everything
#print timesec[0:2], timesec[-3:-1]
#print h[0:2], h[-3:-1]
#print timesec
#print h
ax.plot(xdays, h, '-x', linewidth=1.0)
plt.grid()
plt.title(mystr)
hh=np.ma.masked_array(h,np.isnan(h))
print("mean = "+str(np.mean(hh)))
print("min = "+str(np.min(hh)))
print("max = "+str(np.max(hh)))
print("std = "+str(np.std(hh)))
print("last-first = "+str(h[-1]-h[0]))
plt.show()
| [
2,
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14,
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2,
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9,
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9,
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9,
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411,
... | 1.945981 | 1,555 |
# -*-coding:Utf-8 -*
# Copyright (c) 2010-2017 LE GOFF Vincent
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
# * Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software
# without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT
# OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
"""Fichier contenant le contexte 'communication:immersion'"""
from primaires.format.constantes import ponctuations_finales
from primaires.interpreteur.contexte import Contexte
from primaires.communication.contextes.invitation import Invitation
| [
2,
532,
9,
12,
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25,
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69,
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532,
9,
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198,
2,
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66,
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198,
2,
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396,
3890,
290,
779,
287,
2723,
290,
... | 3.539961 | 513 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 13 12:07:22 2020
@author: medrclaa
Stand alone script for testing arc in ARC. simply run
pytest arc_test.py
To ensure the working environment is suitable for running experiments.
If you only wish to run a single experiment then you an easily hash the
other 2 for quicker testing time.
"""
import unittest
import os
"""
run file in ukf_experiments. putting test at top level allows the
large number of
"""
"if running this file on its own. this will move cwd up to ukf_experiments."
if os.path.split(os.getcwd())[1] != "ukf_experiments":
os.chdir("..")
import arc.arc as arc
from modules.ukf_fx import HiddenPrints
if __name__ == '__main__':
"test the three experiments arc functions are working"
" each test uses 5 agents and some arbitrary parameters for the sake of speed"
arc_tests =Test_arc.setUpClass()
unittest.main() | [
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81,
565,... | 3.019293 | 311 |
import setuptools
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name = "nomics-python",
version = "3.1.0",
author = "Taylor Facen",
author_email = "taylor.facen@gmail.com",
description = "A python wrapper for the Nomics API",
long_description = long_description,
long_description_content_type = "text/markdown",
url = "https://github.com/TaylorFacen/nomics-python",
packages = setuptools.find_packages(),
install_requires = ['requests>=2'],
classifiers = [
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License"
]
) | [
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198,
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7,
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... | 2.626016 | 246 |
import os
from pathlib import Path
from typing import List
from challenges.day3 import frequency_character
def _read_input() -> List[str]:
"""Read the input file."""
travel_map = []
current_path = Path(os.path.dirname(os.path.realpath(__file__)))
image_path = current_path / "resources" / "day3_puzzle_input.txt"
with image_path.open("r", encoding="utf-8") as input_file:
for line in input_file:
travel_map.append(str(line.strip()))
return travel_map
| [
11748,
28686,
198,
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3108,
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58,
2536,
5974,
198,
220,
220,
220,
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55... | 2.693548 | 186 |
#!/usr/bin/env python
# coding: utf-8
from msgpack import unpackb
| [
2,
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198,
2,
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31456,
8002,
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555,
8002,
65,
628,
628,
628,
628,
628,
628
] | 2.516129 | 31 |
'''tzinfo timezone information for Asia/Brunei.'''
from pytz.tzinfo import DstTzInfo
from pytz.tzinfo import memorized_datetime as d
from pytz.tzinfo import memorized_ttinfo as i
Brunei = Brunei()
| [
7061,
6,
22877,
10951,
640,
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1321,
329,
7229,
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1330,
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1143,
62,
19608,
8079,... | 2.926471 | 68 |
# -*- coding: utf-8 -*-
import pytest
from raiden.messages import Ping, Ack, decode, Lock, MediatedTransfer
from raiden.utils import make_privkey_address, sha3
PRIVKEY, ADDRESS = make_privkey_address()
| [
2,
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7... | 2.772152 | 79 |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 5 12:41:09 2017
@author: lracuna
"""
#!/usr/bin/env python
"""
This program uses a simple implementation of the ADMM algorithm to solve
the circle packing problem.
We solve
minimize 1
subject to |x_i - x_j| > 2R,
R < x_i, y_i < L - R
We put a bunch of equal radius balls inside a square.
Type --help to see the options of the program.
Must create a directory .figs.
Guilherme Franca
guifranca@gmail.com
November 2015
"""
import sys, os, optparse
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle, Circle
def nonoverlap(a, i, omega, R):
"""No overlap constraint.
This function receives a 1D array which is the row of a matrix.
Each element is a vector. i is which row we are passing.
"""
nonzeroi = np.nonzero(omega[i])[0]
x = a
n1, n2 = a[nonzeroi]
vec = n1 - n2
norm = np.linalg.norm(vec)
if norm < 2*R: # push the balls appart
disp = R - norm/2
x[nonzeroi] = n1 + (disp/norm)*vec, n2 - (disp/norm)*vec
return x
def insidebox(a, i, omega, R, L):
"""Keep the balls inside the box."""
j = np.nonzero(omega[i])[0][0]
x = a
n = a[j]
if n[0] < R:
x[j,0] = R
elif n[0] > L-R:
x[j,0] = L-R
if n[1] < R:
x[j,1] = R
elif n[1] > L-R:
x[j,1] = L-R
return x
def make_graph(t, z, imgpath, R, L):
"""Create a plot of a given time.
z contains a list of vectors with the position of the center of
each ball. t is the iteration time.
"""
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
fig.suptitle('t=%i' % t)
ax.add_patch(Rectangle((0,0), L, L, fill=False,
linestyle='solid', linewidth=2, color='blue'))
plt.xlim(-0.5, L+0.5)
plt.ylim(-0.5, L+0.5)
plt.axes().set_aspect('equal')
colors = iter(plt.cm.prism_r(np.linspace(0,1,N)))
for x in z:
c = next(colors)
ax.add_patch(Circle(x, radius=R, color=c, alpha=.6))
plt.axis('off')
fig.tight_layout()
fig.savefig(imgpath % t, format='png')
print imgpath
plt.close(fig)
def make_omega(N):
"""Topology matrix
Columns label variables, and rows the functions.
You must order all the "nonoverlap" functions first
and the "inside box" function last.
We also create a vectorized version of omega.
"""
o1 = []
o2 = []
one = np.array([1,1])
zero = np.array([0,0])
# TODO: this is the most expensive way of creating these matrices.
# Maybe improve this.
for i in range(N):
for j in range(i+1, N):
row1 = [0]*N
row1[i], row1[j] = 1, 1
o1.append(row1)
row2 = [zero]*N
row2[i], row2[j] = one, one
o2.append(row2)
for i in range(N):
row = [0]*N
row[i] = 1
o1.append(row)
row2 = [zero]*N
row2[i] = one
o2.append(row2)
o1 = np.array(o1)
o2 = np.array(o2)
return o1, o2
###############################################################################
if __name__ == '__main__':
usg = "%prog -L box -R radius -N balls -M iter [-r rate -o output]"
dsc = "Use ADMM optimization algorithm to fit balls into a box."
parser = optparse.OptionParser(usage=usg, description=dsc)
parser.add_option('-L', '--box_size', action='store', dest='L',
type='float', help='size of the box')
parser.add_option('-R', '--radius', action='store', dest='R',
type='float', help='radius of the balls')
parser.add_option('-N', '--num_balls', action='store', dest='N',
type='int', help='number of balls')
parser.add_option('-M', '--iter', action='store', dest='M',
type='int', help='number of iterations')
parser.add_option('-r', '--rate', action='store', dest='rate',
default=10, type='float', help='frame rate for the movie')
parser.add_option('-o', '--output', action='store', dest='out',
default='out.mp4', type='str', help='movie output file')
parser.add_option('-a', '--alpha', action='store', dest='alpha',
default=0.05, type='float', help='alpha parameter')
parser.add_option('-p', '--rho', action='store', dest='rho',
default=0.5, type='float', help='rho parameter')
options, args = parser.parse_args()
if not options.L:
parser.error("-L option is mandatory")
if not options.R:
parser.error("-R option is mandatory")
if not options.N:
parser.error("-N option is mandatory")
if not options.M:
parser.error("-M option is mandatory")
# initialization
L = options.L
R = options.R
N = options.N
max_iter = options.M
rate = options.rate
output = options.out
omega, omega_vec = make_omega(N)
num_funcs = len(omega)
num_vars = len(omega[0])
s = (num_funcs, num_vars, 2)
alpha = float(options.alpha)
x = np.ones(s)*omega_vec
z = np.random.random_sample(size=(num_vars, 2))+\
(L/2.)*np.ones((num_vars, 2))
zz = np.array([z]*num_funcs)*omega_vec
u = np.ones(s)*omega_vec
n = np.ones(s)*omega_vec
rho = float(options.rho)*omega_vec
# performing optimization
if not os.path.exists('.figs'):
os.makedirs('.figs')
os.system("rm -rf .figs/*")
imgpath = '.figs/fig%04d.png'
for k in range(max_iter):
n = zz - u
# proximal operator
for i in range(num_funcs):
if i < num_funcs - num_vars:
x[i] = nonoverlap(n[i], i, omega, R)
else:
x[i] = insidebox(n[i], i, omega, R, L)
m = x + u
z = np.sum(rho*m, axis=0)/np.sum(rho, axis=0)
zz = np.array([z]*num_funcs)*omega_vec
u = u + alpha*(x-zz)
if k == (max_iter-1):
make_graph(k, z, imgpath, R, L)
print "doing %i/%i" % (k, max_iter)
print "Generating animation '%s' ..." % (output)
os.system("ffmpeg -y -r %f -sameq -i %s %s > /dev/null 2>&1" % \
(rate, imgpath, output))
#os.system("rm -rf .figs/*")
#os.rmdir('.figs')
print "Done!"
print "Playing ..."
os.system("mplayer %s > /dev/null 2>&1" % output) | [
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11510,... | 2.097227 | 3,065 |
import os
import sys
import random
sys.path.append(os.path.join(os.environ['ALFRED_ROOT']))
sys.path.append(os.path.join(os.environ['ALFRED_ROOT'], 'models'))
import torch
import pprint
import json
from data.preprocess import Dataset
from importlib import import_module
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
from models.utils.helper_utils import optimizer_to
if __name__ == '__main__':
# parser
parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
# settings
parser.add_argument('--seed', help='random seed', default=123, type=int)
parser.add_argument('--data', help='dataset folder', default='data/json_feat_2.1.0')
parser.add_argument('--splits', help='json file containing train/dev/test splits', default='data/splits/may17.json')
parser.add_argument('--preprocess', help='store preprocessed data to json files', action='store_true')
parser.add_argument('--pp_folder', help='folder name for preprocessed data')
parser.add_argument('--object_vocab', help='object_vocab version, should be file with .object_vocab ending. default is none', default='none')
parser.add_argument('--save_every_epoch', help='save model after every epoch (warning: consumes a lot of space)', action='store_true')
parser.add_argument('--model', help='model to use', required=True)
parser.add_argument('--gpu', help='use gpu', action='store_true')
parser.add_argument('--dout', help='where to save model', default='exp/model:{model}')
parser.add_argument('--resume', help='load a checkpoint')
# hyper parameters
parser.add_argument('--batch', help='batch size', default=8, type=int)
parser.add_argument('--epoch', help='number of epochs', default=20, type=int)
parser.add_argument('--lr', help='optimizer learning rate', default=1e-4, type=float)
parser.add_argument('--decay_epoch', help='num epoch to adjust learning rate', default=10, type=int)
parser.add_argument('--dhid', help='hidden layer size', default=512, type=int)
parser.add_argument('--dframe', help='image feature vec size', default=2500, type=int)
parser.add_argument('--demb', help='language embedding size', default=100, type=int)
parser.add_argument('--pframe', help='image pixel size (assuming square shape eg: 300x300)', default=300, type=int)
parser.add_argument('--mask_loss_wt', help='weight of mask loss', default=1., type=float)
parser.add_argument('--action_loss_wt', help='weight of action loss', default=1., type=float)
parser.add_argument('--subgoal_aux_loss_wt', help='weight of subgoal completion predictor', default=0., type=float)
parser.add_argument('--pm_aux_loss_wt', help='weight of progress monitor', default=0., type=float)
# architecture ablations
parser.add_argument('--encoder_addons', type=str, default='none', choices=['none', 'max_pool_obj', 'biattn_obj'])
parser.add_argument('--decoder_addons', type=str, default='none', choices=['none', 'aux_loss'])
parser.add_argument('--object_repr', type=str, default='type', choices=['none', 'type', 'instance'])
parser.add_argument('--reweight_aux_bce', help='reweight binary CE for auxiliary tasks', action='store_true')
# target
parser.add_argument('--predict_goal_level_instruction', help='predict abstract single goal level instruction for entire task.', action='store_true')
# dropouts
parser.add_argument('--zero_goal', help='zero out goal language', action='store_true')
parser.add_argument('--zero_instr', help='zero out step-by-step instr language', action='store_true')
parser.add_argument('--act_dropout', help='dropout rate for action input sequence', default=0., type=float)
parser.add_argument('--lang_dropout', help='dropout rate for language (goal + instr)', default=0., type=float)
parser.add_argument('--input_dropout', help='dropout rate for concatted input feats', default=0., type=float)
parser.add_argument('--vis_dropout', help='dropout rate for Resnet feats', default=0.3, type=float)
parser.add_argument('--hstate_dropout', help='dropout rate for LSTM hidden states during unrolling', default=0.3, type=float)
parser.add_argument('--attn_dropout', help='dropout rate for attention', default=0., type=float)
parser.add_argument('--actor_dropout', help='dropout rate for actor fc', default=0., type=float)
parser.add_argument('--word_dropout', help='dropout rate for word fc', default=0., type=float)
# other settings
parser.add_argument('--train_teacher_forcing', help='use gpu', action='store_true')
parser.add_argument('--train_student_forcing_prob', help='bernoulli probability', default=0.1, type=float)
parser.add_argument('--temp_no_history', help='use gpu', action='store_true')
# debugging
parser.add_argument('--fast_epoch', help='fast epoch during debugging', action='store_true')
parser.add_argument('--dataset_fraction', help='use fraction of the dataset for debugging (0 indicates full size)', default=0, type=int)
# args and init
args = parser.parse_args()
args.dout = args.dout.format(**vars(args))
torch.manual_seed(args.seed)
# check if dataset has been preprocessed
if not os.path.exists(os.path.join(args.data, "%s.vocab" % args.pp_folder)) and not args.preprocess:
raise Exception("Dataset not processed; run with --preprocess")
# make output dir
pprint.pprint(args)
if not os.path.isdir(args.dout):
os.makedirs(args.dout)
# load train/valid/tests splits
with open(args.splits) as f:
splits = json.load(f)
# create sanity check split as a small sample of train set
if not 'train_sanity' in splits:
print('Creating train_sanity split. Will save an updated split file.')
splits['train_sanity'] = random.sample(splits['train'], k=len(splits['valid_seen']))
with open(args.splits, 'w') as f:
json.dump(splits, f)
pprint.pprint({k: len(v) for k, v in splits.items()})
# preprocess and save
if args.preprocess:
print("\nPreprocessing dataset and saving to %s folders ... This will take a while. Do this once as required." % args.pp_folder)
dataset = Dataset(args, None)
dataset.preprocess_splits(splits, args.pp_folder)
vocab = torch.load(os.path.join(args.dout, "%s.vocab" % args.pp_folder))
else:
vocab = torch.load(os.path.join(args.data, "%s.vocab" % args.pp_folder))
# load object vocab
if args.object_vocab != 'none':
object_vocab = torch.load(os.path.join(args.data, '%s' % args.object_vocab))
else:
object_vocab = None
# load model
M = import_module('model.{}'.format(args.model))
if args.resume:
print("Loading: " + args.resume)
model, optimizer, start_epoch, start_iters = M.Module.load(args.resume)
end_epoch = args.epoch
if start_epoch >= end_epoch:
print('Checkpoint already finished {}/{} epochs.'.format(start_epoch, end_epoch))
sys.exit(0)
else:
print("Restarting at epoch {}/{}".format(start_epoch, end_epoch-1))
else:
model = M.Module(args, vocab, object_vocab)
optimizer = None
start_epoch = 0
start_iters = None
end_epoch = args.epoch
# to gpu
if args.gpu:
model = model.to(torch.device('cuda'))
model.demo_mode = False
if not optimizer is None:
optimizer_to(optimizer, torch.device('cuda'))
# start train loop
model.run_train(splits, optimizer=optimizer, start_epoch=start_epoch, end_epoch=end_epoch, start_iters=start_iters) | [
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... | 2.757652 | 2,777 |
import pytest
from billy.utils.search import google_book_search
| [
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628
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import math
| [
11748,
10688,
628,
198
] | 3.5 | 4 |
import sys
from sys import exit
if len(sys.argv) == 1 :
print ("No command line argument" )
sys.exit()
#else :
# print ("rest of the program ")
#numbers = sys.argv[1:]
#print (sorted(numbers, key=lambda x: float(x)))
numbers = []
i=1
n= len(sys.argv)
while ( i < n ):
numbers.append(sys.argv[i])
i=i+1
# bubbleSort(numbers)
n = len(numbers)
# Traverse through all array elements
for i in range(n):
# Last i elements are already in place
for j in range(0, n-i-1):
# traverse the array from 0 to n-i-1
# Swap if the element found is greater
# than the next element
if numbers[j] > numbers[j+1] :
numbers[j], numbers[j+1] = numbers[j+1], numbers[j]
print(numbers)
| [
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1,
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17597,
13,
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198,
2,
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1058,
198,
2,
... | 2.239766 | 342 |
from . import dbFuncs
import sys, os
import pkg_resources
from PyQt5.QtCore import Qt
from PyQt5.QtWidgets import QApplication, qApp, QHBoxLayout, QMainWindow, QAction, QMessageBox, QFileDialog, QPushButton
from PyQt5.QtGui import QIcon
| [
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1195,
... | 2.755814 | 86 |
import os
import psutil
import time
| [
11748,
28686,
198,
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26692,
22602,
198,
11748,
640,
628
] | 3.7 | 10 |
# =================================================
# SERVER CONFIGURATIONS
# =================================================
CLIENT_ID=''
CLIENT_SECRET=''
REDIRECT_URI='http://ROCKOPY/'
# =================================================
# SERVER CONFIGURATIONS
# =================================================
SERVER_IP = "127.0.0.1"
SERVER_PORT = 5043
# =================================================
# OTHER OPTIONS
# =================================================
# how many track search results show:
TRACKS_TO_SEARCH = 5
| [
2,
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198,
2,
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4261,
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2,
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11639,
4023,
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11290,
3185,
56... | 4.610169 | 118 |
import discord
from discord.ext import commands
from discord.utils import get | [
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from soad import AsymmetricData as asyd
import matplotlib.pyplot as plt
# This script is prepared for showing the difference between methods of handling asymmetric errors.
if __name__ == "__main__":
Data.set_control_variable()
generate_multiple_variable()
Data.print_variables()
CompareMethods.calculate_sum()
#CompareMethods.calculate_mul()
CompareMethods.print_results()
CompareMethods.plot_results(save=True)
| [
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19482,
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628,... | 3.138889 | 144 |
import scipy.io as scio
import numpy as np
import matplotlib.pyplot as plt
import scipy.optimize as opt
from displayData import display_data
from costFunction import nn_cost_function
from sigmoid import sigmoid_gradient
from randInitializeWeights import rand_init_weights
from checkNNGradients import check_nn_gradients
from predict import predict_nn
# ==================== 1. ==============================
# scipy.iomatdata
data = scio.loadmat('ex4data1.mat')
#
# print(type(Y),type(X)) # XYnumpy.narray
X = data['X']
Y = data['y'].flatten()
# 100
m = X.shape[0]
# [0,m-1]
rand_indices = np.random.permutation(range(m))
selected = X[rand_indices[1:100],:]
#
display_data(selected)
# plt.show()
# ==================== 2. ==================================
weights = scio.loadmat('ex4weights.mat')
theta1 = weights['Theta1'] # 25*401
theta2 = weights['Theta2'] # 10*26
# theta1.flatten()theta1.reshape(theta1.size)
# nn_paramters.shape=(10285,)
nn_paramters = np.concatenate([theta1.flatten(),theta2.flatten()],axis =0)
#
input_layer = 400
hidden_layer = 25
out_layer = 10
#
lmd = 0
cost,grad = nn_cost_function(X,Y,nn_paramters,input_layer,hidden_layer,out_layer,lmd)
print('Cost at parameters (loaded from ex4weights): {:0.6f}\n(This value should be about 0.287629)'.format(cost))
#
lmd = 1
cost,grad = nn_cost_function(X,Y,nn_paramters,input_layer,hidden_layer,out_layer,lmd)
print('Cost at parameters (loaded from ex4weights): {:0.6f}\n(This value should be about 0.383770)'.format(cost))
# sigmoid
g = sigmoid_gradient(np.array([-1, -0.5, 0, 0.5, 1]))
print('Sigmoid gradient evaluated at [-1 -0.5 0 0.5 1]:\n{}'.format(g))
# =========================== 3. =================================
random_theta1 = rand_init_weights(input_layer,hidden_layer)
random_theta2 = rand_init_weights(hidden_layer,out_layer)
rand_nn_parameters = np.concatenate([random_theta1.flatten(),random_theta2.flatten()])
# BP
lmd =3
check_nn_gradients(lmd)
debug_cost, _ = nn_cost_function(X,Y,nn_paramters,input_layer,hidden_layer,out_layer,lmd)
print('Cost at (fixed) debugging parameters (w/ lambda = {}): {:0.6f}\n(for lambda = 3, this value should be about 0.576051)'.format(lmd, debug_cost))
# ========================== 4.NN ==========================================
lmd = 1
nn_params, *unused = opt.fmin_cg(cost_func, fprime=grad_func, x0=rand_nn_parameters, maxiter=400, disp=True, full_output=True)
# Obtain theta1 and theta2 back from nn_params
theta1 = nn_params[:hidden_layer * (input_layer + 1)].reshape(hidden_layer, input_layer + 1)
theta2 = nn_params[hidden_layer * (input_layer + 1):].reshape(out_layer, hidden_layer + 1)
# ======================= 5. ===================================
display_data(theta1[:, 1:])
plt.show()
pred = predict_nn(X,theta1, theta2)
print('Training set accuracy: {}'.format(np.mean(pred == Y)*100))
| [
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355,
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198,
198,
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6601,
1330,
3... | 2.735577 | 1,040 |
"""
This example shows some more complex querying
Key points are filtering by related names and using Q objects
"""
import asyncio
from tortoise import Tortoise, fields
from tortoise.models import Model
from tortoise.query_utils import Q
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(run())
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1330,
28467,
25678,
11,
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198,
673... | 3.405941 | 101 |
import struct
import socket
PACKET_HEADER = {
4: PacketIpv4Header,
}
| [
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316,
40,
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85,
19,
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198
] | 2.333333 | 33 |
from django.conf import settings
from django.core.files.storage import Storage
from django.utils.deconstruct import deconstructible
from fdfs_client.client import Fdfs_client
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856,
198,
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277,
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82,
62,
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13,
... | 3.591837 | 49 |
import gunicorn.app.base
from sovereign import asgi_config
from sovereign.app import app
if __name__ == '__main__':
main()
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13,
1324,
13,
8692,
198,
6738,
18901,
1330,
355,
12397,
62,
11250,
198,
6738,
18901,
13,
1324,
1330,
598,
628,
628,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
1388,
3419,... | 3.046512 | 43 |
Val = int(input('Digite o valor que voc quer sacar:'))
c50 = c20 = c10 = c1 = 0
if Val // 50 != 0:
c50 = Val // 50
Val = Val % 50
if Val // 20 != 0:
c20 = Val // 20
Val = Val % 20
if Val // 10 != 0:
c10 = Val // 10
Val = Val % 10
if Val // 1 != 0:
c1 = Val // 1
if c50 != 0:
print(f'{c50} Cdulas de R$50.00')
if c20 != 0:
print(f'{c20} Cdulas de R$20.00')
if c10 != 0:
print(f'{c10} Cdulas de R$10.00')
if c1 != 0:
print(f'{c1} Cdulas de R$1.00') | [
7762,
796,
493,
7,
15414,
10786,
19511,
578,
267,
1188,
273,
8358,
12776,
42517,
5360,
283,
32105,
4008,
198,
66,
1120,
796,
269,
1238,
796,
269,
940,
796,
269,
16,
796,
657,
198,
361,
3254,
3373,
2026,
14512,
657,
25,
198,
220,
220... | 1.983806 | 247 |
"""
Bazel macros for defining proto libraries.
"""
load("@rules_proto//proto:defs.bzl", "proto_library")
# TODO(#4096): Remove this once it's no longer needed.
def oppia_proto_library(name, **kwargs):
"""
Defines a new proto library.
Note that the library is defined with a stripped import prefix which ensures that protos have a
common import directory (which is needed since Gradle builds protos in the same directory
whereas Bazel doesn't by default). This common import directory is needed for cross-proto
textprotos to work correctly.
Args:
name: str. The name of the proto library.
**kwargs: additional parameters to pass into proto_library.
"""
proto_library(
name = name,
strip_import_prefix = "",
**kwargs
)
| [
37811,
198,
33,
41319,
34749,
329,
16215,
44876,
12782,
13,
198,
37811,
198,
198,
2220,
7203,
31,
38785,
62,
1676,
1462,
1003,
1676,
1462,
25,
4299,
82,
13,
65,
48274,
1600,
366,
1676,
1462,
62,
32016,
4943,
198,
198,
2,
16926,
46,
... | 2.966667 | 270 |
import os
from remotepixel import cbers_ndvi
CBERS_SCENE = "CBERS_4_MUX_20171121_057_094_L2"
CBERS_BUCKET = os.path.join(os.path.dirname(__file__), "fixtures", "cbers-pds")
CBERS_PATH = os.path.join(
CBERS_BUCKET, "CBERS4/MUX/057/094/CBERS_4_MUX_20171121_057_094_L2/"
)
def test_point_valid(monkeypatch):
"""Should work as expected (read data, calculate NDVI and return json info)."""
monkeypatch.setattr(cbers_ndvi, "CBERS_BUCKET", CBERS_BUCKET)
expression = "(b8 - b7) / (b8 + b7)"
coords = [53.9097, 5.3674]
expectedContent = {
"date": "2017-11-21",
"scene": CBERS_SCENE,
"ndvi": -0.1320754716981132,
}
assert cbers_ndvi.point(CBERS_SCENE, coords, expression) == expectedContent
def test_point_invalid(monkeypatch):
"""Should work as expected and retour 0 for outside point."""
monkeypatch.setattr(cbers_ndvi, "CBERS_BUCKET", CBERS_BUCKET)
expression = "(b8 - b7) / (b8 + b7)"
coords = [53.9097, 2.3674]
expectedContent = {"date": "2017-11-21", "scene": CBERS_SCENE, "ndvi": 0.}
assert cbers_ndvi.point(CBERS_SCENE, coords, expression) == expectedContent
def test_area_valid(monkeypatch):
"""Should work as expected (read data, calculate NDVI and return img)."""
monkeypatch.setattr(cbers_ndvi, "CBERS_BUCKET", CBERS_BUCKET)
expression = "(b8 - b7) / (b8 + b7)"
bbox = [53.0859375, 5.266007882805496, 53.4375, 5.615985819155334]
res = cbers_ndvi.area(CBERS_SCENE, bbox, expression)
assert res["date"] == "2017-11-21"
| [
198,
11748,
28686,
198,
198,
6738,
816,
313,
538,
7168,
1330,
269,
1213,
62,
358,
8903,
198,
198,
23199,
4877,
62,
6173,
39267,
796,
366,
23199,
4877,
62,
19,
62,
44,
31235,
62,
1264,
4869,
19244,
62,
43526,
62,
2931,
19,
62,
43,
... | 2.29491 | 668 |
#!/usr/bin/env python
#
# __COPYRIGHT__
#
# Permission is hereby granted, free of charge, to any person obtaining
# a copy of this software and associated documentation files (the
# "Software"), to deal in the Software without restriction, including
# without limitation the rights to use, copy, modify, merge, publish,
# distribute, sublicense, and/or sell copies of the Software, and to
# permit persons to whom the Software is furnished to do so, subject to
# the following conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY
# KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
# WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
#
__revision__ = "__FILE__ __REVISION__ __DATE__ __DEVELOPER__"
"""
Verify that the time subcommand's --which option doesn't fail, and prints
an appropriate error message, if a log file doesn't have its specific
requested results.
"""
import TestSCons_time
test = TestSCons_time.TestSCons_time()
header = """\
set key bottom left
plot '-' title "Startup" with lines lt 1
# Startup
"""
footer = """\
e
"""
line_fmt = "%s 11.123456\n"
lines = []
for i in range(9):
logfile_name = 'foo-%s-0.log' % i
if i == 5:
test.write(test.workpath(logfile_name), "NO RESULTS HERE!\n")
else:
test.fake_logfile(logfile_name)
lines.append(line_fmt % i)
expect = [header] + lines + [footer]
stderr = "file 'foo-5-0.log' has no results!\n"
test.run(arguments = 'time --fmt gnuplot --which total foo*.log',
stdout = ''.join(expect),
stderr = stderr)
expect = [header] + [footer]
test.run(arguments = 'time --fmt gnuplot foo-5-0.log',
stdout = ''.join(expect),
stderr = stderr)
test.pass_test()
# Local Variables:
# tab-width:4
# indent-tabs-mode:nil
# End:
# vim: set expandtab tabstop=4 shiftwidth=4:
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
2,
198,
2,
11593,
34,
3185,
38162,
9947,
834,
198,
2,
198,
2,
2448,
3411,
318,
29376,
7520,
11,
1479,
286,
3877,
11,
284,
597,
1048,
16727,
198,
2,
257,
4866,
286,
428,
3788,
290,
... | 2.873418 | 790 |
"""
The container to store indexes in active learning.
Serve as the basic type of 'set' operation.
"""
# Authors: Ying-Peng Tang
# License: BSD 3 clause
from __future__ import division
import collections
import copy
import numpy as np
from .multi_label_tools import check_index_multilabel, infer_label_size_multilabel, flattern_multilabel_index, \
integrate_multilabel_index
from ..utils.ace_warnings import *
from ..utils.interface import BaseCollection
from ..utils.misc import randperm
def map_whole_index_to_train(train_idx, index_in_whole):
"""Map the indexes from whole dataset to training set.
Parameters
----------
train_idx: {list, numpy.ndarray}
The training indexes.
index_in_whole: {IndexCollection, MultiLabelIndexCollection}
The indexes need to be mapped of the whole data.
Returns
-------
index_in_train: {IndexCollection, MultiLabelIndexCollection}
The mapped indexes.
Examples
--------
>>> train_idx = [231, 333, 423]
>>> index_in_whole = IndexCollection([333, 423])
>>> print(map_whole_index_to_train(train_idx, index_in_whole))
[1, 2]
"""
if isinstance(index_in_whole, MultiLabelIndexCollection):
ind_type = 2
elif isinstance(index_in_whole, IndexCollection):
ind_type = 1
else:
raise TypeError("index_in_whole must be one of {IndexCollection, MultiLabelIndexCollection} type.")
tr_ob = []
for entry in index_in_whole:
if ind_type == 2:
assert entry[0] in train_idx
ind_in_train = np.argwhere(train_idx == entry[0])[0][0]
tr_ob.append((ind_in_train, entry[1]))
else:
assert entry in train_idx
tr_ob.append(np.argwhere(train_idx == entry)[0][0])
if ind_type == 2:
return MultiLabelIndexCollection(tr_ob)
else:
return IndexCollection(tr_ob)
| [
37811,
198,
464,
9290,
284,
3650,
39199,
287,
4075,
4673,
13,
198,
50,
3760,
355,
262,
4096,
2099,
286,
705,
2617,
6,
4905,
13,
198,
37811,
198,
2,
46665,
25,
38833,
12,
47,
1516,
18816,
198,
2,
13789,
25,
347,
10305,
513,
13444,
... | 2.566038 | 742 |
import platform as p
import uuid
import hashlib
| [
11748,
3859,
355,
279,
198,
11748,
334,
27112,
198,
11748,
12234,
8019,
198
] | 3.692308 | 13 |
url = "https://www.delish.com/cooking/recipe-ideas/recipes/a53823/easy-pad-thai-recipe/"
url2 = "https://www.allrecipes.com/recipe/92462/slow-cooker-texas-pulled-pork/"
# opener = urllib.URLopener()
# opener.addheader(('User-Agent', 'Mozilla/5.0'))
# f = urllib.urlopen(url)
import requests
import html2text
h = html2text.HTML2Text()
h.ignore_links = True
f = requests.get(url2)
g = h.handle(f.text)
arrayOflines = g.split("\n")
isPrinting = False
chunk = []
chunks = []
for line in arrayOflines:
if(len(line) != 0):
chunk.append(line)
else:
chunks.append(chunk)
chunk = []
print(chunks)
for c in chunks:
print(c)
print("\n \n")
# if 'ingredients' in line.lower() and len(line) < 15:
# print(line)
# if "ingredients" in line and len(line) < :
# print(len(line))
# isPrinting = True
# if(isPrinting):
# print(line)
# if(len(line) == 0):
# isPrinting = False
# print(arrayOflines)
| [
6371,
796,
366,
5450,
1378,
2503,
13,
12381,
680,
13,
785,
14,
27916,
278,
14,
29102,
431,
12,
485,
292,
14,
8344,
18636,
14,
64,
49561,
1954,
14,
38171,
12,
15636,
12,
400,
1872,
12,
29102,
431,
30487,
198,
6371,
17,
796,
366,
54... | 2.167756 | 459 |
#!/usr/bin/env python
from __future__ import print_function
import inspect
import logging
import os
import platform
import sys
from time import sleep
from flaky import flaky
import pytest
import requests
from jira_test_manager import JiraTestManager
# _non_parallel is used to prevent some tests from failing due to concurrency
# issues because detox, Travis or Jenkins can run test in parallel for multiple
# python versions.
# The current workaround is to run these problematic tests only on py27
_non_parallel = True
if platform.python_version() < '3':
_non_parallel = False
try:
import unittest2 as unittest
except ImportError:
import pip
if hasattr(sys, 'real_prefix'):
pip.main(['install', '--upgrade', 'unittest2'])
else:
pip.main(['install', '--upgrade', '--user', 'unittest2'])
import unittest2 as unittest
else:
import unittest
cmd_folder = os.path.abspath(os.path.join(os.path.split(inspect.getfile(
inspect.currentframe()))[0], ".."))
if cmd_folder not in sys.path:
sys.path.insert(0, cmd_folder)
import jira # noqa
from jira import Role, Issue, JIRA, JIRAError, Project # noqa
from jira.resources import Resource, cls_for_resource # noqa
TEST_ROOT = os.path.dirname(__file__)
TEST_ICON_PATH = os.path.join(TEST_ROOT, 'icon.png')
TEST_ATTACH_PATH = os.path.join(TEST_ROOT, 'tests.py')
OAUTH = False
CONSUMER_KEY = 'oauth-consumer'
KEY_CERT_FILE = '/home/bspeakmon/src/atlassian-oauth-examples/rsa.pem'
KEY_CERT_DATA = None
try:
with open(KEY_CERT_FILE, 'r') as cert:
KEY_CERT_DATA = cert.read()
OAUTH = True
except Exception:
pass
if 'CI_JIRA_URL' in os.environ:
not_on_custom_jira_instance = pytest.mark.skipif(True, reason="Not applicable for custom JIRA instance")
logging.info('Picked up custom JIRA engine.')
else:
not_on_custom_jira_instance = noop
jira_servicedesk = pytest.mark.skipif(jira_servicedesk_detection(), reason="JIRA Service Desk is not available.")
if __name__ == '__main__':
# when running tests we expect various errors and we don't want to display them by default
logging.getLogger("requests").setLevel(logging.FATAL)
logging.getLogger("urllib3").setLevel(logging.FATAL)
logging.getLogger("jira").setLevel(logging.FATAL)
# j = JIRA("https://issues.citrite.net")
# print(j.session())
dirname = "test-reports-%s%s" % (sys.version_info[0], sys.version_info[1])
unittest.main()
# pass
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
6738,
11593,
37443,
834,
1330,
3601,
62,
8818,
198,
11748,
10104,
198,
11748,
18931,
198,
11748,
28686,
198,
11748,
3859,
198,
11748,
25064,
198,
6738,
640,
1330,
3993,
198,
198,
6738,
7... | 2.602711 | 959 |
#!/usr/bin/env python
# Goofile v1.5a
# by Thomas (G13) Richards
# www.g13net.com
# Project Page: code.google.com/p/goofile
# TheHarvester used for inspiration
# A many thanks to the Edge-Security team!
# Modified by Lee Baird
import getopt
import httplib
import re
import string
import sys
global result
result =[]
cant = 0
while cant < limit:
res = run(domain,file)
for x in res:
if result.count(x) == 0:
result.append(x)
cant+=100
if result==[]:
print "No results were found."
else:
for x in result:
print x
if __name__ == "__main__":
try: search(sys.argv[1:])
except KeyboardInterrupt:
print "Search interrupted by user."
except:
sys.exit()
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
198,
2,
1514,
1659,
576,
410,
16,
13,
20,
64,
198,
2,
416,
5658,
357,
38,
1485,
8,
14743,
198,
2,
7324,
13,
70,
1485,
3262,
13,
785,
198,
2,
4935,
7873,
25,
2438,
13,
13297,
13... | 2.487719 | 285 |
n = int(input('Insira um nmero e calcule sua raiz: '))
b = 2
while True:
p = (b + (n / b)) / 2
res = p ** 2
b = p
if abs(n - res) < 0.0001:
break
print(f'p = {p}')
print(f'p = {res}')
| [
77,
796,
493,
7,
15414,
10786,
20376,
8704,
23781,
299,
647,
78,
304,
2386,
23172,
424,
64,
2179,
528,
25,
705,
4008,
198,
65,
796,
362,
198,
4514,
6407,
25,
198,
220,
220,
220,
279,
796,
357,
65,
1343,
357,
77,
1220,
275,
4008,
... | 1.882883 | 111 |
# -*- coding: utf-8 -*-
from __future__ import absolute_import, print_function, unicode_literals
import datetime
import decimal
import platform
import sys
import types
from itertools import chain
#stripped version of SIX
PY2 = sys.version_info[0] == 2
PY3 = sys.version_info[0] == 3
PY_35 = sys.version_info >= (3, 5)
PY_36 = sys.version_info >= (3, 6)
PY_37 = sys.version_info >= (3, 7)
WINDOWS = platform.system() == 'Windows'
LINUX = platform.system() == 'Linux'
MACOS = platform.system() == 'Darwin'
JYTHON = sys.platform.startswith('java')
if PY3:
string_types = str,
integer_types = int,
class_types = type,
text_type = str
binary_type = bytes
none_type = type(None)
import io
StringIO = io.StringIO
BytesIO = io.BytesIO
memoryview = memoryview
buffer_types = (bytes, bytearray, memoryview)
else:
string_types = basestring,
integer_types = (int, long)
class_types = (type, types.ClassType)
text_type = unicode
binary_type = str
none_type = types.NoneType
import StringIO
StringIO = BytesIO = StringIO.StringIO
# memoryview and buffer are not strictly equivalent, but should be fine for
# django core usage (mainly BinaryField). However, Jython doesn't support
# buffer (see http://bugs.jython.org/issue1521), so we have to be careful.
if JYTHON:
memoryview = memoryview
else:
memoryview = buffer
buffer_types = (bytearray, memoryview, buffer)
iterable_types = (list, tuple, set, frozenset, types.GeneratorType, chain)
protected_types = tuple(
chain(string_types, integer_types,
(float, decimal.Decimal, datetime.date, datetime.datetime,
datetime.time, bool, none_type)))
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
198,
6738,
11593,
37443,
834,
1330,
4112,
62,
11748,
11,
3601,
62,
8818,
11,
28000,
1098,
62,
17201,
874,
198,
198,
11748,
4818,
8079,
198,
11748,
32465,
198,
11748,
385... | 2.648318 | 654 |
# coding=utf-8
# pynput
# Copyright (C) 2015-2016 Moses Palmr
#
# This program is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the Free
# Software Foundation, either version 3 of the License, or (at your option) any
# later version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
# details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import enum
import Xlib.display
import Xlib.ext
import Xlib.ext.xtest
import Xlib.X
import Xlib.protocol
from pynput._util.xorg import *
from . import _base
| [
2,
19617,
28,
40477,
12,
23,
198,
2,
279,
2047,
1996,
198,
2,
15069,
357,
34,
8,
1853,
12,
5304,
19010,
18358,
81,
198,
2,
198,
2,
770,
1430,
318,
1479,
3788,
25,
345,
460,
17678,
4163,
340,
290,
14,
273,
13096,
340,
739,
198,
... | 3.53012 | 249 |
allData = {'AK': {'Aleutians East': {'pop': 3141, 'tracts': 1},
'Aleutians West': {'pop': 5561, 'tracts': 2},
'Anchorage': {'pop': 291826, 'tracts': 55},
'Bethel': {'pop': 17013, 'tracts': 3},
'Bristol Bay': {'pop': 997, 'tracts': 1},
'Denali': {'pop': 1826, 'tracts': 1},
'Dillingham': {'pop': 4847, 'tracts': 2},
'Fairbanks North Star': {'pop': 97581, 'tracts': 19},
'Haines': {'pop': 2508, 'tracts': 1},
'Hoonah-Angoon': {'pop': 2150, 'tracts': 2},
'Juneau': {'pop': 31275, 'tracts': 6},
'Kenai Peninsula': {'pop': 55400, 'tracts': 13},
'Ketchikan Gateway': {'pop': 13477, 'tracts': 4},
'Kodiak Island': {'pop': 13592, 'tracts': 5},
'Lake and Peninsula': {'pop': 1631, 'tracts': 1},
'Matanuska-Susitna': {'pop': 88995, 'tracts': 24},
'Nome': {'pop': 9492, 'tracts': 2},
'North Slope': {'pop': 9430, 'tracts': 3},
'Northwest Arctic': {'pop': 7523, 'tracts': 2},
'Petersburg': {'pop': 3815, 'tracts': 1},
'Prince of Wales-Hyder': {'pop': 5559, 'tracts': 4},
'Sitka': {'pop': 8881, 'tracts': 2},
'Skagway': {'pop': 968, 'tracts': 1},
'Southeast Fairbanks': {'pop': 7029, 'tracts': 2},
'Valdez-Cordova': {'pop': 9636, 'tracts': 3},
'Wade Hampton': {'pop': 7459, 'tracts': 1},
'Wrangell': {'pop': 2369, 'tracts': 1},
'Yakutat': {'pop': 662, 'tracts': 1},
'Yukon-Koyukuk': {'pop': 5588, 'tracts': 4}},
'AL': {'Autauga': {'pop': 54571, 'tracts': 12},
'Baldwin': {'pop': 182265, 'tracts': 31},
'Barbour': {'pop': 27457, 'tracts': 9},
'Bibb': {'pop': 22915, 'tracts': 4},
'Blount': {'pop': 57322, 'tracts': 9},
'Bullock': {'pop': 10914, 'tracts': 3},
'Butler': {'pop': 20947, 'tracts': 9},
'Calhoun': {'pop': 118572, 'tracts': 31},
'Chambers': {'pop': 34215, 'tracts': 9},
'Cherokee': {'pop': 25989, 'tracts': 6},
'Chilton': {'pop': 43643, 'tracts': 9},
'Choctaw': {'pop': 13859, 'tracts': 4},
'Clarke': {'pop': 25833, 'tracts': 9},
'Clay': {'pop': 13932, 'tracts': 4},
'Cleburne': {'pop': 14972, 'tracts': 4},
'Coffee': {'pop': 49948, 'tracts': 14},
'Colbert': {'pop': 54428, 'tracts': 14},
'Conecuh': {'pop': 13228, 'tracts': 5},
'Coosa': {'pop': 11539, 'tracts': 3},
'Covington': {'pop': 37765, 'tracts': 14},
'Crenshaw': {'pop': 13906, 'tracts': 6},
'Cullman': {'pop': 80406, 'tracts': 18},
'Dale': {'pop': 50251, 'tracts': 14},
'Dallas': {'pop': 43820, 'tracts': 15},
'DeKalb': {'pop': 71109, 'tracts': 14},
'Elmore': {'pop': 79303, 'tracts': 15},
'Escambia': {'pop': 38319, 'tracts': 9},
'Etowah': {'pop': 104430, 'tracts': 30},
'Fayette': {'pop': 17241, 'tracts': 5},
'Franklin': {'pop': 31704, 'tracts': 9},
'Geneva': {'pop': 26790, 'tracts': 6},
'Greene': {'pop': 9045, 'tracts': 3},
'Hale': {'pop': 15760, 'tracts': 6},
'Henry': {'pop': 17302, 'tracts': 6},
'Houston': {'pop': 101547, 'tracts': 22},
'Jackson': {'pop': 53227, 'tracts': 11},
'Jefferson': {'pop': 658466, 'tracts': 163},
'Lamar': {'pop': 14564, 'tracts': 3},
'Lauderdale': {'pop': 92709, 'tracts': 22},
'Lawrence': {'pop': 34339, 'tracts': 9},
'Lee': {'pop': 140247, 'tracts': 27},
'Limestone': {'pop': 82782, 'tracts': 16},
'Lowndes': {'pop': 11299, 'tracts': 4},
'Macon': {'pop': 21452, 'tracts': 12},
'Madison': {'pop': 334811, 'tracts': 73},
'Marengo': {'pop': 21027, 'tracts': 6},
'Marion': {'pop': 30776, 'tracts': 8},
'Marshall': {'pop': 93019, 'tracts': 18},
'Mobile': {'pop': 412992, 'tracts': 114},
'Monroe': {'pop': 23068, 'tracts': 7},
'Montgomery': {'pop': 229363, 'tracts': 65},
'Morgan': {'pop': 119490, 'tracts': 27},
'Perry': {'pop': 10591, 'tracts': 3},
'Pickens': {'pop': 19746, 'tracts': 5},
'Pike': {'pop': 32899, 'tracts': 8},
'Randolph': {'pop': 22913, 'tracts': 6},
'Russell': {'pop': 52947, 'tracts': 13},
'Shelby': {'pop': 195085, 'tracts': 48},
'St. Clair': {'pop': 83593, 'tracts': 13},
'Sumter': {'pop': 13763, 'tracts': 4},
'Talladega': {'pop': 82291, 'tracts': 22},
'Tallapoosa': {'pop': 41616, 'tracts': 10},
'Tuscaloosa': {'pop': 194656, 'tracts': 47},
'Walker': {'pop': 67023, 'tracts': 18},
'Washington': {'pop': 17581, 'tracts': 5},
'Wilcox': {'pop': 11670, 'tracts': 4},
'Winston': {'pop': 24484, 'tracts': 7}},
'AR': {'Arkansas': {'pop': 19019, 'tracts': 8},
'Ashley': {'pop': 21853, 'tracts': 7},
'Baxter': {'pop': 41513, 'tracts': 9},
'Benton': {'pop': 221339, 'tracts': 49},
'Boone': {'pop': 36903, 'tracts': 7},
'Bradley': {'pop': 11508, 'tracts': 5},
'Calhoun': {'pop': 5368, 'tracts': 2},
'Carroll': {'pop': 27446, 'tracts': 5},
'Chicot': {'pop': 11800, 'tracts': 4},
'Clark': {'pop': 22995, 'tracts': 5},
'Clay': {'pop': 16083, 'tracts': 6},
'Cleburne': {'pop': 25970, 'tracts': 7},
'Cleveland': {'pop': 8689, 'tracts': 2},
'Columbia': {'pop': 24552, 'tracts': 5},
'Conway': {'pop': 21273, 'tracts': 6},
'Craighead': {'pop': 96443, 'tracts': 17},
'Crawford': {'pop': 61948, 'tracts': 11},
'Crittenden': {'pop': 50902, 'tracts': 20},
'Cross': {'pop': 17870, 'tracts': 6},
'Dallas': {'pop': 8116, 'tracts': 3},
'Desha': {'pop': 13008, 'tracts': 5},
'Drew': {'pop': 18509, 'tracts': 5},
'Faulkner': {'pop': 113237, 'tracts': 25},
'Franklin': {'pop': 18125, 'tracts': 3},
'Fulton': {'pop': 12245, 'tracts': 2},
'Garland': {'pop': 96024, 'tracts': 20},
'Grant': {'pop': 17853, 'tracts': 4},
'Greene': {'pop': 42090, 'tracts': 9},
'Hempstead': {'pop': 22609, 'tracts': 5},
'Hot Spring': {'pop': 32923, 'tracts': 7},
'Howard': {'pop': 13789, 'tracts': 3},
'Independence': {'pop': 36647, 'tracts': 8},
'Izard': {'pop': 13696, 'tracts': 4},
'Jackson': {'pop': 17997, 'tracts': 5},
'Jefferson': {'pop': 77435, 'tracts': 24},
'Johnson': {'pop': 25540, 'tracts': 6},
'Lafayette': {'pop': 7645, 'tracts': 2},
'Lawrence': {'pop': 17415, 'tracts': 6},
'Lee': {'pop': 10424, 'tracts': 4},
'Lincoln': {'pop': 14134, 'tracts': 4},
'Little River': {'pop': 13171, 'tracts': 4},
'Logan': {'pop': 22353, 'tracts': 6},
'Lonoke': {'pop': 68356, 'tracts': 16},
'Madison': {'pop': 15717, 'tracts': 4},
'Marion': {'pop': 16653, 'tracts': 4},
'Miller': {'pop': 43462, 'tracts': 12},
'Mississippi': {'pop': 46480, 'tracts': 12},
'Monroe': {'pop': 8149, 'tracts': 3},
'Montgomery': {'pop': 9487, 'tracts': 3},
'Nevada': {'pop': 8997, 'tracts': 3},
'Newton': {'pop': 8330, 'tracts': 2},
'Ouachita': {'pop': 26120, 'tracts': 6},
'Perry': {'pop': 10445, 'tracts': 3},
'Phillips': {'pop': 21757, 'tracts': 6},
'Pike': {'pop': 11291, 'tracts': 3},
'Poinsett': {'pop': 24583, 'tracts': 7},
'Polk': {'pop': 20662, 'tracts': 6},
'Pope': {'pop': 61754, 'tracts': 11},
'Prairie': {'pop': 8715, 'tracts': 3},
'Pulaski': {'pop': 382748, 'tracts': 95},
'Randolph': {'pop': 17969, 'tracts': 4},
'Saline': {'pop': 107118, 'tracts': 21},
'Scott': {'pop': 11233, 'tracts': 3},
'Searcy': {'pop': 8195, 'tracts': 3},
'Sebastian': {'pop': 125744, 'tracts': 26},
'Sevier': {'pop': 17058, 'tracts': 4},
'Sharp': {'pop': 17264, 'tracts': 4},
'St. Francis': {'pop': 28258, 'tracts': 6},
'Stone': {'pop': 12394, 'tracts': 3},
'Union': {'pop': 41639, 'tracts': 10},
'Van Buren': {'pop': 17295, 'tracts': 5},
'Washington': {'pop': 203065, 'tracts': 32},
'White': {'pop': 77076, 'tracts': 13},
'Woodruff': {'pop': 7260, 'tracts': 2},
'Yell': {'pop': 22185, 'tracts': 6}},
'AZ': {'Apache': {'pop': 71518, 'tracts': 16},
'Cochise': {'pop': 131346, 'tracts': 32},
'Coconino': {'pop': 134421, 'tracts': 28},
'Gila': {'pop': 53597, 'tracts': 16},
'Graham': {'pop': 37220, 'tracts': 9},
'Greenlee': {'pop': 8437, 'tracts': 3},
'La Paz': {'pop': 20489, 'tracts': 9},
'Maricopa': {'pop': 3817117, 'tracts': 916},
'Mohave': {'pop': 200186, 'tracts': 43},
'Navajo': {'pop': 107449, 'tracts': 31},
'Pima': {'pop': 980263, 'tracts': 241},
'Pinal': {'pop': 375770, 'tracts': 75},
'Santa Cruz': {'pop': 47420, 'tracts': 10},
'Yavapai': {'pop': 211033, 'tracts': 42},
'Yuma': {'pop': 195751, 'tracts': 55}},
'CA': {'Alameda': {'pop': 1510271, 'tracts': 360},
'Alpine': {'pop': 1175, 'tracts': 1},
'Amador': {'pop': 38091, 'tracts': 9},
'Butte': {'pop': 220000, 'tracts': 51},
'Calaveras': {'pop': 45578, 'tracts': 10},
'Colusa': {'pop': 21419, 'tracts': 5},
'Contra Costa': {'pop': 1049025, 'tracts': 208},
'Del Norte': {'pop': 28610, 'tracts': 7},
'El Dorado': {'pop': 181058, 'tracts': 43},
'Fresno': {'pop': 930450, 'tracts': 199},
'Glenn': {'pop': 28122, 'tracts': 6},
'Humboldt': {'pop': 134623, 'tracts': 30},
'Imperial': {'pop': 174528, 'tracts': 31},
'Inyo': {'pop': 18546, 'tracts': 6},
'Kern': {'pop': 839631, 'tracts': 151},
'Kings': {'pop': 152982, 'tracts': 27},
'Lake': {'pop': 64665, 'tracts': 15},
'Lassen': {'pop': 34895, 'tracts': 9},
'Los Angeles': {'pop': 9818605, 'tracts': 2343},
'Madera': {'pop': 150865, 'tracts': 23},
'Marin': {'pop': 252409, 'tracts': 55},
'Mariposa': {'pop': 18251, 'tracts': 6},
'Mendocino': {'pop': 87841, 'tracts': 20},
'Merced': {'pop': 255793, 'tracts': 49},
'Modoc': {'pop': 9686, 'tracts': 4},
'Mono': {'pop': 14202, 'tracts': 3},
'Monterey': {'pop': 415057, 'tracts': 93},
'Napa': {'pop': 136484, 'tracts': 40},
'Nevada': {'pop': 98764, 'tracts': 20},
'Orange': {'pop': 3010232, 'tracts': 583},
'Placer': {'pop': 348432, 'tracts': 85},
'Plumas': {'pop': 20007, 'tracts': 7},
'Riverside': {'pop': 2189641, 'tracts': 453},
'Sacramento': {'pop': 1418788, 'tracts': 317},
'San Benito': {'pop': 55269, 'tracts': 11},
'San Bernardino': {'pop': 2035210, 'tracts': 369},
'San Diego': {'pop': 3095313, 'tracts': 628},
'San Francisco': {'pop': 805235, 'tracts': 196},
'San Joaquin': {'pop': 685306, 'tracts': 139},
'San Luis Obispo': {'pop': 269637, 'tracts': 53},
'San Mateo': {'pop': 718451, 'tracts': 158},
'Santa Barbara': {'pop': 423895, 'tracts': 90},
'Santa Clara': {'pop': 1781642, 'tracts': 372},
'Santa Cruz': {'pop': 262382, 'tracts': 52},
'Shasta': {'pop': 177223, 'tracts': 48},
'Sierra': {'pop': 3240, 'tracts': 1},
'Siskiyou': {'pop': 44900, 'tracts': 14},
'Solano': {'pop': 413344, 'tracts': 96},
'Sonoma': {'pop': 483878, 'tracts': 99},
'Stanislaus': {'pop': 514453, 'tracts': 94},
'Sutter': {'pop': 94737, 'tracts': 21},
'Tehama': {'pop': 63463, 'tracts': 11},
'Trinity': {'pop': 13786, 'tracts': 5},
'Tulare': {'pop': 442179, 'tracts': 78},
'Tuolumne': {'pop': 55365, 'tracts': 11},
'Ventura': {'pop': 823318, 'tracts': 174},
'Yolo': {'pop': 200849, 'tracts': 41},
'Yuba': {'pop': 72155, 'tracts': 14}},
'CO': {'Adams': {'pop': 441603, 'tracts': 97},
'Alamosa': {'pop': 15445, 'tracts': 4},
'Arapahoe': {'pop': 572003, 'tracts': 147},
'Archuleta': {'pop': 12084, 'tracts': 4},
'Baca': {'pop': 3788, 'tracts': 2},
'Bent': {'pop': 6499, 'tracts': 1},
'Boulder': {'pop': 294567, 'tracts': 68},
'Broomfield': {'pop': 55889, 'tracts': 18},
'Chaffee': {'pop': 17809, 'tracts': 5},
'Cheyenne': {'pop': 1836, 'tracts': 1},
'Clear Creek': {'pop': 9088, 'tracts': 3},
'Conejos': {'pop': 8256, 'tracts': 2},
'Costilla': {'pop': 3524, 'tracts': 2},
'Crowley': {'pop': 5823, 'tracts': 1},
'Custer': {'pop': 4255, 'tracts': 1},
'Delta': {'pop': 30952, 'tracts': 7},
'Denver': {'pop': 600158, 'tracts': 144},
'Dolores': {'pop': 2064, 'tracts': 1},
'Douglas': {'pop': 285465, 'tracts': 61},
'Eagle': {'pop': 52197, 'tracts': 14},
'El Paso': {'pop': 622263, 'tracts': 130},
'Elbert': {'pop': 23086, 'tracts': 7},
'Fremont': {'pop': 46824, 'tracts': 14},
'Garfield': {'pop': 56389, 'tracts': 11},
'Gilpin': {'pop': 5441, 'tracts': 1},
'Grand': {'pop': 14843, 'tracts': 3},
'Gunnison': {'pop': 15324, 'tracts': 4},
'Hinsdale': {'pop': 843, 'tracts': 1},
'Huerfano': {'pop': 6711, 'tracts': 2},
'Jackson': {'pop': 1394, 'tracts': 1},
'Jefferson': {'pop': 534543, 'tracts': 138},
'Kiowa': {'pop': 1398, 'tracts': 1},
'Kit Carson': {'pop': 8270, 'tracts': 3},
'La Plata': {'pop': 51334, 'tracts': 10},
'Lake': {'pop': 7310, 'tracts': 2},
'Larimer': {'pop': 299630, 'tracts': 73},
'Las Animas': {'pop': 15507, 'tracts': 6},
'Lincoln': {'pop': 5467, 'tracts': 2},
'Logan': {'pop': 22709, 'tracts': 6},
'Mesa': {'pop': 146723, 'tracts': 29},
'Mineral': {'pop': 712, 'tracts': 1},
'Moffat': {'pop': 13795, 'tracts': 4},
'Montezuma': {'pop': 25535, 'tracts': 7},
'Montrose': {'pop': 41276, 'tracts': 10},
'Morgan': {'pop': 28159, 'tracts': 8},
'Otero': {'pop': 18831, 'tracts': 7},
'Ouray': {'pop': 4436, 'tracts': 1},
'Park': {'pop': 16206, 'tracts': 5},
'Phillips': {'pop': 4442, 'tracts': 2},
'Pitkin': {'pop': 17148, 'tracts': 4},
'Prowers': {'pop': 12551, 'tracts': 5},
'Pueblo': {'pop': 159063, 'tracts': 55},
'Rio Blanco': {'pop': 6666, 'tracts': 2},
'Rio Grande': {'pop': 11982, 'tracts': 3},
'Routt': {'pop': 23509, 'tracts': 8},
'Saguache': {'pop': 6108, 'tracts': 2},
'San Juan': {'pop': 699, 'tracts': 1},
'San Miguel': {'pop': 7359, 'tracts': 4},
'Sedgwick': {'pop': 2379, 'tracts': 1},
'Summit': {'pop': 27994, 'tracts': 5},
'Teller': {'pop': 23350, 'tracts': 6},
'Washington': {'pop': 4814, 'tracts': 2},
'Weld': {'pop': 252825, 'tracts': 77},
'Yuma': {'pop': 10043, 'tracts': 2}},
'CT': {'Fairfield': {'pop': 916829, 'tracts': 211},
'Hartford': {'pop': 894014, 'tracts': 224},
'Litchfield': {'pop': 189927, 'tracts': 51},
'Middlesex': {'pop': 165676, 'tracts': 36},
'New Haven': {'pop': 862477, 'tracts': 190},
'New London': {'pop': 274055, 'tracts': 66},
'Tolland': {'pop': 152691, 'tracts': 29},
'Windham': {'pop': 118428, 'tracts': 25}},
'DC': {'District of Columbia': {'pop': 601723, 'tracts': 179}},
'DE': {'Kent': {'pop': 162310, 'tracts': 33},
'New Castle': {'pop': 538479, 'tracts': 131},
'Sussex': {'pop': 197145, 'tracts': 54}},
'FL': {'Alachua': {'pop': 247336, 'tracts': 56},
'Baker': {'pop': 27115, 'tracts': 4},
'Bay': {'pop': 168852, 'tracts': 44},
'Bradford': {'pop': 28520, 'tracts': 4},
'Brevard': {'pop': 543376, 'tracts': 113},
'Broward': {'pop': 1748066, 'tracts': 361},
'Calhoun': {'pop': 14625, 'tracts': 3},
'Charlotte': {'pop': 159978, 'tracts': 39},
'Citrus': {'pop': 141236, 'tracts': 27},
'Clay': {'pop': 190865, 'tracts': 30},
'Collier': {'pop': 321520, 'tracts': 73},
'Columbia': {'pop': 67531, 'tracts': 12},
'DeSoto': {'pop': 34862, 'tracts': 9},
'Dixie': {'pop': 16422, 'tracts': 3},
'Duval': {'pop': 864263, 'tracts': 173},
'Escambia': {'pop': 297619, 'tracts': 71},
'Flagler': {'pop': 95696, 'tracts': 20},
'Franklin': {'pop': 11549, 'tracts': 4},
'Gadsden': {'pop': 46389, 'tracts': 9},
'Gilchrist': {'pop': 16939, 'tracts': 5},
'Glades': {'pop': 12884, 'tracts': 4},
'Gulf': {'pop': 15863, 'tracts': 3},
'Hamilton': {'pop': 14799, 'tracts': 3},
'Hardee': {'pop': 27731, 'tracts': 6},
'Hendry': {'pop': 39140, 'tracts': 7},
'Hernando': {'pop': 172778, 'tracts': 45},
'Highlands': {'pop': 98786, 'tracts': 27},
'Hillsborough': {'pop': 1229226, 'tracts': 321},
'Holmes': {'pop': 19927, 'tracts': 4},
'Indian River': {'pop': 138028, 'tracts': 30},
'Jackson': {'pop': 49746, 'tracts': 11},
'Jefferson': {'pop': 14761, 'tracts': 3},
'Lafayette': {'pop': 8870, 'tracts': 2},
'Lake': {'pop': 297052, 'tracts': 56},
'Lee': {'pop': 618754, 'tracts': 166},
'Leon': {'pop': 275487, 'tracts': 68},
'Levy': {'pop': 40801, 'tracts': 9},
'Liberty': {'pop': 8365, 'tracts': 2},
'Madison': {'pop': 19224, 'tracts': 5},
'Manatee': {'pop': 322833, 'tracts': 78},
'Marion': {'pop': 331298, 'tracts': 63},
'Martin': {'pop': 146318, 'tracts': 35},
'Miami-Dade': {'pop': 2496435, 'tracts': 519},
'Monroe': {'pop': 73090, 'tracts': 30},
'Nassau': {'pop': 73314, 'tracts': 12},
'Okaloosa': {'pop': 180822, 'tracts': 41},
'Okeechobee': {'pop': 39996, 'tracts': 12},
'Orange': {'pop': 1145956, 'tracts': 207},
'Osceola': {'pop': 268685, 'tracts': 41},
'Palm Beach': {'pop': 1320134, 'tracts': 337},
'Pasco': {'pop': 464697, 'tracts': 134},
'Pinellas': {'pop': 916542, 'tracts': 245},
'Polk': {'pop': 602095, 'tracts': 154},
'Putnam': {'pop': 74364, 'tracts': 17},
'Santa Rosa': {'pop': 151372, 'tracts': 25},
'Sarasota': {'pop': 379448, 'tracts': 94},
'Seminole': {'pop': 422718, 'tracts': 86},
'St. Johns': {'pop': 190039, 'tracts': 40},
'St. Lucie': {'pop': 277789, 'tracts': 44},
'Sumter': {'pop': 93420, 'tracts': 19},
'Suwannee': {'pop': 41551, 'tracts': 7},
'Taylor': {'pop': 22570, 'tracts': 4},
'Union': {'pop': 15535, 'tracts': 3},
'Volusia': {'pop': 494593, 'tracts': 113},
'Wakulla': {'pop': 30776, 'tracts': 4},
'Walton': {'pop': 55043, 'tracts': 11},
'Washington': {'pop': 24896, 'tracts': 7}},
'GA': {'Appling': {'pop': 18236, 'tracts': 5},
'Atkinson': {'pop': 8375, 'tracts': 3},
'Bacon': {'pop': 11096, 'tracts': 3},
'Baker': {'pop': 3451, 'tracts': 2},
'Baldwin': {'pop': 45720, 'tracts': 9},
'Banks': {'pop': 18395, 'tracts': 4},
'Barrow': {'pop': 69367, 'tracts': 18},
'Bartow': {'pop': 100157, 'tracts': 15},
'Ben Hill': {'pop': 17634, 'tracts': 5},
'Berrien': {'pop': 19286, 'tracts': 6},
'Bibb': {'pop': 155547, 'tracts': 44},
'Bleckley': {'pop': 13063, 'tracts': 3},
'Brantley': {'pop': 18411, 'tracts': 3},
'Brooks': {'pop': 16243, 'tracts': 5},
'Bryan': {'pop': 30233, 'tracts': 7},
'Bulloch': {'pop': 70217, 'tracts': 12},
'Burke': {'pop': 23316, 'tracts': 6},
'Butts': {'pop': 23655, 'tracts': 3},
'Calhoun': {'pop': 6694, 'tracts': 2},
'Camden': {'pop': 50513, 'tracts': 10},
'Candler': {'pop': 10998, 'tracts': 3},
'Carroll': {'pop': 110527, 'tracts': 17},
'Catoosa': {'pop': 63942, 'tracts': 11},
'Charlton': {'pop': 12171, 'tracts': 2},
'Chatham': {'pop': 265128, 'tracts': 72},
'Chattahoochee': {'pop': 11267, 'tracts': 5},
'Chattooga': {'pop': 26015, 'tracts': 6},
'Cherokee': {'pop': 214346, 'tracts': 26},
'Clarke': {'pop': 116714, 'tracts': 30},
'Clay': {'pop': 3183, 'tracts': 1},
'Clayton': {'pop': 259424, 'tracts': 50},
'Clinch': {'pop': 6798, 'tracts': 2},
'Cobb': {'pop': 688078, 'tracts': 120},
'Coffee': {'pop': 42356, 'tracts': 9},
'Colquitt': {'pop': 45498, 'tracts': 10},
'Columbia': {'pop': 124053, 'tracts': 20},
'Cook': {'pop': 17212, 'tracts': 4},
'Coweta': {'pop': 127317, 'tracts': 20},
'Crawford': {'pop': 12630, 'tracts': 3},
'Crisp': {'pop': 23439, 'tracts': 6},
'Dade': {'pop': 16633, 'tracts': 4},
'Dawson': {'pop': 22330, 'tracts': 3},
'DeKalb': {'pop': 691893, 'tracts': 145},
'Decatur': {'pop': 27842, 'tracts': 7},
'Dodge': {'pop': 21796, 'tracts': 6},
'Dooly': {'pop': 14918, 'tracts': 3},
'Dougherty': {'pop': 94565, 'tracts': 27},
'Douglas': {'pop': 132403, 'tracts': 20},
'Early': {'pop': 11008, 'tracts': 5},
'Echols': {'pop': 4034, 'tracts': 2},
'Effingham': {'pop': 52250, 'tracts': 10},
'Elbert': {'pop': 20166, 'tracts': 5},
'Emanuel': {'pop': 22598, 'tracts': 6},
'Evans': {'pop': 11000, 'tracts': 3},
'Fannin': {'pop': 23682, 'tracts': 5},
'Fayette': {'pop': 106567, 'tracts': 20},
'Floyd': {'pop': 96317, 'tracts': 20},
'Forsyth': {'pop': 175511, 'tracts': 45},
'Franklin': {'pop': 22084, 'tracts': 5},
'Fulton': {'pop': 920581, 'tracts': 204},
'Gilmer': {'pop': 28292, 'tracts': 5},
'Glascock': {'pop': 3082, 'tracts': 1},
'Glynn': {'pop': 79626, 'tracts': 15},
'Gordon': {'pop': 55186, 'tracts': 9},
'Grady': {'pop': 25011, 'tracts': 6},
'Greene': {'pop': 15994, 'tracts': 7},
'Gwinnett': {'pop': 805321, 'tracts': 113},
'Habersham': {'pop': 43041, 'tracts': 8},
'Hall': {'pop': 179684, 'tracts': 36},
'Hancock': {'pop': 9429, 'tracts': 2},
'Haralson': {'pop': 28780, 'tracts': 5},
'Harris': {'pop': 32024, 'tracts': 5},
'Hart': {'pop': 25213, 'tracts': 5},
'Heard': {'pop': 11834, 'tracts': 3},
'Henry': {'pop': 203922, 'tracts': 25},
'Houston': {'pop': 139900, 'tracts': 23},
'Irwin': {'pop': 9538, 'tracts': 2},
'Jackson': {'pop': 60485, 'tracts': 11},
'Jasper': {'pop': 13900, 'tracts': 3},
'Jeff Davis': {'pop': 15068, 'tracts': 3},
'Jefferson': {'pop': 16930, 'tracts': 4},
'Jenkins': {'pop': 8340, 'tracts': 2},
'Johnson': {'pop': 9980, 'tracts': 3},
'Jones': {'pop': 28669, 'tracts': 6},
'Lamar': {'pop': 18317, 'tracts': 3},
'Lanier': {'pop': 10078, 'tracts': 2},
'Laurens': {'pop': 48434, 'tracts': 13},
'Lee': {'pop': 28298, 'tracts': 5},
'Liberty': {'pop': 63453, 'tracts': 14},
'Lincoln': {'pop': 7996, 'tracts': 2},
'Long': {'pop': 14464, 'tracts': 3},
'Lowndes': {'pop': 109233, 'tracts': 25},
'Lumpkin': {'pop': 29966, 'tracts': 4},
'Macon': {'pop': 14740, 'tracts': 4},
'Madison': {'pop': 28120, 'tracts': 6},
'Marion': {'pop': 8742, 'tracts': 2},
'McDuffie': {'pop': 21875, 'tracts': 5},
'McIntosh': {'pop': 14333, 'tracts': 4},
'Meriwether': {'pop': 21992, 'tracts': 4},
'Miller': {'pop': 6125, 'tracts': 3},
'Mitchell': {'pop': 23498, 'tracts': 5},
'Monroe': {'pop': 26424, 'tracts': 5},
'Montgomery': {'pop': 9123, 'tracts': 3},
'Morgan': {'pop': 17868, 'tracts': 5},
'Murray': {'pop': 39628, 'tracts': 8},
'Muscogee': {'pop': 189885, 'tracts': 53},
'Newton': {'pop': 99958, 'tracts': 13},
'Oconee': {'pop': 32808, 'tracts': 6},
'Oglethorpe': {'pop': 14899, 'tracts': 4},
'Paulding': {'pop': 142324, 'tracts': 19},
'Peach': {'pop': 27695, 'tracts': 6},
'Pickens': {'pop': 29431, 'tracts': 6},
'Pierce': {'pop': 18758, 'tracts': 4},
'Pike': {'pop': 17869, 'tracts': 4},
'Polk': {'pop': 41475, 'tracts': 7},
'Pulaski': {'pop': 12010, 'tracts': 3},
'Putnam': {'pop': 21218, 'tracts': 5},
'Quitman': {'pop': 2513, 'tracts': 1},
'Rabun': {'pop': 16276, 'tracts': 5},
'Randolph': {'pop': 7719, 'tracts': 2},
'Richmond': {'pop': 200549, 'tracts': 47},
'Rockdale': {'pop': 85215, 'tracts': 15},
'Schley': {'pop': 5010, 'tracts': 2},
'Screven': {'pop': 14593, 'tracts': 5},
'Seminole': {'pop': 8729, 'tracts': 3},
'Spalding': {'pop': 64073, 'tracts': 12},
'Stephens': {'pop': 26175, 'tracts': 5},
'Stewart': {'pop': 6058, 'tracts': 2},
'Sumter': {'pop': 32819, 'tracts': 8},
'Talbot': {'pop': 6865, 'tracts': 3},
'Taliaferro': {'pop': 1717, 'tracts': 1},
'Tattnall': {'pop': 25520, 'tracts': 5},
'Taylor': {'pop': 8906, 'tracts': 3},
'Telfair': {'pop': 16500, 'tracts': 3},
'Terrell': {'pop': 9315, 'tracts': 4},
'Thomas': {'pop': 44720, 'tracts': 11},
'Tift': {'pop': 40118, 'tracts': 9},
'Toombs': {'pop': 27223, 'tracts': 6},
'Towns': {'pop': 10471, 'tracts': 3},
'Treutlen': {'pop': 6885, 'tracts': 2},
'Troup': {'pop': 67044, 'tracts': 14},
'Turner': {'pop': 8930, 'tracts': 2},
'Twiggs': {'pop': 9023, 'tracts': 2},
'Union': {'pop': 21356, 'tracts': 6},
'Upson': {'pop': 27153, 'tracts': 7},
'Walker': {'pop': 68756, 'tracts': 13},
'Walton': {'pop': 83768, 'tracts': 15},
'Ware': {'pop': 36312, 'tracts': 9},
'Warren': {'pop': 5834, 'tracts': 2},
'Washington': {'pop': 21187, 'tracts': 5},
'Wayne': {'pop': 30099, 'tracts': 6},
'Webster': {'pop': 2799, 'tracts': 2},
'Wheeler': {'pop': 7421, 'tracts': 2},
'White': {'pop': 27144, 'tracts': 5},
'Whitfield': {'pop': 102599, 'tracts': 18},
'Wilcox': {'pop': 9255, 'tracts': 4},
'Wilkes': {'pop': 10593, 'tracts': 4},
'Wilkinson': {'pop': 9563, 'tracts': 3},
'Worth': {'pop': 21679, 'tracts': 5}},
'HI': {'Hawaii': {'pop': 185079, 'tracts': 34},
'Honolulu': {'pop': 953207, 'tracts': 244},
'Kalawao': {'pop': 90, 'tracts': 1},
'Kauai': {'pop': 67091, 'tracts': 16},
'Maui': {'pop': 154834, 'tracts': 37}},
'IA': {'Adair': {'pop': 7682, 'tracts': 3},
'Adams': {'pop': 4029, 'tracts': 2},
'Allamakee': {'pop': 14330, 'tracts': 5},
'Appanoose': {'pop': 12887, 'tracts': 5},
'Audubon': {'pop': 6119, 'tracts': 3},
'Benton': {'pop': 26076, 'tracts': 7},
'Black Hawk': {'pop': 131090, 'tracts': 38},
'Boone': {'pop': 26306, 'tracts': 7},
'Bremer': {'pop': 24276, 'tracts': 8},
'Buchanan': {'pop': 20958, 'tracts': 6},
'Buena Vista': {'pop': 20260, 'tracts': 6},
'Butler': {'pop': 14867, 'tracts': 5},
'Calhoun': {'pop': 9670, 'tracts': 4},
'Carroll': {'pop': 20816, 'tracts': 6},
'Cass': {'pop': 13956, 'tracts': 5},
'Cedar': {'pop': 18499, 'tracts': 5},
'Cerro Gordo': {'pop': 44151, 'tracts': 11},
'Cherokee': {'pop': 12072, 'tracts': 4},
'Chickasaw': {'pop': 12439, 'tracts': 4},
'Clarke': {'pop': 9286, 'tracts': 3},
'Clay': {'pop': 16667, 'tracts': 4},
'Clayton': {'pop': 18129, 'tracts': 6},
'Clinton': {'pop': 49116, 'tracts': 12},
'Crawford': {'pop': 17096, 'tracts': 5},
'Dallas': {'pop': 66135, 'tracts': 15},
'Davis': {'pop': 8753, 'tracts': 2},
'Decatur': {'pop': 8457, 'tracts': 3},
'Delaware': {'pop': 17764, 'tracts': 4},
'Des Moines': {'pop': 40325, 'tracts': 11},
'Dickinson': {'pop': 16667, 'tracts': 5},
'Dubuque': {'pop': 93653, 'tracts': 26},
'Emmet': {'pop': 10302, 'tracts': 4},
'Fayette': {'pop': 20880, 'tracts': 7},
'Floyd': {'pop': 16303, 'tracts': 5},
'Franklin': {'pop': 10680, 'tracts': 3},
'Fremont': {'pop': 7441, 'tracts': 3},
'Greene': {'pop': 9336, 'tracts': 4},
'Grundy': {'pop': 12453, 'tracts': 4},
'Guthrie': {'pop': 10954, 'tracts': 3},
'Hamilton': {'pop': 15673, 'tracts': 5},
'Hancock': {'pop': 11341, 'tracts': 4},
'Hardin': {'pop': 17534, 'tracts': 6},
'Harrison': {'pop': 14928, 'tracts': 5},
'Henry': {'pop': 20145, 'tracts': 5},
'Howard': {'pop': 9566, 'tracts': 3},
'Humboldt': {'pop': 9815, 'tracts': 4},
'Ida': {'pop': 7089, 'tracts': 3},
'Iowa': {'pop': 16355, 'tracts': 4},
'Jackson': {'pop': 19848, 'tracts': 6},
'Jasper': {'pop': 36842, 'tracts': 9},
'Jefferson': {'pop': 16843, 'tracts': 4},
'Johnson': {'pop': 130882, 'tracts': 24},
'Jones': {'pop': 20638, 'tracts': 5},
'Keokuk': {'pop': 10511, 'tracts': 4},
'Kossuth': {'pop': 15543, 'tracts': 6},
'Lee': {'pop': 35862, 'tracts': 11},
'Linn': {'pop': 211226, 'tracts': 45},
'Louisa': {'pop': 11387, 'tracts': 3},
'Lucas': {'pop': 8898, 'tracts': 4},
'Lyon': {'pop': 11581, 'tracts': 3},
'Madison': {'pop': 15679, 'tracts': 3},
'Mahaska': {'pop': 22381, 'tracts': 7},
'Marion': {'pop': 33309, 'tracts': 8},
'Marshall': {'pop': 40648, 'tracts': 10},
'Mills': {'pop': 15059, 'tracts': 5},
'Mitchell': {'pop': 10776, 'tracts': 3},
'Monona': {'pop': 9243, 'tracts': 4},
'Monroe': {'pop': 7970, 'tracts': 3},
'Montgomery': {'pop': 10740, 'tracts': 4},
'Muscatine': {'pop': 42745, 'tracts': 10},
"O'Brien": {'pop': 14398, 'tracts': 4},
'Osceola': {'pop': 6462, 'tracts': 2},
'Page': {'pop': 15932, 'tracts': 6},
'Palo Alto': {'pop': 9421, 'tracts': 4},
'Plymouth': {'pop': 24986, 'tracts': 6},
'Pocahontas': {'pop': 7310, 'tracts': 3},
'Polk': {'pop': 430640, 'tracts': 98},
'Pottawattamie': {'pop': 93158, 'tracts': 30},
'Poweshiek': {'pop': 18914, 'tracts': 5},
'Ringgold': {'pop': 5131, 'tracts': 2},
'Sac': {'pop': 10350, 'tracts': 4},
'Scott': {'pop': 165224, 'tracts': 47},
'Shelby': {'pop': 12167, 'tracts': 4},
'Sioux': {'pop': 33704, 'tracts': 7},
'Story': {'pop': 89542, 'tracts': 20},
'Tama': {'pop': 17767, 'tracts': 6},
'Taylor': {'pop': 6317, 'tracts': 3},
'Union': {'pop': 12534, 'tracts': 4},
'Van Buren': {'pop': 7570, 'tracts': 2},
'Wapello': {'pop': 35625, 'tracts': 11},
'Warren': {'pop': 46225, 'tracts': 12},
'Washington': {'pop': 21704, 'tracts': 5},
'Wayne': {'pop': 6403, 'tracts': 3},
'Webster': {'pop': 38013, 'tracts': 12},
'Winnebago': {'pop': 10866, 'tracts': 3},
'Winneshiek': {'pop': 21056, 'tracts': 5},
'Woodbury': {'pop': 102172, 'tracts': 26},
'Worth': {'pop': 7598, 'tracts': 3},
'Wright': {'pop': 13229, 'tracts': 5}},
'ID': {'Ada': {'pop': 392365, 'tracts': 59},
'Adams': {'pop': 3976, 'tracts': 2},
'Bannock': {'pop': 82839, 'tracts': 22},
'Bear Lake': {'pop': 5986, 'tracts': 2},
'Benewah': {'pop': 9285, 'tracts': 2},
'Bingham': {'pop': 45607, 'tracts': 8},
'Blaine': {'pop': 21376, 'tracts': 4},
'Boise': {'pop': 7028, 'tracts': 1},
'Bonner': {'pop': 40877, 'tracts': 9},
'Bonneville': {'pop': 104234, 'tracts': 21},
'Boundary': {'pop': 10972, 'tracts': 2},
'Butte': {'pop': 2891, 'tracts': 1},
'Camas': {'pop': 1117, 'tracts': 1},
'Canyon': {'pop': 188923, 'tracts': 29},
'Caribou': {'pop': 6963, 'tracts': 2},
'Cassia': {'pop': 22952, 'tracts': 6},
'Clark': {'pop': 982, 'tracts': 1},
'Clearwater': {'pop': 8761, 'tracts': 2},
'Custer': {'pop': 4368, 'tracts': 1},
'Elmore': {'pop': 27038, 'tracts': 5},
'Franklin': {'pop': 12786, 'tracts': 2},
'Fremont': {'pop': 13242, 'tracts': 3},
'Gem': {'pop': 16719, 'tracts': 3},
'Gooding': {'pop': 15464, 'tracts': 2},
'Idaho': {'pop': 16267, 'tracts': 5},
'Jefferson': {'pop': 26140, 'tracts': 4},
'Jerome': {'pop': 22374, 'tracts': 5},
'Kootenai': {'pop': 138494, 'tracts': 25},
'Latah': {'pop': 37244, 'tracts': 7},
'Lemhi': {'pop': 7936, 'tracts': 3},
'Lewis': {'pop': 3821, 'tracts': 3},
'Lincoln': {'pop': 5208, 'tracts': 1},
'Madison': {'pop': 37536, 'tracts': 6},
'Minidoka': {'pop': 20069, 'tracts': 5},
'Nez Perce': {'pop': 39265, 'tracts': 10},
'Oneida': {'pop': 4286, 'tracts': 1},
'Owyhee': {'pop': 11526, 'tracts': 3},
'Payette': {'pop': 22623, 'tracts': 4},
'Power': {'pop': 7817, 'tracts': 2},
'Shoshone': {'pop': 12765, 'tracts': 3},
'Teton': {'pop': 10170, 'tracts': 1},
'Twin Falls': {'pop': 77230, 'tracts': 14},
'Valley': {'pop': 9862, 'tracts': 3},
'Washington': {'pop': 10198, 'tracts': 3}},
'IL': {'Adams': {'pop': 67103, 'tracts': 18},
'Alexander': {'pop': 8238, 'tracts': 4},
'Bond': {'pop': 17768, 'tracts': 4},
'Boone': {'pop': 54165, 'tracts': 7},
'Brown': {'pop': 6937, 'tracts': 2},
'Bureau': {'pop': 34978, 'tracts': 10},
'Calhoun': {'pop': 5089, 'tracts': 2},
'Carroll': {'pop': 15387, 'tracts': 6},
'Cass': {'pop': 13642, 'tracts': 5},
'Champaign': {'pop': 201081, 'tracts': 43},
'Christian': {'pop': 34800, 'tracts': 10},
'Clark': {'pop': 16335, 'tracts': 4},
'Clay': {'pop': 13815, 'tracts': 4},
'Clinton': {'pop': 37762, 'tracts': 8},
'Coles': {'pop': 53873, 'tracts': 12},
'Cook': {'pop': 5194675, 'tracts': 1318},
'Crawford': {'pop': 19817, 'tracts': 6},
'Cumberland': {'pop': 11048, 'tracts': 3},
'De Witt': {'pop': 16561, 'tracts': 5},
'DeKalb': {'pop': 105160, 'tracts': 21},
'Douglas': {'pop': 19980, 'tracts': 5},
'DuPage': {'pop': 916924, 'tracts': 216},
'Edgar': {'pop': 18576, 'tracts': 5},
'Edwards': {'pop': 6721, 'tracts': 3},
'Effingham': {'pop': 34242, 'tracts': 8},
'Fayette': {'pop': 22140, 'tracts': 7},
'Ford': {'pop': 14081, 'tracts': 5},
'Franklin': {'pop': 39561, 'tracts': 12},
'Fulton': {'pop': 37069, 'tracts': 12},
'Gallatin': {'pop': 5589, 'tracts': 2},
'Greene': {'pop': 13886, 'tracts': 5},
'Grundy': {'pop': 50063, 'tracts': 10},
'Hamilton': {'pop': 8457, 'tracts': 3},
'Hancock': {'pop': 19104, 'tracts': 7},
'Hardin': {'pop': 4320, 'tracts': 2},
'Henderson': {'pop': 7331, 'tracts': 3},
'Henry': {'pop': 50486, 'tracts': 13},
'Iroquois': {'pop': 29718, 'tracts': 9},
'Jackson': {'pop': 60218, 'tracts': 14},
'Jasper': {'pop': 9698, 'tracts': 3},
'Jefferson': {'pop': 38827, 'tracts': 11},
'Jersey': {'pop': 22985, 'tracts': 6},
'Jo Daviess': {'pop': 22678, 'tracts': 6},
'Johnson': {'pop': 12582, 'tracts': 4},
'Kane': {'pop': 515269, 'tracts': 82},
'Kankakee': {'pop': 113449, 'tracts': 29},
'Kendall': {'pop': 114736, 'tracts': 10},
'Knox': {'pop': 52919, 'tracts': 16},
'La Salle': {'pop': 113924, 'tracts': 28},
'Lake': {'pop': 703462, 'tracts': 153},
'Lawrence': {'pop': 16833, 'tracts': 5},
'Lee': {'pop': 36031, 'tracts': 9},
'Livingston': {'pop': 38950, 'tracts': 10},
'Logan': {'pop': 30305, 'tracts': 8},
'Macon': {'pop': 110768, 'tracts': 34},
'Macoupin': {'pop': 47765, 'tracts': 13},
'Madison': {'pop': 269282, 'tracts': 61},
'Marion': {'pop': 39437, 'tracts': 12},
'Marshall': {'pop': 12640, 'tracts': 5},
'Mason': {'pop': 14666, 'tracts': 6},
'Massac': {'pop': 15429, 'tracts': 4},
'McDonough': {'pop': 32612, 'tracts': 10},
'McHenry': {'pop': 308760, 'tracts': 52},
'McLean': {'pop': 169572, 'tracts': 41},
'Menard': {'pop': 12705, 'tracts': 3},
'Mercer': {'pop': 16434, 'tracts': 4},
'Monroe': {'pop': 32957, 'tracts': 6},
'Montgomery': {'pop': 30104, 'tracts': 8},
'Morgan': {'pop': 35547, 'tracts': 10},
'Moultrie': {'pop': 14846, 'tracts': 4},
'Ogle': {'pop': 53497, 'tracts': 11},
'Peoria': {'pop': 186494, 'tracts': 48},
'Perry': {'pop': 22350, 'tracts': 6},
'Piatt': {'pop': 16729, 'tracts': 4},
'Pike': {'pop': 16430, 'tracts': 5},
'Pope': {'pop': 4470, 'tracts': 2},
'Pulaski': {'pop': 6161, 'tracts': 2},
'Putnam': {'pop': 6006, 'tracts': 2},
'Randolph': {'pop': 33476, 'tracts': 9},
'Richland': {'pop': 16233, 'tracts': 5},
'Rock Island': {'pop': 147546, 'tracts': 40},
'Saline': {'pop': 24913, 'tracts': 9},
'Sangamon': {'pop': 197465, 'tracts': 53},
'Schuyler': {'pop': 7544, 'tracts': 3},
'Scott': {'pop': 5355, 'tracts': 2},
'Shelby': {'pop': 22363, 'tracts': 6},
'St. Clair': {'pop': 270056, 'tracts': 60},
'Stark': {'pop': 5994, 'tracts': 2},
'Stephenson': {'pop': 47711, 'tracts': 13},
'Tazewell': {'pop': 135394, 'tracts': 30},
'Union': {'pop': 17808, 'tracts': 5},
'Vermilion': {'pop': 81625, 'tracts': 24},
'Wabash': {'pop': 11947, 'tracts': 4},
'Warren': {'pop': 17707, 'tracts': 5},
'Washington': {'pop': 14716, 'tracts': 4},
'Wayne': {'pop': 16760, 'tracts': 5},
'White': {'pop': 14665, 'tracts': 5},
'Whiteside': {'pop': 58498, 'tracts': 18},
'Will': {'pop': 677560, 'tracts': 152},
'Williamson': {'pop': 66357, 'tracts': 15},
'Winnebago': {'pop': 295266, 'tracts': 77},
'Woodford': {'pop': 38664, 'tracts': 9}},
'IN': {'Adams': {'pop': 34387, 'tracts': 7},
'Allen': {'pop': 355329, 'tracts': 96},
'Bartholomew': {'pop': 76794, 'tracts': 15},
'Benton': {'pop': 8854, 'tracts': 3},
'Blackford': {'pop': 12766, 'tracts': 4},
'Boone': {'pop': 56640, 'tracts': 10},
'Brown': {'pop': 15242, 'tracts': 4},
'Carroll': {'pop': 20155, 'tracts': 7},
'Cass': {'pop': 38966, 'tracts': 11},
'Clark': {'pop': 110232, 'tracts': 26},
'Clay': {'pop': 26890, 'tracts': 6},
'Clinton': {'pop': 33224, 'tracts': 8},
'Crawford': {'pop': 10713, 'tracts': 3},
'Daviess': {'pop': 31648, 'tracts': 7},
'DeKalb': {'pop': 42223, 'tracts': 9},
'Dearborn': {'pop': 50047, 'tracts': 10},
'Decatur': {'pop': 25740, 'tracts': 6},
'Delaware': {'pop': 117671, 'tracts': 30},
'Dubois': {'pop': 41889, 'tracts': 7},
'Elkhart': {'pop': 197559, 'tracts': 36},
'Fayette': {'pop': 24277, 'tracts': 7},
'Floyd': {'pop': 74578, 'tracts': 20},
'Fountain': {'pop': 17240, 'tracts': 5},
'Franklin': {'pop': 23087, 'tracts': 5},
'Fulton': {'pop': 20836, 'tracts': 6},
'Gibson': {'pop': 33503, 'tracts': 7},
'Grant': {'pop': 70061, 'tracts': 16},
'Greene': {'pop': 33165, 'tracts': 9},
'Hamilton': {'pop': 274569, 'tracts': 39},
'Hancock': {'pop': 70002, 'tracts': 10},
'Harrison': {'pop': 39364, 'tracts': 6},
'Hendricks': {'pop': 145448, 'tracts': 21},
'Henry': {'pop': 49462, 'tracts': 13},
'Howard': {'pop': 82752, 'tracts': 20},
'Huntington': {'pop': 37124, 'tracts': 9},
'Jackson': {'pop': 42376, 'tracts': 10},
'Jasper': {'pop': 33478, 'tracts': 8},
'Jay': {'pop': 21253, 'tracts': 7},
'Jefferson': {'pop': 32428, 'tracts': 7},
'Jennings': {'pop': 28525, 'tracts': 6},
'Johnson': {'pop': 139654, 'tracts': 22},
'Knox': {'pop': 38440, 'tracts': 10},
'Kosciusko': {'pop': 77358, 'tracts': 19},
'LaGrange': {'pop': 37128, 'tracts': 8},
'LaPorte': {'pop': 111467, 'tracts': 28},
'Lake': {'pop': 496005, 'tracts': 117},
'Lawrence': {'pop': 46134, 'tracts': 10},
'Madison': {'pop': 131636, 'tracts': 37},
'Marion': {'pop': 903393, 'tracts': 224},
'Marshall': {'pop': 47051, 'tracts': 12},
'Martin': {'pop': 10334, 'tracts': 3},
'Miami': {'pop': 36903, 'tracts': 10},
'Monroe': {'pop': 137974, 'tracts': 31},
'Montgomery': {'pop': 38124, 'tracts': 9},
'Morgan': {'pop': 68894, 'tracts': 13},
'Newton': {'pop': 14244, 'tracts': 4},
'Noble': {'pop': 47536, 'tracts': 10},
'Ohio': {'pop': 6128, 'tracts': 2},
'Orange': {'pop': 19840, 'tracts': 6},
'Owen': {'pop': 21575, 'tracts': 5},
'Parke': {'pop': 17339, 'tracts': 4},
'Perry': {'pop': 19338, 'tracts': 5},
'Pike': {'pop': 12845, 'tracts': 4},
'Porter': {'pop': 164343, 'tracts': 32},
'Posey': {'pop': 25910, 'tracts': 7},
'Pulaski': {'pop': 13402, 'tracts': 4},
'Putnam': {'pop': 37963, 'tracts': 7},
'Randolph': {'pop': 26171, 'tracts': 8},
'Ripley': {'pop': 28818, 'tracts': 6},
'Rush': {'pop': 17392, 'tracts': 5},
'Scott': {'pop': 24181, 'tracts': 5},
'Shelby': {'pop': 44436, 'tracts': 10},
'Spencer': {'pop': 20952, 'tracts': 5},
'St. Joseph': {'pop': 266931, 'tracts': 75},
'Starke': {'pop': 23363, 'tracts': 7},
'Steuben': {'pop': 34185, 'tracts': 9},
'Sullivan': {'pop': 21475, 'tracts': 5},
'Switzerland': {'pop': 10613, 'tracts': 3},
'Tippecanoe': {'pop': 172780, 'tracts': 37},
'Tipton': {'pop': 15936, 'tracts': 4},
'Union': {'pop': 7516, 'tracts': 2},
'Vanderburgh': {'pop': 179703, 'tracts': 49},
'Vermillion': {'pop': 16212, 'tracts': 5},
'Vigo': {'pop': 107848, 'tracts': 28},
'Wabash': {'pop': 32888, 'tracts': 8},
'Warren': {'pop': 8508, 'tracts': 2},
'Warrick': {'pop': 59689, 'tracts': 11},
'Washington': {'pop': 28262, 'tracts': 6},
'Wayne': {'pop': 68917, 'tracts': 17},
'Wells': {'pop': 27636, 'tracts': 7},
'White': {'pop': 24643, 'tracts': 8},
'Whitley': {'pop': 33292, 'tracts': 7}},
'KS': {'Allen': {'pop': 13371, 'tracts': 5},
'Anderson': {'pop': 8102, 'tracts': 2},
'Atchison': {'pop': 16924, 'tracts': 4},
'Barber': {'pop': 4861, 'tracts': 2},
'Barton': {'pop': 27674, 'tracts': 8},
'Bourbon': {'pop': 15173, 'tracts': 5},
'Brown': {'pop': 9984, 'tracts': 3},
'Butler': {'pop': 65880, 'tracts': 13},
'Chase': {'pop': 2790, 'tracts': 1},
'Chautauqua': {'pop': 3669, 'tracts': 1},
'Cherokee': {'pop': 21603, 'tracts': 6},
'Cheyenne': {'pop': 2726, 'tracts': 1},
'Clark': {'pop': 2215, 'tracts': 1},
'Clay': {'pop': 8535, 'tracts': 2},
'Cloud': {'pop': 9533, 'tracts': 4},
'Coffey': {'pop': 8601, 'tracts': 3},
'Comanche': {'pop': 1891, 'tracts': 1},
'Cowley': {'pop': 36311, 'tracts': 11},
'Crawford': {'pop': 39134, 'tracts': 11},
'Decatur': {'pop': 2961, 'tracts': 2},
'Dickinson': {'pop': 19754, 'tracts': 6},
'Doniphan': {'pop': 7945, 'tracts': 3},
'Douglas': {'pop': 110826, 'tracts': 22},
'Edwards': {'pop': 3037, 'tracts': 2},
'Elk': {'pop': 2882, 'tracts': 1},
'Ellis': {'pop': 28452, 'tracts': 6},
'Ellsworth': {'pop': 6497, 'tracts': 2},
'Finney': {'pop': 36776, 'tracts': 12},
'Ford': {'pop': 33848, 'tracts': 7},
'Franklin': {'pop': 25992, 'tracts': 5},
'Geary': {'pop': 34362, 'tracts': 8},
'Gove': {'pop': 2695, 'tracts': 2},
'Graham': {'pop': 2597, 'tracts': 2},
'Grant': {'pop': 7829, 'tracts': 2},
'Gray': {'pop': 6006, 'tracts': 2},
'Greeley': {'pop': 1247, 'tracts': 1},
'Greenwood': {'pop': 6689, 'tracts': 3},
'Hamilton': {'pop': 2690, 'tracts': 1},
'Harper': {'pop': 6034, 'tracts': 3},
'Harvey': {'pop': 34684, 'tracts': 6},
'Haskell': {'pop': 4256, 'tracts': 1},
'Hodgeman': {'pop': 1916, 'tracts': 1},
'Jackson': {'pop': 13462, 'tracts': 3},
'Jefferson': {'pop': 19126, 'tracts': 4},
'Jewell': {'pop': 3077, 'tracts': 2},
'Johnson': {'pop': 544179, 'tracts': 130},
'Kearny': {'pop': 3977, 'tracts': 1},
'Kingman': {'pop': 7858, 'tracts': 3},
'Kiowa': {'pop': 2553, 'tracts': 1},
'Labette': {'pop': 21607, 'tracts': 8},
'Lane': {'pop': 1750, 'tracts': 1},
'Leavenworth': {'pop': 76227, 'tracts': 16},
'Lincoln': {'pop': 3241, 'tracts': 1},
'Linn': {'pop': 9656, 'tracts': 2},
'Logan': {'pop': 2756, 'tracts': 1},
'Lyon': {'pop': 33690, 'tracts': 8},
'Marion': {'pop': 12660, 'tracts': 4},
'Marshall': {'pop': 10117, 'tracts': 4},
'McPherson': {'pop': 29180, 'tracts': 7},
'Meade': {'pop': 4575, 'tracts': 2},
'Miami': {'pop': 32787, 'tracts': 8},
'Mitchell': {'pop': 6373, 'tracts': 2},
'Montgomery': {'pop': 35471, 'tracts': 13},
'Morris': {'pop': 5923, 'tracts': 2},
'Morton': {'pop': 3233, 'tracts': 1},
'Nemaha': {'pop': 10178, 'tracts': 3},
'Neosho': {'pop': 16512, 'tracts': 5},
'Ness': {'pop': 3107, 'tracts': 2},
'Norton': {'pop': 5671, 'tracts': 1},
'Osage': {'pop': 16295, 'tracts': 5},
'Osborne': {'pop': 3858, 'tracts': 1},
'Ottawa': {'pop': 6091, 'tracts': 2},
'Pawnee': {'pop': 6973, 'tracts': 2},
'Phillips': {'pop': 5642, 'tracts': 3},
'Pottawatomie': {'pop': 21604, 'tracts': 4},
'Pratt': {'pop': 9656, 'tracts': 3},
'Rawlins': {'pop': 2519, 'tracts': 1},
'Reno': {'pop': 64511, 'tracts': 17},
'Republic': {'pop': 4980, 'tracts': 3},
'Rice': {'pop': 10083, 'tracts': 3},
'Riley': {'pop': 71115, 'tracts': 14},
'Rooks': {'pop': 5181, 'tracts': 2},
'Rush': {'pop': 3307, 'tracts': 2},
'Russell': {'pop': 6970, 'tracts': 2},
'Saline': {'pop': 55606, 'tracts': 12},
'Scott': {'pop': 4936, 'tracts': 1},
'Sedgwick': {'pop': 498365, 'tracts': 124},
'Seward': {'pop': 22952, 'tracts': 5},
'Shawnee': {'pop': 177934, 'tracts': 43},
'Sheridan': {'pop': 2556, 'tracts': 2},
'Sherman': {'pop': 6010, 'tracts': 2},
'Smith': {'pop': 3853, 'tracts': 2},
'Stafford': {'pop': 4437, 'tracts': 2},
'Stanton': {'pop': 2235, 'tracts': 1},
'Stevens': {'pop': 5724, 'tracts': 2},
'Sumner': {'pop': 24132, 'tracts': 6},
'Thomas': {'pop': 7900, 'tracts': 2},
'Trego': {'pop': 3001, 'tracts': 1},
'Wabaunsee': {'pop': 7053, 'tracts': 2},
'Wallace': {'pop': 1485, 'tracts': 1},
'Washington': {'pop': 5799, 'tracts': 2},
'Wichita': {'pop': 2234, 'tracts': 1},
'Wilson': {'pop': 9409, 'tracts': 4},
'Woodson': {'pop': 3309, 'tracts': 2},
'Wyandotte': {'pop': 157505, 'tracts': 70}},
'KY': {'Adair': {'pop': 18656, 'tracts': 7},
'Allen': {'pop': 19956, 'tracts': 6},
'Anderson': {'pop': 21421, 'tracts': 5},
'Ballard': {'pop': 8249, 'tracts': 3},
'Barren': {'pop': 42173, 'tracts': 10},
'Bath': {'pop': 11591, 'tracts': 3},
'Bell': {'pop': 28691, 'tracts': 9},
'Boone': {'pop': 118811, 'tracts': 22},
'Bourbon': {'pop': 19985, 'tracts': 6},
'Boyd': {'pop': 49542, 'tracts': 13},
'Boyle': {'pop': 28432, 'tracts': 7},
'Bracken': {'pop': 8488, 'tracts': 3},
'Breathitt': {'pop': 13878, 'tracts': 7},
'Breckinridge': {'pop': 20059, 'tracts': 6},
'Bullitt': {'pop': 74319, 'tracts': 18},
'Butler': {'pop': 12690, 'tracts': 5},
'Caldwell': {'pop': 12984, 'tracts': 3},
'Calloway': {'pop': 37191, 'tracts': 9},
'Campbell': {'pop': 90336, 'tracts': 25},
'Carlisle': {'pop': 5104, 'tracts': 3},
'Carroll': {'pop': 10811, 'tracts': 3},
'Carter': {'pop': 27720, 'tracts': 7},
'Casey': {'pop': 15955, 'tracts': 5},
'Christian': {'pop': 73955, 'tracts': 19},
'Clark': {'pop': 35613, 'tracts': 10},
'Clay': {'pop': 21730, 'tracts': 6},
'Clinton': {'pop': 10272, 'tracts': 3},
'Crittenden': {'pop': 9315, 'tracts': 4},
'Cumberland': {'pop': 6856, 'tracts': 2},
'Daviess': {'pop': 96656, 'tracts': 23},
'Edmonson': {'pop': 12161, 'tracts': 4},
'Elliott': {'pop': 7852, 'tracts': 2},
'Estill': {'pop': 14672, 'tracts': 4},
'Fayette': {'pop': 295803, 'tracts': 82},
'Fleming': {'pop': 14348, 'tracts': 4},
'Floyd': {'pop': 39451, 'tracts': 10},
'Franklin': {'pop': 49285, 'tracts': 11},
'Fulton': {'pop': 6813, 'tracts': 2},
'Gallatin': {'pop': 8589, 'tracts': 2},
'Garrard': {'pop': 16912, 'tracts': 4},
'Grant': {'pop': 24662, 'tracts': 4},
'Graves': {'pop': 37121, 'tracts': 9},
'Grayson': {'pop': 25746, 'tracts': 7},
'Green': {'pop': 11258, 'tracts': 4},
'Greenup': {'pop': 36910, 'tracts': 9},
'Hancock': {'pop': 8565, 'tracts': 3},
'Hardin': {'pop': 105543, 'tracts': 22},
'Harlan': {'pop': 29278, 'tracts': 11},
'Harrison': {'pop': 18846, 'tracts': 5},
'Hart': {'pop': 18199, 'tracts': 5},
'Henderson': {'pop': 46250, 'tracts': 11},
'Henry': {'pop': 15416, 'tracts': 5},
'Hickman': {'pop': 4902, 'tracts': 1},
'Hopkins': {'pop': 46920, 'tracts': 12},
'Jackson': {'pop': 13494, 'tracts': 3},
'Jefferson': {'pop': 741096, 'tracts': 191},
'Jessamine': {'pop': 48586, 'tracts': 9},
'Johnson': {'pop': 23356, 'tracts': 6},
'Kenton': {'pop': 159720, 'tracts': 41},
'Knott': {'pop': 16346, 'tracts': 5},
'Knox': {'pop': 31883, 'tracts': 8},
'Larue': {'pop': 14193, 'tracts': 4},
'Laurel': {'pop': 58849, 'tracts': 13},
'Lawrence': {'pop': 15860, 'tracts': 5},
'Lee': {'pop': 7887, 'tracts': 3},
'Leslie': {'pop': 11310, 'tracts': 3},
'Letcher': {'pop': 24519, 'tracts': 7},
'Lewis': {'pop': 13870, 'tracts': 4},
'Lincoln': {'pop': 24742, 'tracts': 6},
'Livingston': {'pop': 9519, 'tracts': 2},
'Logan': {'pop': 26835, 'tracts': 6},
'Lyon': {'pop': 8314, 'tracts': 3},
'Madison': {'pop': 82916, 'tracts': 19},
'Magoffin': {'pop': 13333, 'tracts': 4},
'Marion': {'pop': 19820, 'tracts': 6},
'Marshall': {'pop': 31448, 'tracts': 6},
'Martin': {'pop': 12929, 'tracts': 3},
'Mason': {'pop': 17490, 'tracts': 5},
'McCracken': {'pop': 65565, 'tracts': 17},
'McCreary': {'pop': 18306, 'tracts': 4},
'McLean': {'pop': 9531, 'tracts': 3},
'Meade': {'pop': 28602, 'tracts': 8},
'Menifee': {'pop': 6306, 'tracts': 2},
'Mercer': {'pop': 21331, 'tracts': 5},
'Metcalfe': {'pop': 10099, 'tracts': 3},
'Monroe': {'pop': 10963, 'tracts': 4},
'Montgomery': {'pop': 26499, 'tracts': 6},
'Morgan': {'pop': 13923, 'tracts': 5},
'Muhlenberg': {'pop': 31499, 'tracts': 9},
'Nelson': {'pop': 43437, 'tracts': 9},
'Nicholas': {'pop': 7135, 'tracts': 2},
'Ohio': {'pop': 23842, 'tracts': 7},
'Oldham': {'pop': 60316, 'tracts': 14},
'Owen': {'pop': 10841, 'tracts': 3},
'Owsley': {'pop': 4755, 'tracts': 2},
'Pendleton': {'pop': 14877, 'tracts': 3},
'Perry': {'pop': 28712, 'tracts': 8},
'Pike': {'pop': 65024, 'tracts': 19},
'Powell': {'pop': 12613, 'tracts': 2},
'Pulaski': {'pop': 63063, 'tracts': 14},
'Robertson': {'pop': 2282, 'tracts': 1},
'Rockcastle': {'pop': 17056, 'tracts': 4},
'Rowan': {'pop': 23333, 'tracts': 4},
'Russell': {'pop': 17565, 'tracts': 5},
'Scott': {'pop': 47173, 'tracts': 14},
'Shelby': {'pop': 42074, 'tracts': 9},
'Simpson': {'pop': 17327, 'tracts': 4},
'Spencer': {'pop': 17061, 'tracts': 4},
'Taylor': {'pop': 24512, 'tracts': 5},
'Todd': {'pop': 12460, 'tracts': 4},
'Trigg': {'pop': 14339, 'tracts': 5},
'Trimble': {'pop': 8809, 'tracts': 2},
'Union': {'pop': 15007, 'tracts': 4},
'Warren': {'pop': 113792, 'tracts': 24},
'Washington': {'pop': 11717, 'tracts': 3},
'Wayne': {'pop': 20813, 'tracts': 5},
'Webster': {'pop': 13621, 'tracts': 4},
'Whitley': {'pop': 35637, 'tracts': 8},
'Wolfe': {'pop': 7355, 'tracts': 2},
'Woodford': {'pop': 24939, 'tracts': 8}},
'LA': {'Acadia': {'pop': 61773, 'tracts': 12},
'Allen': {'pop': 25764, 'tracts': 5},
'Ascension': {'pop': 107215, 'tracts': 14},
'Assumption': {'pop': 23421, 'tracts': 6},
'Avoyelles': {'pop': 42073, 'tracts': 9},
'Beauregard': {'pop': 35654, 'tracts': 7},
'Bienville': {'pop': 14353, 'tracts': 5},
'Bossier': {'pop': 116979, 'tracts': 22},
'Caddo': {'pop': 254969, 'tracts': 64},
'Calcasieu': {'pop': 192768, 'tracts': 44},
'Caldwell': {'pop': 10132, 'tracts': 3},
'Cameron': {'pop': 6839, 'tracts': 3},
'Catahoula': {'pop': 10407, 'tracts': 3},
'Claiborne': {'pop': 17195, 'tracts': 5},
'Concordia': {'pop': 20822, 'tracts': 5},
'De Soto': {'pop': 26656, 'tracts': 7},
'East Baton Rouge': {'pop': 440171, 'tracts': 92},
'East Carroll': {'pop': 7759, 'tracts': 3},
'East Feliciana': {'pop': 20267, 'tracts': 5},
'Evangeline': {'pop': 33984, 'tracts': 8},
'Franklin': {'pop': 20767, 'tracts': 6},
'Grant': {'pop': 22309, 'tracts': 5},
'Iberia': {'pop': 73240, 'tracts': 15},
'Iberville': {'pop': 33387, 'tracts': 7},
'Jackson': {'pop': 16274, 'tracts': 5},
'Jefferson': {'pop': 432552, 'tracts': 127},
'Jefferson Davis': {'pop': 31594, 'tracts': 7},
'La Salle': {'pop': 14890, 'tracts': 3},
'Lafayette': {'pop': 221578, 'tracts': 43},
'Lafourche': {'pop': 96318, 'tracts': 23},
'Lincoln': {'pop': 46735, 'tracts': 10},
'Livingston': {'pop': 128026, 'tracts': 17},
'Madison': {'pop': 12093, 'tracts': 5},
'Morehouse': {'pop': 27979, 'tracts': 8},
'Natchitoches': {'pop': 39566, 'tracts': 9},
'Orleans': {'pop': 343829, 'tracts': 177},
'Ouachita': {'pop': 153720, 'tracts': 40},
'Plaquemines': {'pop': 23042, 'tracts': 9},
'Pointe Coupee': {'pop': 22802, 'tracts': 6},
'Rapides': {'pop': 131613, 'tracts': 33},
'Red River': {'pop': 9091, 'tracts': 2},
'Richland': {'pop': 20725, 'tracts': 6},
'Sabine': {'pop': 24233, 'tracts': 7},
'St. Bernard': {'pop': 35897, 'tracts': 18},
'St. Charles': {'pop': 52780, 'tracts': 13},
'St. Helena': {'pop': 11203, 'tracts': 2},
'St. James': {'pop': 22102, 'tracts': 7},
'St. John the Baptist': {'pop': 45924, 'tracts': 11},
'St. Landry': {'pop': 83384, 'tracts': 19},
'St. Martin': {'pop': 52160, 'tracts': 11},
'St. Mary': {'pop': 54650, 'tracts': 16},
'St. Tammany': {'pop': 233740, 'tracts': 43},
'Tangipahoa': {'pop': 121097, 'tracts': 20},
'Tensas': {'pop': 5252, 'tracts': 3},
'Terrebonne': {'pop': 111860, 'tracts': 21},
'Union': {'pop': 22721, 'tracts': 6},
'Vermilion': {'pop': 57999, 'tracts': 12},
'Vernon': {'pop': 52334, 'tracts': 12},
'Washington': {'pop': 47168, 'tracts': 11},
'Webster': {'pop': 41207, 'tracts': 11},
'West Baton Rouge': {'pop': 23788, 'tracts': 5},
'West Carroll': {'pop': 11604, 'tracts': 3},
'West Feliciana': {'pop': 15625, 'tracts': 3},
'Winn': {'pop': 15313, 'tracts': 4}},
'MA': {'Barnstable': {'pop': 215888, 'tracts': 57},
'Berkshire': {'pop': 131219, 'tracts': 39},
'Bristol': {'pop': 548285, 'tracts': 126},
'Dukes': {'pop': 16535, 'tracts': 4},
'Essex': {'pop': 743159, 'tracts': 163},
'Franklin': {'pop': 71372, 'tracts': 18},
'Hampden': {'pop': 463490, 'tracts': 103},
'Hampshire': {'pop': 158080, 'tracts': 36},
'Middlesex': {'pop': 1503085, 'tracts': 318},
'Nantucket': {'pop': 10172, 'tracts': 6},
'Norfolk': {'pop': 670850, 'tracts': 130},
'Plymouth': {'pop': 494919, 'tracts': 100},
'Suffolk': {'pop': 722023, 'tracts': 204},
'Worcester': {'pop': 798552, 'tracts': 172}},
'MD': {'Allegany': {'pop': 75087, 'tracts': 23},
'Anne Arundel': {'pop': 537656, 'tracts': 104},
'Baltimore': {'pop': 805029, 'tracts': 214},
'Baltimore City': {'pop': 620961, 'tracts': 200},
'Calvert': {'pop': 88737, 'tracts': 18},
'Caroline': {'pop': 33066, 'tracts': 9},
'Carroll': {'pop': 167134, 'tracts': 38},
'Cecil': {'pop': 101108, 'tracts': 19},
'Charles': {'pop': 146551, 'tracts': 30},
'Dorchester': {'pop': 32618, 'tracts': 10},
'Frederick': {'pop': 233385, 'tracts': 61},
'Garrett': {'pop': 30097, 'tracts': 7},
'Harford': {'pop': 244826, 'tracts': 57},
'Howard': {'pop': 287085, 'tracts': 55},
'Kent': {'pop': 20197, 'tracts': 5},
'Montgomery': {'pop': 971777, 'tracts': 215},
"Prince George's": {'pop': 863420, 'tracts': 218},
"Queen Anne's": {'pop': 47798, 'tracts': 12},
'Somerset': {'pop': 26470, 'tracts': 8},
"St. Mary's": {'pop': 105151, 'tracts': 18},
'Talbot': {'pop': 37782, 'tracts': 10},
'Washington': {'pop': 147430, 'tracts': 32},
'Wicomico': {'pop': 98733, 'tracts': 19},
'Worcester': {'pop': 51454, 'tracts': 17}},
'ME': {'Androscoggin': {'pop': 107702, 'tracts': 28},
'Aroostook': {'pop': 71870, 'tracts': 24},
'Cumberland': {'pop': 281674, 'tracts': 67},
'Franklin': {'pop': 30768, 'tracts': 9},
'Hancock': {'pop': 54418, 'tracts': 17},
'Kennebec': {'pop': 122151, 'tracts': 31},
'Knox': {'pop': 39736, 'tracts': 11},
'Lincoln': {'pop': 34457, 'tracts': 9},
'Oxford': {'pop': 57833, 'tracts': 17},
'Penobscot': {'pop': 153923, 'tracts': 46},
'Piscataquis': {'pop': 17535, 'tracts': 8},
'Sagadahoc': {'pop': 35293, 'tracts': 8},
'Somerset': {'pop': 52228, 'tracts': 17},
'Waldo': {'pop': 38786, 'tracts': 8},
'Washington': {'pop': 32856, 'tracts': 14},
'York': {'pop': 197131, 'tracts': 41}},
'MI': {'Alcona': {'pop': 10942, 'tracts': 5},
'Alger': {'pop': 9601, 'tracts': 3},
'Allegan': {'pop': 111408, 'tracts': 25},
'Alpena': {'pop': 29598, 'tracts': 10},
'Antrim': {'pop': 23580, 'tracts': 7},
'Arenac': {'pop': 15899, 'tracts': 5},
'Baraga': {'pop': 8860, 'tracts': 2},
'Barry': {'pop': 59173, 'tracts': 11},
'Bay': {'pop': 107771, 'tracts': 26},
'Benzie': {'pop': 17525, 'tracts': 5},
'Berrien': {'pop': 156813, 'tracts': 48},
'Branch': {'pop': 45248, 'tracts': 12},
'Calhoun': {'pop': 136146, 'tracts': 39},
'Cass': {'pop': 52293, 'tracts': 11},
'Charlevoix': {'pop': 25949, 'tracts': 13},
'Cheboygan': {'pop': 26152, 'tracts': 8},
'Chippewa': {'pop': 38520, 'tracts': 14},
'Clare': {'pop': 30926, 'tracts': 11},
'Clinton': {'pop': 75382, 'tracts': 22},
'Crawford': {'pop': 14074, 'tracts': 5},
'Delta': {'pop': 37069, 'tracts': 11},
'Dickinson': {'pop': 26168, 'tracts': 7},
'Eaton': {'pop': 107759, 'tracts': 28},
'Emmet': {'pop': 32694, 'tracts': 8},
'Genesee': {'pop': 425790, 'tracts': 131},
'Gladwin': {'pop': 25692, 'tracts': 9},
'Gogebic': {'pop': 16427, 'tracts': 7},
'Grand Traverse': {'pop': 86986, 'tracts': 16},
'Gratiot': {'pop': 42476, 'tracts': 10},
'Hillsdale': {'pop': 46688, 'tracts': 12},
'Houghton': {'pop': 36628, 'tracts': 11},
'Huron': {'pop': 33118, 'tracts': 12},
'Ingham': {'pop': 280895, 'tracts': 81},
'Ionia': {'pop': 63905, 'tracts': 13},
'Iosco': {'pop': 25887, 'tracts': 9},
'Iron': {'pop': 11817, 'tracts': 5},
'Isabella': {'pop': 70311, 'tracts': 15},
'Jackson': {'pop': 160248, 'tracts': 38},
'Kalamazoo': {'pop': 250331, 'tracts': 57},
'Kalkaska': {'pop': 17153, 'tracts': 5},
'Kent': {'pop': 602622, 'tracts': 128},
'Keweenaw': {'pop': 2156, 'tracts': 2},
'Lake': {'pop': 11539, 'tracts': 4},
'Lapeer': {'pop': 88319, 'tracts': 24},
'Leelanau': {'pop': 21708, 'tracts': 6},
'Lenawee': {'pop': 99892, 'tracts': 23},
'Livingston': {'pop': 180967, 'tracts': 61},
'Luce': {'pop': 6631, 'tracts': 3},
'Mackinac': {'pop': 11113, 'tracts': 6},
'Macomb': {'pop': 840978, 'tracts': 216},
'Manistee': {'pop': 24733, 'tracts': 9},
'Marquette': {'pop': 67077, 'tracts': 24},
'Mason': {'pop': 28705, 'tracts': 8},
'Mecosta': {'pop': 42798, 'tracts': 11},
'Menominee': {'pop': 24029, 'tracts': 7},
'Midland': {'pop': 83629, 'tracts': 19},
'Missaukee': {'pop': 14849, 'tracts': 4},
'Monroe': {'pop': 152021, 'tracts': 39},
'Montcalm': {'pop': 63342, 'tracts': 13},
'Montmorency': {'pop': 9765, 'tracts': 5},
'Muskegon': {'pop': 172188, 'tracts': 42},
'Newaygo': {'pop': 48460, 'tracts': 11},
'Oakland': {'pop': 1202362, 'tracts': 338},
'Oceana': {'pop': 26570, 'tracts': 7},
'Ogemaw': {'pop': 21699, 'tracts': 7},
'Ontonagon': {'pop': 6780, 'tracts': 4},
'Osceola': {'pop': 23528, 'tracts': 6},
'Oscoda': {'pop': 8640, 'tracts': 5},
'Otsego': {'pop': 24164, 'tracts': 6},
'Ottawa': {'pop': 263801, 'tracts': 53},
'Presque Isle': {'pop': 13376, 'tracts': 6},
'Roscommon': {'pop': 24449, 'tracts': 10},
'Saginaw': {'pop': 200169, 'tracts': 56},
'Sanilac': {'pop': 43114, 'tracts': 12},
'Schoolcraft': {'pop': 8485, 'tracts': 3},
'Shiawassee': {'pop': 70648, 'tracts': 17},
'St. Clair': {'pop': 163040, 'tracts': 49},
'St. Joseph': {'pop': 61295, 'tracts': 17},
'Tuscola': {'pop': 55729, 'tracts': 13},
'Van Buren': {'pop': 76258, 'tracts': 15},
'Washtenaw': {'pop': 344791, 'tracts': 100},
'Wayne': {'pop': 1820584, 'tracts': 610},
'Wexford': {'pop': 32735, 'tracts': 8}},
'MN': {'Aitkin': {'pop': 16202, 'tracts': 6},
'Anoka': {'pop': 330844, 'tracts': 83},
'Becker': {'pop': 32504, 'tracts': 10},
'Beltrami': {'pop': 44442, 'tracts': 10},
'Benton': {'pop': 38451, 'tracts': 9},
'Big Stone': {'pop': 5269, 'tracts': 3},
'Blue Earth': {'pop': 64013, 'tracts': 16},
'Brown': {'pop': 25893, 'tracts': 8},
'Carlton': {'pop': 35386, 'tracts': 7},
'Carver': {'pop': 91042, 'tracts': 19},
'Cass': {'pop': 28567, 'tracts': 10},
'Chippewa': {'pop': 12441, 'tracts': 4},
'Chisago': {'pop': 53887, 'tracts': 10},
'Clay': {'pop': 58999, 'tracts': 13},
'Clearwater': {'pop': 8695, 'tracts': 3},
'Cook': {'pop': 5176, 'tracts': 3},
'Cottonwood': {'pop': 11687, 'tracts': 4},
'Crow Wing': {'pop': 62500, 'tracts': 16},
'Dakota': {'pop': 398552, 'tracts': 95},
'Dodge': {'pop': 20087, 'tracts': 5},
'Douglas': {'pop': 36009, 'tracts': 9},
'Faribault': {'pop': 14553, 'tracts': 6},
'Fillmore': {'pop': 20866, 'tracts': 6},
'Freeborn': {'pop': 31255, 'tracts': 10},
'Goodhue': {'pop': 46183, 'tracts': 10},
'Grant': {'pop': 6018, 'tracts': 2},
'Hennepin': {'pop': 1152425, 'tracts': 299},
'Houston': {'pop': 19027, 'tracts': 5},
'Hubbard': {'pop': 20428, 'tracts': 7},
'Isanti': {'pop': 37816, 'tracts': 8},
'Itasca': {'pop': 45058, 'tracts': 11},
'Jackson': {'pop': 10266, 'tracts': 4},
'Kanabec': {'pop': 16239, 'tracts': 4},
'Kandiyohi': {'pop': 42239, 'tracts': 12},
'Kittson': {'pop': 4552, 'tracts': 2},
'Koochiching': {'pop': 13311, 'tracts': 4},
'Lac qui Parle': {'pop': 7259, 'tracts': 3},
'Lake': {'pop': 10866, 'tracts': 3},
'Lake of the Woods': {'pop': 4045, 'tracts': 2},
'Le Sueur': {'pop': 27703, 'tracts': 6},
'Lincoln': {'pop': 5896, 'tracts': 2},
'Lyon': {'pop': 25857, 'tracts': 7},
'Mahnomen': {'pop': 5413, 'tracts': 2},
'Marshall': {'pop': 9439, 'tracts': 4},
'Martin': {'pop': 20840, 'tracts': 6},
'McLeod': {'pop': 36651, 'tracts': 7},
'Meeker': {'pop': 23300, 'tracts': 6},
'Mille Lacs': {'pop': 26097, 'tracts': 7},
'Morrison': {'pop': 33198, 'tracts': 8},
'Mower': {'pop': 39163, 'tracts': 11},
'Murray': {'pop': 8725, 'tracts': 3},
'Nicollet': {'pop': 32727, 'tracts': 7},
'Nobles': {'pop': 21378, 'tracts': 6},
'Norman': {'pop': 6852, 'tracts': 3},
'Olmsted': {'pop': 144248, 'tracts': 33},
'Otter Tail': {'pop': 57303, 'tracts': 17},
'Pennington': {'pop': 13930, 'tracts': 5},
'Pine': {'pop': 29750, 'tracts': 8},
'Pipestone': {'pop': 9596, 'tracts': 5},
'Polk': {'pop': 31600, 'tracts': 10},
'Pope': {'pop': 10995, 'tracts': 4},
'Ramsey': {'pop': 508640, 'tracts': 137},
'Red Lake': {'pop': 4089, 'tracts': 2},
'Redwood': {'pop': 16059, 'tracts': 6},
'Renville': {'pop': 15730, 'tracts': 6},
'Rice': {'pop': 64142, 'tracts': 13},
'Rock': {'pop': 9687, 'tracts': 3},
'Roseau': {'pop': 15629, 'tracts': 5},
'Scott': {'pop': 129928, 'tracts': 21},
'Sherburne': {'pop': 88499, 'tracts': 11},
'Sibley': {'pop': 15226, 'tracts': 4},
'St. Louis': {'pop': 200226, 'tracts': 66},
'Stearns': {'pop': 150642, 'tracts': 29},
'Steele': {'pop': 36576, 'tracts': 8},
'Stevens': {'pop': 9726, 'tracts': 3},
'Swift': {'pop': 9783, 'tracts': 4},
'Todd': {'pop': 24895, 'tracts': 8},
'Traverse': {'pop': 3558, 'tracts': 2},
'Wabasha': {'pop': 21676, 'tracts': 6},
'Wadena': {'pop': 13843, 'tracts': 3},
'Waseca': {'pop': 19136, 'tracts': 5},
'Washington': {'pop': 238136, 'tracts': 50},
'Watonwan': {'pop': 11211, 'tracts': 3},
'Wilkin': {'pop': 6576, 'tracts': 2},
'Winona': {'pop': 51461, 'tracts': 10},
'Wright': {'pop': 124700, 'tracts': 17},
'Yellow Medicine': {'pop': 10438, 'tracts': 4}},
'MO': {'Adair': {'pop': 25607, 'tracts': 7},
'Andrew': {'pop': 17291, 'tracts': 4},
'Atchison': {'pop': 5685, 'tracts': 2},
'Audrain': {'pop': 25529, 'tracts': 7},
'Barry': {'pop': 35597, 'tracts': 7},
'Barton': {'pop': 12402, 'tracts': 3},
'Bates': {'pop': 17049, 'tracts': 4},
'Benton': {'pop': 19056, 'tracts': 6},
'Bollinger': {'pop': 12363, 'tracts': 3},
'Boone': {'pop': 162642, 'tracts': 29},
'Buchanan': {'pop': 89201, 'tracts': 25},
'Butler': {'pop': 42794, 'tracts': 10},
'Caldwell': {'pop': 9424, 'tracts': 2},
'Callaway': {'pop': 44332, 'tracts': 8},
'Camden': {'pop': 44002, 'tracts': 11},
'Cape Girardeau': {'pop': 75674, 'tracts': 16},
'Carroll': {'pop': 9295, 'tracts': 3},
'Carter': {'pop': 6265, 'tracts': 2},
'Cass': {'pop': 99478, 'tracts': 20},
'Cedar': {'pop': 13982, 'tracts': 3},
'Chariton': {'pop': 7831, 'tracts': 3},
'Christian': {'pop': 77422, 'tracts': 14},
'Clark': {'pop': 7139, 'tracts': 3},
'Clay': {'pop': 221939, 'tracts': 44},
'Clinton': {'pop': 20743, 'tracts': 4},
'Cole': {'pop': 75990, 'tracts': 15},
'Cooper': {'pop': 17601, 'tracts': 5},
'Crawford': {'pop': 24696, 'tracts': 6},
'Dade': {'pop': 7883, 'tracts': 2},
'Dallas': {'pop': 16777, 'tracts': 3},
'Daviess': {'pop': 8433, 'tracts': 2},
'DeKalb': {'pop': 12892, 'tracts': 2},
'Dent': {'pop': 15657, 'tracts': 4},
'Douglas': {'pop': 13684, 'tracts': 3},
'Dunklin': {'pop': 31953, 'tracts': 10},
'Franklin': {'pop': 101492, 'tracts': 17},
'Gasconade': {'pop': 15222, 'tracts': 5},
'Gentry': {'pop': 6738, 'tracts': 2},
'Greene': {'pop': 275174, 'tracts': 62},
'Grundy': {'pop': 10261, 'tracts': 4},
'Harrison': {'pop': 8957, 'tracts': 3},
'Henry': {'pop': 22272, 'tracts': 6},
'Hickory': {'pop': 9627, 'tracts': 3},
'Holt': {'pop': 4912, 'tracts': 3},
'Howard': {'pop': 10144, 'tracts': 3},
'Howell': {'pop': 40400, 'tracts': 8},
'Iron': {'pop': 10630, 'tracts': 4},
'Jackson': {'pop': 674158, 'tracts': 199},
'Jasper': {'pop': 117404, 'tracts': 22},
'Jefferson': {'pop': 218733, 'tracts': 42},
'Johnson': {'pop': 52595, 'tracts': 9},
'Knox': {'pop': 4131, 'tracts': 2},
'Laclede': {'pop': 35571, 'tracts': 6},
'Lafayette': {'pop': 33381, 'tracts': 7},
'Lawrence': {'pop': 38634, 'tracts': 7},
'Lewis': {'pop': 10211, 'tracts': 4},
'Lincoln': {'pop': 52566, 'tracts': 7},
'Linn': {'pop': 12761, 'tracts': 5},
'Livingston': {'pop': 15195, 'tracts': 5},
'Macon': {'pop': 15566, 'tracts': 5},
'Madison': {'pop': 12226, 'tracts': 3},
'Maries': {'pop': 9176, 'tracts': 3},
'Marion': {'pop': 28781, 'tracts': 8},
'McDonald': {'pop': 23083, 'tracts': 4},
'Mercer': {'pop': 3785, 'tracts': 2},
'Miller': {'pop': 24748, 'tracts': 5},
'Mississippi': {'pop': 14358, 'tracts': 4},
'Moniteau': {'pop': 15607, 'tracts': 4},
'Monroe': {'pop': 8840, 'tracts': 3},
'Montgomery': {'pop': 12236, 'tracts': 4},
'Morgan': {'pop': 20565, 'tracts': 5},
'New Madrid': {'pop': 18956, 'tracts': 6},
'Newton': {'pop': 58114, 'tracts': 12},
'Nodaway': {'pop': 23370, 'tracts': 5},
'Oregon': {'pop': 10881, 'tracts': 3},
'Osage': {'pop': 13878, 'tracts': 4},
'Ozark': {'pop': 9723, 'tracts': 2},
'Pemiscot': {'pop': 18296, 'tracts': 6},
'Perry': {'pop': 18971, 'tracts': 5},
'Pettis': {'pop': 42201, 'tracts': 11},
'Phelps': {'pop': 45156, 'tracts': 10},
'Pike': {'pop': 18516, 'tracts': 5},
'Platte': {'pop': 89322, 'tracts': 20},
'Polk': {'pop': 31137, 'tracts': 4},
'Pulaski': {'pop': 52274, 'tracts': 9},
'Putnam': {'pop': 4979, 'tracts': 2},
'Ralls': {'pop': 10167, 'tracts': 3},
'Randolph': {'pop': 25414, 'tracts': 6},
'Ray': {'pop': 23494, 'tracts': 4},
'Reynolds': {'pop': 6696, 'tracts': 2},
'Ripley': {'pop': 14100, 'tracts': 4},
'Saline': {'pop': 23370, 'tracts': 8},
'Schuyler': {'pop': 4431, 'tracts': 2},
'Scotland': {'pop': 4843, 'tracts': 2},
'Scott': {'pop': 39191, 'tracts': 10},
'Shannon': {'pop': 8441, 'tracts': 2},
'Shelby': {'pop': 6373, 'tracts': 3},
'St. Charles': {'pop': 360485, 'tracts': 79},
'St. Clair': {'pop': 9805, 'tracts': 3},
'St. Francois': {'pop': 65359, 'tracts': 11},
'St. Louis': {'pop': 998954, 'tracts': 199},
'St. Louis City': {'pop': 319294, 'tracts': 106},
'Ste. Genevieve': {'pop': 18145, 'tracts': 4},
'Stoddard': {'pop': 29968, 'tracts': 8},
'Stone': {'pop': 32202, 'tracts': 6},
'Sullivan': {'pop': 6714, 'tracts': 3},
'Taney': {'pop': 51675, 'tracts': 10},
'Texas': {'pop': 26008, 'tracts': 4},
'Vernon': {'pop': 21159, 'tracts': 6},
'Warren': {'pop': 32513, 'tracts': 5},
'Washington': {'pop': 25195, 'tracts': 5},
'Wayne': {'pop': 13521, 'tracts': 4},
'Webster': {'pop': 36202, 'tracts': 8},
'Worth': {'pop': 2171, 'tracts': 1},
'Wright': {'pop': 18815, 'tracts': 4}},
'MS': {'Adams': {'pop': 32297, 'tracts': 9},
'Alcorn': {'pop': 37057, 'tracts': 7},
'Amite': {'pop': 13131, 'tracts': 3},
'Attala': {'pop': 19564, 'tracts': 6},
'Benton': {'pop': 8729, 'tracts': 2},
'Bolivar': {'pop': 34145, 'tracts': 8},
'Calhoun': {'pop': 14962, 'tracts': 5},
'Carroll': {'pop': 10597, 'tracts': 2},
'Chickasaw': {'pop': 17392, 'tracts': 4},
'Choctaw': {'pop': 8547, 'tracts': 3},
'Claiborne': {'pop': 9604, 'tracts': 3},
'Clarke': {'pop': 16732, 'tracts': 4},
'Clay': {'pop': 20634, 'tracts': 5},
'Coahoma': {'pop': 26151, 'tracts': 7},
'Copiah': {'pop': 29449, 'tracts': 6},
'Covington': {'pop': 19568, 'tracts': 4},
'DeSoto': {'pop': 161252, 'tracts': 33},
'Forrest': {'pop': 74934, 'tracts': 17},
'Franklin': {'pop': 8118, 'tracts': 2},
'George': {'pop': 22578, 'tracts': 5},
'Greene': {'pop': 14400, 'tracts': 2},
'Grenada': {'pop': 21906, 'tracts': 5},
'Hancock': {'pop': 43929, 'tracts': 7},
'Harrison': {'pop': 187105, 'tracts': 46},
'Hinds': {'pop': 245285, 'tracts': 64},
'Holmes': {'pop': 19198, 'tracts': 5},
'Humphreys': {'pop': 9375, 'tracts': 3},
'Issaquena': {'pop': 1406, 'tracts': 1},
'Itawamba': {'pop': 23401, 'tracts': 5},
'Jackson': {'pop': 139668, 'tracts': 28},
'Jasper': {'pop': 17062, 'tracts': 4},
'Jefferson': {'pop': 7726, 'tracts': 2},
'Jefferson Davis': {'pop': 12487, 'tracts': 3},
'Jones': {'pop': 67761, 'tracts': 14},
'Kemper': {'pop': 10456, 'tracts': 2},
'Lafayette': {'pop': 47351, 'tracts': 10},
'Lamar': {'pop': 55658, 'tracts': 8},
'Lauderdale': {'pop': 80261, 'tracts': 19},
'Lawrence': {'pop': 12929, 'tracts': 3},
'Leake': {'pop': 23805, 'tracts': 5},
'Lee': {'pop': 82910, 'tracts': 19},
'Leflore': {'pop': 32317, 'tracts': 8},
'Lincoln': {'pop': 34869, 'tracts': 6},
'Lowndes': {'pop': 59779, 'tracts': 14},
'Madison': {'pop': 95203, 'tracts': 21},
'Marion': {'pop': 27088, 'tracts': 6},
'Marshall': {'pop': 37144, 'tracts': 6},
'Monroe': {'pop': 36989, 'tracts': 9},
'Montgomery': {'pop': 10925, 'tracts': 3},
'Neshoba': {'pop': 29676, 'tracts': 7},
'Newton': {'pop': 21720, 'tracts': 5},
'Noxubee': {'pop': 11545, 'tracts': 3},
'Oktibbeha': {'pop': 47671, 'tracts': 8},
'Panola': {'pop': 34707, 'tracts': 6},
'Pearl River': {'pop': 55834, 'tracts': 9},
'Perry': {'pop': 12250, 'tracts': 3},
'Pike': {'pop': 40404, 'tracts': 8},
'Pontotoc': {'pop': 29957, 'tracts': 6},
'Prentiss': {'pop': 25276, 'tracts': 5},
'Quitman': {'pop': 8223, 'tracts': 3},
'Rankin': {'pop': 141617, 'tracts': 27},
'Scott': {'pop': 28264, 'tracts': 6},
'Sharkey': {'pop': 4916, 'tracts': 2},
'Simpson': {'pop': 27503, 'tracts': 5},
'Smith': {'pop': 16491, 'tracts': 3},
'Stone': {'pop': 17786, 'tracts': 3},
'Sunflower': {'pop': 29450, 'tracts': 7},
'Tallahatchie': {'pop': 15378, 'tracts': 4},
'Tate': {'pop': 28886, 'tracts': 5},
'Tippah': {'pop': 22232, 'tracts': 4},
'Tishomingo': {'pop': 19593, 'tracts': 4},
'Tunica': {'pop': 10778, 'tracts': 3},
'Union': {'pop': 27134, 'tracts': 6},
'Walthall': {'pop': 15443, 'tracts': 3},
'Warren': {'pop': 48773, 'tracts': 12},
'Washington': {'pop': 51137, 'tracts': 19},
'Wayne': {'pop': 20747, 'tracts': 4},
'Webster': {'pop': 10253, 'tracts': 3},
'Wilkinson': {'pop': 9878, 'tracts': 2},
'Winston': {'pop': 19198, 'tracts': 5},
'Yalobusha': {'pop': 12678, 'tracts': 3},
'Yazoo': {'pop': 28065, 'tracts': 6}},
'MT': {'Beaverhead': {'pop': 9246, 'tracts': 3},
'Big Horn': {'pop': 12865, 'tracts': 5},
'Blaine': {'pop': 6491, 'tracts': 4},
'Broadwater': {'pop': 5612, 'tracts': 2},
'Carbon': {'pop': 10078, 'tracts': 5},
'Carter': {'pop': 1160, 'tracts': 1},
'Cascade': {'pop': 81327, 'tracts': 22},
'Chouteau': {'pop': 5813, 'tracts': 2},
'Custer': {'pop': 11699, 'tracts': 6},
'Daniels': {'pop': 1751, 'tracts': 1},
'Dawson': {'pop': 8966, 'tracts': 3},
'Deer Lodge': {'pop': 9298, 'tracts': 3},
'Fallon': {'pop': 2890, 'tracts': 1},
'Fergus': {'pop': 11586, 'tracts': 2},
'Flathead': {'pop': 90928, 'tracts': 19},
'Gallatin': {'pop': 89513, 'tracts': 22},
'Garfield': {'pop': 1206, 'tracts': 1},
'Glacier': {'pop': 13399, 'tracts': 4},
'Golden Valley': {'pop': 884, 'tracts': 1},
'Granite': {'pop': 3079, 'tracts': 1},
'Hill': {'pop': 16096, 'tracts': 6},
'Jefferson': {'pop': 11406, 'tracts': 3},
'Judith Basin': {'pop': 2072, 'tracts': 1},
'Lake': {'pop': 28746, 'tracts': 8},
'Lewis and Clark': {'pop': 63395, 'tracts': 14},
'Liberty': {'pop': 2339, 'tracts': 1},
'Lincoln': {'pop': 19687, 'tracts': 5},
'Madison': {'pop': 7691, 'tracts': 3},
'McCone': {'pop': 1734, 'tracts': 1},
'Meagher': {'pop': 1891, 'tracts': 1},
'Mineral': {'pop': 4223, 'tracts': 2},
'Missoula': {'pop': 109299, 'tracts': 20},
'Musselshell': {'pop': 4538, 'tracts': 2},
'Park': {'pop': 15636, 'tracts': 6},
'Petroleum': {'pop': 494, 'tracts': 1},
'Phillips': {'pop': 4253, 'tracts': 1},
'Pondera': {'pop': 6153, 'tracts': 2},
'Powder River': {'pop': 1743, 'tracts': 1},
'Powell': {'pop': 7027, 'tracts': 2},
'Prairie': {'pop': 1179, 'tracts': 1},
'Ravalli': {'pop': 40212, 'tracts': 10},
'Richland': {'pop': 9746, 'tracts': 4},
'Roosevelt': {'pop': 10425, 'tracts': 3},
'Rosebud': {'pop': 9233, 'tracts': 4},
'Sanders': {'pop': 11413, 'tracts': 3},
'Sheridan': {'pop': 3384, 'tracts': 2},
'Silver Bow': {'pop': 34200, 'tracts': 8},
'Stillwater': {'pop': 9117, 'tracts': 3},
'Sweet Grass': {'pop': 3651, 'tracts': 1},
'Teton': {'pop': 6073, 'tracts': 3},
'Toole': {'pop': 5324, 'tracts': 3},
'Treasure': {'pop': 718, 'tracts': 1},
'Valley': {'pop': 7369, 'tracts': 3},
'Wheatland': {'pop': 2168, 'tracts': 1},
'Wibaux': {'pop': 1017, 'tracts': 1},
'Yellowstone': {'pop': 147972, 'tracts': 32}},
'NC': {'Alamance': {'pop': 151131, 'tracts': 36},
'Alexander': {'pop': 37198, 'tracts': 7},
'Alleghany': {'pop': 11155, 'tracts': 3},
'Anson': {'pop': 26948, 'tracts': 6},
'Ashe': {'pop': 27281, 'tracts': 6},
'Avery': {'pop': 17797, 'tracts': 5},
'Beaufort': {'pop': 47759, 'tracts': 11},
'Bertie': {'pop': 21282, 'tracts': 4},
'Bladen': {'pop': 35190, 'tracts': 6},
'Brunswick': {'pop': 107431, 'tracts': 33},
'Buncombe': {'pop': 238318, 'tracts': 56},
'Burke': {'pop': 90912, 'tracts': 18},
'Cabarrus': {'pop': 178011, 'tracts': 37},
'Caldwell': {'pop': 83029, 'tracts': 17},
'Camden': {'pop': 9980, 'tracts': 2},
'Carteret': {'pop': 66469, 'tracts': 38},
'Caswell': {'pop': 23719, 'tracts': 6},
'Catawba': {'pop': 154358, 'tracts': 31},
'Chatham': {'pop': 63505, 'tracts': 13},
'Cherokee': {'pop': 27444, 'tracts': 7},
'Chowan': {'pop': 14793, 'tracts': 3},
'Clay': {'pop': 10587, 'tracts': 2},
'Cleveland': {'pop': 98078, 'tracts': 22},
'Columbus': {'pop': 58098, 'tracts': 13},
'Craven': {'pop': 103505, 'tracts': 21},
'Cumberland': {'pop': 319431, 'tracts': 68},
'Currituck': {'pop': 23547, 'tracts': 8},
'Dare': {'pop': 33920, 'tracts': 11},
'Davidson': {'pop': 162878, 'tracts': 34},
'Davie': {'pop': 41240, 'tracts': 7},
'Duplin': {'pop': 58505, 'tracts': 11},
'Durham': {'pop': 267587, 'tracts': 60},
'Edgecombe': {'pop': 56552, 'tracts': 14},
'Forsyth': {'pop': 350670, 'tracts': 93},
'Franklin': {'pop': 60619, 'tracts': 12},
'Gaston': {'pop': 206086, 'tracts': 65},
'Gates': {'pop': 12197, 'tracts': 3},
'Graham': {'pop': 8861, 'tracts': 3},
'Granville': {'pop': 59916, 'tracts': 13},
'Greene': {'pop': 21362, 'tracts': 4},
'Guilford': {'pop': 488406, 'tracts': 119},
'Halifax': {'pop': 54691, 'tracts': 12},
'Harnett': {'pop': 114678, 'tracts': 27},
'Haywood': {'pop': 59036, 'tracts': 16},
'Henderson': {'pop': 106740, 'tracts': 27},
'Hertford': {'pop': 24669, 'tracts': 5},
'Hoke': {'pop': 46952, 'tracts': 9},
'Hyde': {'pop': 5810, 'tracts': 2},
'Iredell': {'pop': 159437, 'tracts': 44},
'Jackson': {'pop': 40271, 'tracts': 9},
'Johnston': {'pop': 168878, 'tracts': 25},
'Jones': {'pop': 10153, 'tracts': 3},
'Lee': {'pop': 57866, 'tracts': 13},
'Lenoir': {'pop': 59495, 'tracts': 15},
'Lincoln': {'pop': 78265, 'tracts': 18},
'Macon': {'pop': 33922, 'tracts': 9},
'Madison': {'pop': 20764, 'tracts': 6},
'Martin': {'pop': 24505, 'tracts': 6},
'McDowell': {'pop': 44996, 'tracts': 10},
'Mecklenburg': {'pop': 919628, 'tracts': 233},
'Mitchell': {'pop': 15579, 'tracts': 4},
'Montgomery': {'pop': 27798, 'tracts': 6},
'Moore': {'pop': 88247, 'tracts': 18},
'Nash': {'pop': 95840, 'tracts': 18},
'New Hanover': {'pop': 202667, 'tracts': 45},
'Northampton': {'pop': 22099, 'tracts': 5},
'Onslow': {'pop': 177772, 'tracts': 32},
'Orange': {'pop': 133801, 'tracts': 28},
'Pamlico': {'pop': 13144, 'tracts': 4},
'Pasquotank': {'pop': 40661, 'tracts': 10},
'Pender': {'pop': 52217, 'tracts': 16},
'Perquimans': {'pop': 13453, 'tracts': 3},
'Person': {'pop': 39464, 'tracts': 7},
'Pitt': {'pop': 168148, 'tracts': 32},
'Polk': {'pop': 20510, 'tracts': 7},
'Randolph': {'pop': 141752, 'tracts': 28},
'Richmond': {'pop': 46639, 'tracts': 11},
'Robeson': {'pop': 134168, 'tracts': 31},
'Rockingham': {'pop': 93643, 'tracts': 21},
'Rowan': {'pop': 138428, 'tracts': 30},
'Rutherford': {'pop': 67810, 'tracts': 13},
'Sampson': {'pop': 63431, 'tracts': 11},
'Scotland': {'pop': 36157, 'tracts': 7},
'Stanly': {'pop': 60585, 'tracts': 13},
'Stokes': {'pop': 47401, 'tracts': 9},
'Surry': {'pop': 73673, 'tracts': 22},
'Swain': {'pop': 13981, 'tracts': 5},
'Transylvania': {'pop': 33090, 'tracts': 7},
'Tyrrell': {'pop': 4407, 'tracts': 1},
'Union': {'pop': 201292, 'tracts': 41},
'Vance': {'pop': 45422, 'tracts': 10},
'Wake': {'pop': 900993, 'tracts': 187},
'Warren': {'pop': 20972, 'tracts': 6},
'Washington': {'pop': 13228, 'tracts': 3},
'Watauga': {'pop': 51079, 'tracts': 13},
'Wayne': {'pop': 122623, 'tracts': 26},
'Wilkes': {'pop': 69340, 'tracts': 14},
'Wilson': {'pop': 81234, 'tracts': 19},
'Yadkin': {'pop': 38406, 'tracts': 7},
'Yancey': {'pop': 17818, 'tracts': 5}},
'ND': {'Adams': {'pop': 2343, 'tracts': 1},
'Barnes': {'pop': 11066, 'tracts': 4},
'Benson': {'pop': 6660, 'tracts': 4},
'Billings': {'pop': 783, 'tracts': 1},
'Bottineau': {'pop': 6429, 'tracts': 3},
'Bowman': {'pop': 3151, 'tracts': 2},
'Burke': {'pop': 1968, 'tracts': 1},
'Burleigh': {'pop': 81308, 'tracts': 19},
'Cass': {'pop': 149778, 'tracts': 33},
'Cavalier': {'pop': 3993, 'tracts': 2},
'Dickey': {'pop': 5289, 'tracts': 3},
'Divide': {'pop': 2071, 'tracts': 1},
'Dunn': {'pop': 3536, 'tracts': 1},
'Eddy': {'pop': 2385, 'tracts': 1},
'Emmons': {'pop': 3550, 'tracts': 1},
'Foster': {'pop': 3343, 'tracts': 1},
'Golden Valley': {'pop': 1680, 'tracts': 1},
'Grand Forks': {'pop': 66861, 'tracts': 18},
'Grant': {'pop': 2394, 'tracts': 1},
'Griggs': {'pop': 2420, 'tracts': 1},
'Hettinger': {'pop': 2477, 'tracts': 2},
'Kidder': {'pop': 2435, 'tracts': 1},
'LaMoure': {'pop': 4139, 'tracts': 2},
'Logan': {'pop': 1990, 'tracts': 1},
'McHenry': {'pop': 5395, 'tracts': 2},
'McIntosh': {'pop': 2809, 'tracts': 1},
'McKenzie': {'pop': 6360, 'tracts': 4},
'McLean': {'pop': 8962, 'tracts': 2},
'Mercer': {'pop': 8424, 'tracts': 3},
'Morton': {'pop': 27471, 'tracts': 5},
'Mountrail': {'pop': 7673, 'tracts': 3},
'Nelson': {'pop': 3126, 'tracts': 1},
'Oliver': {'pop': 1846, 'tracts': 1},
'Pembina': {'pop': 7413, 'tracts': 5},
'Pierce': {'pop': 4357, 'tracts': 2},
'Ramsey': {'pop': 11451, 'tracts': 3},
'Ransom': {'pop': 5457, 'tracts': 3},
'Renville': {'pop': 2470, 'tracts': 1},
'Richland': {'pop': 16321, 'tracts': 6},
'Rolette': {'pop': 13937, 'tracts': 4},
'Sargent': {'pop': 3829, 'tracts': 2},
'Sheridan': {'pop': 1321, 'tracts': 1},
'Sioux': {'pop': 4153, 'tracts': 2},
'Slope': {'pop': 727, 'tracts': 1},
'Stark': {'pop': 24199, 'tracts': 8},
'Steele': {'pop': 1975, 'tracts': 1},
'Stutsman': {'pop': 21100, 'tracts': 6},
'Towner': {'pop': 2246, 'tracts': 1},
'Traill': {'pop': 8121, 'tracts': 4},
'Walsh': {'pop': 11119, 'tracts': 6},
'Ward': {'pop': 61675, 'tracts': 13},
'Wells': {'pop': 4207, 'tracts': 2},
'Williams': {'pop': 22398, 'tracts': 7}},
'NE': {'Adams': {'pop': 31364, 'tracts': 9},
'Antelope': {'pop': 6685, 'tracts': 3},
'Arthur': {'pop': 460, 'tracts': 1},
'Banner': {'pop': 690, 'tracts': 1},
'Blaine': {'pop': 478, 'tracts': 1},
'Boone': {'pop': 5505, 'tracts': 2},
'Box Butte': {'pop': 11308, 'tracts': 3},
'Boyd': {'pop': 2099, 'tracts': 1},
'Brown': {'pop': 3145, 'tracts': 1},
'Buffalo': {'pop': 46102, 'tracts': 11},
'Burt': {'pop': 6858, 'tracts': 3},
'Butler': {'pop': 8395, 'tracts': 3},
'Cass': {'pop': 25241, 'tracts': 6},
'Cedar': {'pop': 8852, 'tracts': 2},
'Chase': {'pop': 3966, 'tracts': 1},
'Cherry': {'pop': 5713, 'tracts': 2},
'Cheyenne': {'pop': 9998, 'tracts': 3},
'Clay': {'pop': 6542, 'tracts': 2},
'Colfax': {'pop': 10515, 'tracts': 3},
'Cuming': {'pop': 9139, 'tracts': 3},
'Custer': {'pop': 10939, 'tracts': 4},
'Dakota': {'pop': 21006, 'tracts': 4},
'Dawes': {'pop': 9182, 'tracts': 2},
'Dawson': {'pop': 24326, 'tracts': 7},
'Deuel': {'pop': 1941, 'tracts': 1},
'Dixon': {'pop': 6000, 'tracts': 2},
'Dodge': {'pop': 36691, 'tracts': 9},
'Douglas': {'pop': 517110, 'tracts': 156},
'Dundy': {'pop': 2008, 'tracts': 1},
'Fillmore': {'pop': 5890, 'tracts': 2},
'Franklin': {'pop': 3225, 'tracts': 2},
'Frontier': {'pop': 2756, 'tracts': 1},
'Furnas': {'pop': 4959, 'tracts': 1},
'Gage': {'pop': 22311, 'tracts': 7},
'Garden': {'pop': 2057, 'tracts': 1},
'Garfield': {'pop': 2049, 'tracts': 1},
'Gosper': {'pop': 2044, 'tracts': 1},
'Grant': {'pop': 614, 'tracts': 1},
'Greeley': {'pop': 2538, 'tracts': 1},
'Hall': {'pop': 58607, 'tracts': 14},
'Hamilton': {'pop': 9124, 'tracts': 3},
'Harlan': {'pop': 3423, 'tracts': 1},
'Hayes': {'pop': 967, 'tracts': 1},
'Hitchcock': {'pop': 2908, 'tracts': 1},
'Holt': {'pop': 10435, 'tracts': 4},
'Hooker': {'pop': 736, 'tracts': 1},
'Howard': {'pop': 6274, 'tracts': 2},
'Jefferson': {'pop': 7547, 'tracts': 3},
'Johnson': {'pop': 5217, 'tracts': 2},
'Kearney': {'pop': 6489, 'tracts': 2},
'Keith': {'pop': 8368, 'tracts': 3},
'Keya Paha': {'pop': 824, 'tracts': 1},
'Kimball': {'pop': 3821, 'tracts': 1},
'Knox': {'pop': 8701, 'tracts': 3},
'Lancaster': {'pop': 285407, 'tracts': 74},
'Lincoln': {'pop': 36288, 'tracts': 8},
'Logan': {'pop': 763, 'tracts': 1},
'Loup': {'pop': 632, 'tracts': 1},
'Madison': {'pop': 34876, 'tracts': 9},
'McPherson': {'pop': 539, 'tracts': 1},
'Merrick': {'pop': 7845, 'tracts': 3},
'Morrill': {'pop': 5042, 'tracts': 1},
'Nance': {'pop': 3735, 'tracts': 1},
'Nemaha': {'pop': 7248, 'tracts': 2},
'Nuckolls': {'pop': 4500, 'tracts': 2},
'Otoe': {'pop': 15740, 'tracts': 5},
'Pawnee': {'pop': 2773, 'tracts': 1},
'Perkins': {'pop': 2970, 'tracts': 1},
'Phelps': {'pop': 9188, 'tracts': 3},
'Pierce': {'pop': 7266, 'tracts': 2},
'Platte': {'pop': 32237, 'tracts': 7},
'Polk': {'pop': 5406, 'tracts': 2},
'Red Willow': {'pop': 11055, 'tracts': 3},
'Richardson': {'pop': 8363, 'tracts': 3},
'Rock': {'pop': 1526, 'tracts': 1},
'Saline': {'pop': 14200, 'tracts': 4},
'Sarpy': {'pop': 158840, 'tracts': 43},
'Saunders': {'pop': 20780, 'tracts': 5},
'Scotts Bluff': {'pop': 36970, 'tracts': 11},
'Seward': {'pop': 16750, 'tracts': 4},
'Sheridan': {'pop': 5469, 'tracts': 2},
'Sherman': {'pop': 3152, 'tracts': 1},
'Sioux': {'pop': 1311, 'tracts': 1},
'Stanton': {'pop': 6129, 'tracts': 2},
'Thayer': {'pop': 5228, 'tracts': 2},
'Thomas': {'pop': 647, 'tracts': 1},
'Thurston': {'pop': 6940, 'tracts': 2},
'Valley': {'pop': 4260, 'tracts': 2},
'Washington': {'pop': 20234, 'tracts': 5},
'Wayne': {'pop': 9595, 'tracts': 2},
'Webster': {'pop': 3812, 'tracts': 2},
'Wheeler': {'pop': 818, 'tracts': 1},
'York': {'pop': 13665, 'tracts': 4}},
'NH': {'Belknap': {'pop': 60088, 'tracts': 15},
'Carroll': {'pop': 47818, 'tracts': 11},
'Cheshire': {'pop': 77117, 'tracts': 16},
'Coos': {'pop': 33055, 'tracts': 11},
'Grafton': {'pop': 89118, 'tracts': 19},
'Hillsborough': {'pop': 400721, 'tracts': 86},
'Merrimack': {'pop': 146445, 'tracts': 36},
'Rockingham': {'pop': 295223, 'tracts': 66},
'Strafford': {'pop': 123143, 'tracts': 25},
'Sullivan': {'pop': 43742, 'tracts': 10}},
'NJ': {'Atlantic': {'pop': 274549, 'tracts': 69},
'Bergen': {'pop': 905116, 'tracts': 179},
'Burlington': {'pop': 448734, 'tracts': 114},
'Camden': {'pop': 513657, 'tracts': 127},
'Cape May': {'pop': 97265, 'tracts': 32},
'Cumberland': {'pop': 156898, 'tracts': 35},
'Essex': {'pop': 783969, 'tracts': 210},
'Gloucester': {'pop': 288288, 'tracts': 63},
'Hudson': {'pop': 634266, 'tracts': 166},
'Hunterdon': {'pop': 128349, 'tracts': 26},
'Mercer': {'pop': 366513, 'tracts': 77},
'Middlesex': {'pop': 809858, 'tracts': 175},
'Monmouth': {'pop': 630380, 'tracts': 144},
'Morris': {'pop': 492276, 'tracts': 100},
'Ocean': {'pop': 576567, 'tracts': 126},
'Passaic': {'pop': 501226, 'tracts': 100},
'Salem': {'pop': 66083, 'tracts': 24},
'Somerset': {'pop': 323444, 'tracts': 68},
'Sussex': {'pop': 149265, 'tracts': 41},
'Union': {'pop': 536499, 'tracts': 108},
'Warren': {'pop': 108692, 'tracts': 23}},
'NM': {'Bernalillo': {'pop': 662564, 'tracts': 153},
'Catron': {'pop': 3725, 'tracts': 1},
'Chaves': {'pop': 65645, 'tracts': 16},
'Cibola': {'pop': 27213, 'tracts': 7},
'Colfax': {'pop': 13750, 'tracts': 3},
'Curry': {'pop': 48376, 'tracts': 12},
'De Baca': {'pop': 2022, 'tracts': 1},
'Dona Ana': {'pop': 209233, 'tracts': 41},
'Eddy': {'pop': 53829, 'tracts': 12},
'Grant': {'pop': 29514, 'tracts': 8},
'Guadalupe': {'pop': 4687, 'tracts': 1},
'Harding': {'pop': 695, 'tracts': 1},
'Hidalgo': {'pop': 4894, 'tracts': 2},
'Lea': {'pop': 64727, 'tracts': 18},
'Lincoln': {'pop': 20497, 'tracts': 5},
'Los Alamos': {'pop': 17950, 'tracts': 4},
'Luna': {'pop': 25095, 'tracts': 6},
'McKinley': {'pop': 71492, 'tracts': 17},
'Mora': {'pop': 4881, 'tracts': 1},
'Otero': {'pop': 63797, 'tracts': 16},
'Quay': {'pop': 9041, 'tracts': 3},
'Rio Arriba': {'pop': 40246, 'tracts': 9},
'Roosevelt': {'pop': 19846, 'tracts': 5},
'San Juan': {'pop': 130044, 'tracts': 33},
'San Miguel': {'pop': 29393, 'tracts': 7},
'Sandoval': {'pop': 131561, 'tracts': 28},
'Santa Fe': {'pop': 144170, 'tracts': 50},
'Sierra': {'pop': 11988, 'tracts': 4},
'Socorro': {'pop': 17866, 'tracts': 6},
'Taos': {'pop': 32937, 'tracts': 6},
'Torrance': {'pop': 16383, 'tracts': 4},
'Union': {'pop': 4549, 'tracts': 1},
'Valencia': {'pop': 76569, 'tracts': 18}},
'NV': {'Carson City': {'pop': 55274, 'tracts': 14},
'Churchill': {'pop': 24877, 'tracts': 7},
'Clark': {'pop': 1951269, 'tracts': 487},
'Douglas': {'pop': 46997, 'tracts': 17},
'Elko': {'pop': 48818, 'tracts': 14},
'Esmeralda': {'pop': 783, 'tracts': 1},
'Eureka': {'pop': 1987, 'tracts': 1},
'Humboldt': {'pop': 16528, 'tracts': 4},
'Lander': {'pop': 5775, 'tracts': 1},
'Lincoln': {'pop': 5345, 'tracts': 2},
'Lyon': {'pop': 51980, 'tracts': 10},
'Mineral': {'pop': 4772, 'tracts': 2},
'Nye': {'pop': 43946, 'tracts': 10},
'Pershing': {'pop': 6753, 'tracts': 1},
'Storey': {'pop': 4010, 'tracts': 1},
'Washoe': {'pop': 421407, 'tracts': 112},
'White Pine': {'pop': 10030, 'tracts': 3}},
'NY': {'Albany': {'pop': 304204, 'tracts': 75},
'Allegany': {'pop': 48946, 'tracts': 13},
'Bronx': {'pop': 1385108, 'tracts': 339},
'Broome': {'pop': 200600, 'tracts': 55},
'Cattaraugus': {'pop': 80317, 'tracts': 21},
'Cayuga': {'pop': 80026, 'tracts': 20},
'Chautauqua': {'pop': 134905, 'tracts': 35},
'Chemung': {'pop': 88830, 'tracts': 22},
'Chenango': {'pop': 50477, 'tracts': 12},
'Clinton': {'pop': 82128, 'tracts': 19},
'Columbia': {'pop': 63096, 'tracts': 21},
'Cortland': {'pop': 49336, 'tracts': 12},
'Delaware': {'pop': 47980, 'tracts': 14},
'Dutchess': {'pop': 297488, 'tracts': 79},
'Erie': {'pop': 919040, 'tracts': 237},
'Essex': {'pop': 39370, 'tracts': 13},
'Franklin': {'pop': 51599, 'tracts': 14},
'Fulton': {'pop': 55531, 'tracts': 15},
'Genesee': {'pop': 60079, 'tracts': 15},
'Greene': {'pop': 49221, 'tracts': 15},
'Hamilton': {'pop': 4836, 'tracts': 4},
'Herkimer': {'pop': 64519, 'tracts': 19},
'Jefferson': {'pop': 116229, 'tracts': 26},
'Kings': {'pop': 2504700, 'tracts': 760},
'Lewis': {'pop': 27087, 'tracts': 7},
'Livingston': {'pop': 65393, 'tracts': 15},
'Madison': {'pop': 73442, 'tracts': 16},
'Monroe': {'pop': 744344, 'tracts': 192},
'Montgomery': {'pop': 50219, 'tracts': 16},
'Nassau': {'pop': 1339532, 'tracts': 280},
'New York': {'pop': 1585873, 'tracts': 288},
'Niagara': {'pop': 216469, 'tracts': 61},
'Oneida': {'pop': 234878, 'tracts': 74},
'Onondaga': {'pop': 467026, 'tracts': 140},
'Ontario': {'pop': 107931, 'tracts': 25},
'Orange': {'pop': 372813, 'tracts': 79},
'Orleans': {'pop': 42883, 'tracts': 11},
'Oswego': {'pop': 122109, 'tracts': 29},
'Otsego': {'pop': 62259, 'tracts': 17},
'Putnam': {'pop': 99710, 'tracts': 19},
'Queens': {'pop': 2230722, 'tracts': 669},
'Rensselaer': {'pop': 159429, 'tracts': 42},
'Richmond': {'pop': 468730, 'tracts': 109},
'Rockland': {'pop': 311687, 'tracts': 65},
'Saratoga': {'pop': 219607, 'tracts': 50},
'Schenectady': {'pop': 154727, 'tracts': 43},
'Schoharie': {'pop': 32749, 'tracts': 8},
'Schuyler': {'pop': 18343, 'tracts': 5},
'Seneca': {'pop': 35251, 'tracts': 10},
'St. Lawrence': {'pop': 111944, 'tracts': 28},
'Steuben': {'pop': 98990, 'tracts': 30},
'Suffolk': {'pop': 1493350, 'tracts': 322},
'Sullivan': {'pop': 77547, 'tracts': 24},
'Tioga': {'pop': 51125, 'tracts': 10},
'Tompkins': {'pop': 101564, 'tracts': 23},
'Ulster': {'pop': 182493, 'tracts': 47},
'Warren': {'pop': 65707, 'tracts': 19},
'Washington': {'pop': 63216, 'tracts': 17},
'Wayne': {'pop': 93772, 'tracts': 23},
'Westchester': {'pop': 949113, 'tracts': 223},
'Wyoming': {'pop': 42155, 'tracts': 11},
'Yates': {'pop': 25348, 'tracts': 5}},
'OH': {'Adams': {'pop': 28550, 'tracts': 6},
'Allen': {'pop': 106331, 'tracts': 33},
'Ashland': {'pop': 53139, 'tracts': 11},
'Ashtabula': {'pop': 101497, 'tracts': 25},
'Athens': {'pop': 64757, 'tracts': 15},
'Auglaize': {'pop': 45949, 'tracts': 11},
'Belmont': {'pop': 70400, 'tracts': 20},
'Brown': {'pop': 44846, 'tracts': 9},
'Butler': {'pop': 368130, 'tracts': 80},
'Carroll': {'pop': 28836, 'tracts': 7},
'Champaign': {'pop': 40097, 'tracts': 10},
'Clark': {'pop': 138333, 'tracts': 44},
'Clermont': {'pop': 197363, 'tracts': 40},
'Clinton': {'pop': 42040, 'tracts': 9},
'Columbiana': {'pop': 107841, 'tracts': 24},
'Coshocton': {'pop': 36901, 'tracts': 10},
'Crawford': {'pop': 43784, 'tracts': 13},
'Cuyahoga': {'pop': 1280122, 'tracts': 447},
'Darke': {'pop': 52959, 'tracts': 12},
'Defiance': {'pop': 39037, 'tracts': 9},
'Delaware': {'pop': 174214, 'tracts': 35},
'Erie': {'pop': 77079, 'tracts': 19},
'Fairfield': {'pop': 146156, 'tracts': 28},
'Fayette': {'pop': 29030, 'tracts': 7},
'Franklin': {'pop': 1163414, 'tracts': 284},
'Fulton': {'pop': 42698, 'tracts': 9},
'Gallia': {'pop': 30934, 'tracts': 7},
'Geauga': {'pop': 93389, 'tracts': 21},
'Greene': {'pop': 161573, 'tracts': 35},
'Guernsey': {'pop': 40087, 'tracts': 10},
'Hamilton': {'pop': 802374, 'tracts': 222},
'Hancock': {'pop': 74782, 'tracts': 13},
'Hardin': {'pop': 32058, 'tracts': 7},
'Harrison': {'pop': 15864, 'tracts': 5},
'Henry': {'pop': 28215, 'tracts': 7},
'Highland': {'pop': 43589, 'tracts': 9},
'Hocking': {'pop': 29380, 'tracts': 7},
'Holmes': {'pop': 42366, 'tracts': 8},
'Huron': {'pop': 59626, 'tracts': 13},
'Jackson': {'pop': 33225, 'tracts': 7},
'Jefferson': {'pop': 69709, 'tracts': 23},
'Knox': {'pop': 60921, 'tracts': 12},
'Lake': {'pop': 230041, 'tracts': 59},
'Lawrence': {'pop': 62450, 'tracts': 16},
'Licking': {'pop': 166492, 'tracts': 32},
'Logan': {'pop': 45858, 'tracts': 11},
'Lorain': {'pop': 301356, 'tracts': 73},
'Lucas': {'pop': 441815, 'tracts': 127},
'Madison': {'pop': 43435, 'tracts': 12},
'Mahoning': {'pop': 238823, 'tracts': 70},
'Marion': {'pop': 66501, 'tracts': 18},
'Medina': {'pop': 172332, 'tracts': 37},
'Meigs': {'pop': 23770, 'tracts': 6},
'Mercer': {'pop': 40814, 'tracts': 9},
'Miami': {'pop': 102506, 'tracts': 21},
'Monroe': {'pop': 14642, 'tracts': 4},
'Montgomery': {'pop': 535153, 'tracts': 153},
'Morgan': {'pop': 15054, 'tracts': 4},
'Morrow': {'pop': 34827, 'tracts': 6},
'Muskingum': {'pop': 86074, 'tracts': 19},
'Noble': {'pop': 14645, 'tracts': 3},
'Ottawa': {'pop': 41428, 'tracts': 13},
'Paulding': {'pop': 19614, 'tracts': 5},
'Perry': {'pop': 36058, 'tracts': 6},
'Pickaway': {'pop': 55698, 'tracts': 13},
'Pike': {'pop': 28709, 'tracts': 6},
'Portage': {'pop': 161419, 'tracts': 35},
'Preble': {'pop': 42270, 'tracts': 12},
'Putnam': {'pop': 34499, 'tracts': 7},
'Richland': {'pop': 124475, 'tracts': 30},
'Ross': {'pop': 78064, 'tracts': 17},
'Sandusky': {'pop': 60944, 'tracts': 15},
'Scioto': {'pop': 79499, 'tracts': 20},
'Seneca': {'pop': 56745, 'tracts': 14},
'Shelby': {'pop': 49423, 'tracts': 10},
'Stark': {'pop': 375586, 'tracts': 86},
'Summit': {'pop': 541781, 'tracts': 135},
'Trumbull': {'pop': 210312, 'tracts': 55},
'Tuscarawas': {'pop': 92582, 'tracts': 21},
'Union': {'pop': 52300, 'tracts': 10},
'Van Wert': {'pop': 28744, 'tracts': 9},
'Vinton': {'pop': 13435, 'tracts': 3},
'Warren': {'pop': 212693, 'tracts': 33},
'Washington': {'pop': 61778, 'tracts': 16},
'Wayne': {'pop': 114520, 'tracts': 32},
'Williams': {'pop': 37642, 'tracts': 9},
'Wood': {'pop': 125488, 'tracts': 28},
'Wyandot': {'pop': 22615, 'tracts': 6}},
'OK': {'Adair': {'pop': 22683, 'tracts': 5},
'Alfalfa': {'pop': 5642, 'tracts': 3},
'Atoka': {'pop': 14182, 'tracts': 4},
'Beaver': {'pop': 5636, 'tracts': 3},
'Beckham': {'pop': 22119, 'tracts': 4},
'Blaine': {'pop': 11943, 'tracts': 5},
'Bryan': {'pop': 42416, 'tracts': 11},
'Caddo': {'pop': 29600, 'tracts': 8},
'Canadian': {'pop': 115541, 'tracts': 29},
'Carter': {'pop': 47557, 'tracts': 11},
'Cherokee': {'pop': 46987, 'tracts': 9},
'Choctaw': {'pop': 15205, 'tracts': 5},
'Cimarron': {'pop': 2475, 'tracts': 2},
'Cleveland': {'pop': 255755, 'tracts': 62},
'Coal': {'pop': 5925, 'tracts': 2},
'Comanche': {'pop': 124098, 'tracts': 32},
'Cotton': {'pop': 6193, 'tracts': 2},
'Craig': {'pop': 15029, 'tracts': 5},
'Creek': {'pop': 69967, 'tracts': 21},
'Custer': {'pop': 27469, 'tracts': 5},
'Delaware': {'pop': 41487, 'tracts': 9},
'Dewey': {'pop': 4810, 'tracts': 3},
'Ellis': {'pop': 4151, 'tracts': 2},
'Garfield': {'pop': 60580, 'tracts': 12},
'Garvin': {'pop': 27576, 'tracts': 9},
'Grady': {'pop': 52431, 'tracts': 10},
'Grant': {'pop': 4527, 'tracts': 2},
'Greer': {'pop': 6239, 'tracts': 2},
'Harmon': {'pop': 2922, 'tracts': 1},
'Harper': {'pop': 3685, 'tracts': 2},
'Haskell': {'pop': 12769, 'tracts': 4},
'Hughes': {'pop': 14003, 'tracts': 5},
'Jackson': {'pop': 26446, 'tracts': 8},
'Jefferson': {'pop': 6472, 'tracts': 3},
'Johnston': {'pop': 10957, 'tracts': 3},
'Kay': {'pop': 46562, 'tracts': 11},
'Kingfisher': {'pop': 15034, 'tracts': 4},
'Kiowa': {'pop': 9446, 'tracts': 3},
'Latimer': {'pop': 11154, 'tracts': 3},
'Le Flore': {'pop': 50384, 'tracts': 12},
'Lincoln': {'pop': 34273, 'tracts': 7},
'Logan': {'pop': 41848, 'tracts': 8},
'Love': {'pop': 9423, 'tracts': 3},
'Major': {'pop': 7527, 'tracts': 3},
'Marshall': {'pop': 15840, 'tracts': 4},
'Mayes': {'pop': 41259, 'tracts': 9},
'McClain': {'pop': 34506, 'tracts': 6},
'McCurtain': {'pop': 33151, 'tracts': 8},
'McIntosh': {'pop': 20252, 'tracts': 6},
'Murray': {'pop': 13488, 'tracts': 3},
'Muskogee': {'pop': 70990, 'tracts': 16},
'Noble': {'pop': 11561, 'tracts': 4},
'Nowata': {'pop': 10536, 'tracts': 4},
'Okfuskee': {'pop': 12191, 'tracts': 4},
'Oklahoma': {'pop': 718633, 'tracts': 241},
'Okmulgee': {'pop': 40069, 'tracts': 10},
'Osage': {'pop': 47472, 'tracts': 11},
'Ottawa': {'pop': 31848, 'tracts': 9},
'Pawnee': {'pop': 16577, 'tracts': 5},
'Payne': {'pop': 77350, 'tracts': 17},
'Pittsburg': {'pop': 45837, 'tracts': 13},
'Pontotoc': {'pop': 37492, 'tracts': 10},
'Pottawatomie': {'pop': 69442, 'tracts': 16},
'Pushmataha': {'pop': 11572, 'tracts': 3},
'Roger Mills': {'pop': 3647, 'tracts': 1},
'Rogers': {'pop': 86905, 'tracts': 28},
'Seminole': {'pop': 25482, 'tracts': 9},
'Sequoyah': {'pop': 42391, 'tracts': 9},
'Stephens': {'pop': 45048, 'tracts': 11},
'Texas': {'pop': 20640, 'tracts': 5},
'Tillman': {'pop': 7992, 'tracts': 5},
'Tulsa': {'pop': 603403, 'tracts': 175},
'Wagoner': {'pop': 73085, 'tracts': 22},
'Washington': {'pop': 50976, 'tracts': 13},
'Washita': {'pop': 11629, 'tracts': 4},
'Woods': {'pop': 8878, 'tracts': 3},
'Woodward': {'pop': 20081, 'tracts': 5}},
'OR': {'Baker': {'pop': 16134, 'tracts': 6},
'Benton': {'pop': 85579, 'tracts': 18},
'Clackamas': {'pop': 375992, 'tracts': 80},
'Clatsop': {'pop': 37039, 'tracts': 12},
'Columbia': {'pop': 49351, 'tracts': 10},
'Coos': {'pop': 63043, 'tracts': 13},
'Crook': {'pop': 20978, 'tracts': 4},
'Curry': {'pop': 22364, 'tracts': 6},
'Deschutes': {'pop': 157733, 'tracts': 24},
'Douglas': {'pop': 107667, 'tracts': 22},
'Gilliam': {'pop': 1871, 'tracts': 1},
'Grant': {'pop': 7445, 'tracts': 2},
'Harney': {'pop': 7422, 'tracts': 2},
'Hood River': {'pop': 22346, 'tracts': 4},
'Jackson': {'pop': 203206, 'tracts': 41},
'Jefferson': {'pop': 21720, 'tracts': 6},
'Josephine': {'pop': 82713, 'tracts': 16},
'Klamath': {'pop': 66380, 'tracts': 20},
'Lake': {'pop': 7895, 'tracts': 2},
'Lane': {'pop': 351715, 'tracts': 86},
'Lincoln': {'pop': 46034, 'tracts': 18},
'Linn': {'pop': 116672, 'tracts': 21},
'Malheur': {'pop': 31313, 'tracts': 8},
'Marion': {'pop': 315335, 'tracts': 58},
'Morrow': {'pop': 11173, 'tracts': 2},
'Multnomah': {'pop': 735334, 'tracts': 171},
'Polk': {'pop': 75403, 'tracts': 12},
'Sherman': {'pop': 1765, 'tracts': 1},
'Tillamook': {'pop': 25250, 'tracts': 8},
'Umatilla': {'pop': 75889, 'tracts': 15},
'Union': {'pop': 25748, 'tracts': 8},
'Wallowa': {'pop': 7008, 'tracts': 3},
'Wasco': {'pop': 25213, 'tracts': 8},
'Washington': {'pop': 529710, 'tracts': 104},
'Wheeler': {'pop': 1441, 'tracts': 1},
'Yamhill': {'pop': 99193, 'tracts': 17}},
'PA': {'Adams': {'pop': 101407, 'tracts': 23},
'Allegheny': {'pop': 1223348, 'tracts': 402},
'Armstrong': {'pop': 68941, 'tracts': 19},
'Beaver': {'pop': 170539, 'tracts': 51},
'Bedford': {'pop': 49762, 'tracts': 11},
'Berks': {'pop': 411442, 'tracts': 90},
'Blair': {'pop': 127089, 'tracts': 34},
'Bradford': {'pop': 62622, 'tracts': 14},
'Bucks': {'pop': 625249, 'tracts': 143},
'Butler': {'pop': 183862, 'tracts': 44},
'Cambria': {'pop': 143679, 'tracts': 42},
'Cameron': {'pop': 5085, 'tracts': 2},
'Carbon': {'pop': 65249, 'tracts': 12},
'Centre': {'pop': 153990, 'tracts': 31},
'Chester': {'pop': 498886, 'tracts': 116},
'Clarion': {'pop': 39988, 'tracts': 10},
'Clearfield': {'pop': 81642, 'tracts': 20},
'Clinton': {'pop': 39238, 'tracts': 9},
'Columbia': {'pop': 67295, 'tracts': 15},
'Crawford': {'pop': 88765, 'tracts': 23},
'Cumberland': {'pop': 235406, 'tracts': 49},
'Dauphin': {'pop': 268100, 'tracts': 65},
'Delaware': {'pop': 558979, 'tracts': 144},
'Elk': {'pop': 31946, 'tracts': 9},
'Erie': {'pop': 280566, 'tracts': 72},
'Fayette': {'pop': 136606, 'tracts': 36},
'Forest': {'pop': 7716, 'tracts': 3},
'Franklin': {'pop': 149618, 'tracts': 27},
'Fulton': {'pop': 14845, 'tracts': 3},
'Greene': {'pop': 38686, 'tracts': 9},
'Huntingdon': {'pop': 45913, 'tracts': 12},
'Indiana': {'pop': 88880, 'tracts': 23},
'Jefferson': {'pop': 45200, 'tracts': 13},
'Juniata': {'pop': 24636, 'tracts': 5},
'Lackawanna': {'pop': 214437, 'tracts': 59},
'Lancaster': {'pop': 519445, 'tracts': 98},
'Lawrence': {'pop': 91108, 'tracts': 28},
'Lebanon': {'pop': 133568, 'tracts': 31},
'Lehigh': {'pop': 349497, 'tracts': 76},
'Luzerne': {'pop': 320918, 'tracts': 104},
'Lycoming': {'pop': 116111, 'tracts': 29},
'McKean': {'pop': 43450, 'tracts': 12},
'Mercer': {'pop': 116638, 'tracts': 30},
'Mifflin': {'pop': 46682, 'tracts': 12},
'Monroe': {'pop': 169842, 'tracts': 33},
'Montgomery': {'pop': 799874, 'tracts': 211},
'Montour': {'pop': 18267, 'tracts': 4},
'Northampton': {'pop': 297735, 'tracts': 68},
'Northumberland': {'pop': 94528, 'tracts': 24},
'Perry': {'pop': 45969, 'tracts': 10},
'Philadelphia': {'pop': 1526006, 'tracts': 384},
'Pike': {'pop': 57369, 'tracts': 18},
'Potter': {'pop': 17457, 'tracts': 5},
'Schuylkill': {'pop': 148289, 'tracts': 40},
'Snyder': {'pop': 39702, 'tracts': 8},
'Somerset': {'pop': 77742, 'tracts': 21},
'Sullivan': {'pop': 6428, 'tracts': 2},
'Susquehanna': {'pop': 43356, 'tracts': 11},
'Tioga': {'pop': 41981, 'tracts': 10},
'Union': {'pop': 44947, 'tracts': 10},
'Venango': {'pop': 54984, 'tracts': 16},
'Warren': {'pop': 41815, 'tracts': 13},
'Washington': {'pop': 207820, 'tracts': 59},
'Wayne': {'pop': 52822, 'tracts': 14},
'Westmoreland': {'pop': 365169, 'tracts': 100},
'Wyoming': {'pop': 28276, 'tracts': 7},
'York': {'pop': 434972, 'tracts': 90}},
'RI': {'Bristol': {'pop': 49875, 'tracts': 11},
'Kent': {'pop': 166158, 'tracts': 39},
'Newport': {'pop': 82888, 'tracts': 22},
'Providence': {'pop': 626667, 'tracts': 141},
'Washington': {'pop': 126979, 'tracts': 29}},
'SC': {'Abbeville': {'pop': 25417, 'tracts': 6},
'Aiken': {'pop': 160099, 'tracts': 33},
'Allendale': {'pop': 10419, 'tracts': 3},
'Anderson': {'pop': 187126, 'tracts': 39},
'Bamberg': {'pop': 15987, 'tracts': 4},
'Barnwell': {'pop': 22621, 'tracts': 6},
'Beaufort': {'pop': 162233, 'tracts': 41},
'Berkeley': {'pop': 177843, 'tracts': 45},
'Calhoun': {'pop': 15175, 'tracts': 3},
'Charleston': {'pop': 350209, 'tracts': 86},
'Cherokee': {'pop': 55342, 'tracts': 13},
'Chester': {'pop': 33140, 'tracts': 11},
'Chesterfield': {'pop': 46734, 'tracts': 10},
'Clarendon': {'pop': 34971, 'tracts': 12},
'Colleton': {'pop': 38892, 'tracts': 10},
'Darlington': {'pop': 68681, 'tracts': 16},
'Dillon': {'pop': 32062, 'tracts': 6},
'Dorchester': {'pop': 136555, 'tracts': 25},
'Edgefield': {'pop': 26985, 'tracts': 6},
'Fairfield': {'pop': 23956, 'tracts': 5},
'Florence': {'pop': 136885, 'tracts': 33},
'Georgetown': {'pop': 60158, 'tracts': 15},
'Greenville': {'pop': 451225, 'tracts': 111},
'Greenwood': {'pop': 69661, 'tracts': 14},
'Hampton': {'pop': 21090, 'tracts': 5},
'Horry': {'pop': 269291, 'tracts': 72},
'Jasper': {'pop': 24777, 'tracts': 5},
'Kershaw': {'pop': 61697, 'tracts': 15},
'Lancaster': {'pop': 76652, 'tracts': 14},
'Laurens': {'pop': 66537, 'tracts': 17},
'Lee': {'pop': 19220, 'tracts': 7},
'Lexington': {'pop': 262391, 'tracts': 74},
'Marion': {'pop': 33062, 'tracts': 8},
'Marlboro': {'pop': 28933, 'tracts': 7},
'McCormick': {'pop': 10233, 'tracts': 3},
'Newberry': {'pop': 37508, 'tracts': 8},
'Oconee': {'pop': 74273, 'tracts': 15},
'Orangeburg': {'pop': 92501, 'tracts': 20},
'Pickens': {'pop': 119224, 'tracts': 28},
'Richland': {'pop': 384504, 'tracts': 89},
'Saluda': {'pop': 19875, 'tracts': 5},
'Spartanburg': {'pop': 284307, 'tracts': 69},
'Sumter': {'pop': 107456, 'tracts': 23},
'Union': {'pop': 28961, 'tracts': 9},
'Williamsburg': {'pop': 34423, 'tracts': 11},
'York': {'pop': 226073, 'tracts': 46}},
'SD': {'Aurora': {'pop': 2710, 'tracts': 1},
'Beadle': {'pop': 17398, 'tracts': 6},
'Bennett': {'pop': 3431, 'tracts': 2},
'Bon Homme': {'pop': 7070, 'tracts': 2},
'Brookings': {'pop': 31965, 'tracts': 6},
'Brown': {'pop': 36531, 'tracts': 8},
'Brule': {'pop': 5255, 'tracts': 2},
'Buffalo': {'pop': 1912, 'tracts': 1},
'Butte': {'pop': 10110, 'tracts': 2},
'Campbell': {'pop': 1466, 'tracts': 1},
'Charles Mix': {'pop': 9129, 'tracts': 3},
'Clark': {'pop': 3691, 'tracts': 1},
'Clay': {'pop': 13864, 'tracts': 3},
'Codington': {'pop': 27227, 'tracts': 7},
'Corson': {'pop': 4050, 'tracts': 2},
'Custer': {'pop': 8216, 'tracts': 2},
'Davison': {'pop': 19504, 'tracts': 4},
'Day': {'pop': 5710, 'tracts': 3},
'Deuel': {'pop': 4364, 'tracts': 2},
'Dewey': {'pop': 5301, 'tracts': 2},
'Douglas': {'pop': 3002, 'tracts': 1},
'Edmunds': {'pop': 4071, 'tracts': 2},
'Fall River': {'pop': 7094, 'tracts': 2},
'Faulk': {'pop': 2364, 'tracts': 1},
'Grant': {'pop': 7356, 'tracts': 2},
'Gregory': {'pop': 4271, 'tracts': 2},
'Haakon': {'pop': 1937, 'tracts': 1},
'Hamlin': {'pop': 5903, 'tracts': 2},
'Hand': {'pop': 3431, 'tracts': 2},
'Hanson': {'pop': 3331, 'tracts': 1},
'Harding': {'pop': 1255, 'tracts': 1},
'Hughes': {'pop': 17022, 'tracts': 4},
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'Hyde': {'pop': 1420, 'tracts': 1},
'Jackson': {'pop': 3031, 'tracts': 2},
'Jerauld': {'pop': 2071, 'tracts': 1},
'Jones': {'pop': 1006, 'tracts': 1},
'Kingsbury': {'pop': 5148, 'tracts': 2},
'Lake': {'pop': 11200, 'tracts': 3},
'Lawrence': {'pop': 24097, 'tracts': 5},
'Lincoln': {'pop': 44828, 'tracts': 11},
'Lyman': {'pop': 3755, 'tracts': 2},
'Marshall': {'pop': 4656, 'tracts': 1},
'McCook': {'pop': 5618, 'tracts': 2},
'McPherson': {'pop': 2459, 'tracts': 1},
'Meade': {'pop': 25434, 'tracts': 5},
'Mellette': {'pop': 2048, 'tracts': 1},
'Miner': {'pop': 2389, 'tracts': 1},
'Minnehaha': {'pop': 169468, 'tracts': 42},
'Moody': {'pop': 6486, 'tracts': 2},
'Pennington': {'pop': 100948, 'tracts': 23},
'Perkins': {'pop': 2982, 'tracts': 1},
'Potter': {'pop': 2329, 'tracts': 1},
'Roberts': {'pop': 10149, 'tracts': 4},
'Sanborn': {'pop': 2355, 'tracts': 1},
'Shannon': {'pop': 13586, 'tracts': 3},
'Spink': {'pop': 6415, 'tracts': 3},
'Stanley': {'pop': 2966, 'tracts': 1},
'Sully': {'pop': 1373, 'tracts': 1},
'Todd': {'pop': 9612, 'tracts': 2},
'Tripp': {'pop': 5644, 'tracts': 2},
'Turner': {'pop': 8347, 'tracts': 2},
'Union': {'pop': 14399, 'tracts': 3},
'Walworth': {'pop': 5438, 'tracts': 2},
'Yankton': {'pop': 22438, 'tracts': 5},
'Ziebach': {'pop': 2801, 'tracts': 1}},
'TN': {'Anderson': {'pop': 75129, 'tracts': 18},
'Bedford': {'pop': 45058, 'tracts': 9},
'Benton': {'pop': 16489, 'tracts': 5},
'Bledsoe': {'pop': 12876, 'tracts': 3},
'Blount': {'pop': 123010, 'tracts': 28},
'Bradley': {'pop': 98963, 'tracts': 19},
'Campbell': {'pop': 40716, 'tracts': 11},
'Cannon': {'pop': 13801, 'tracts': 3},
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'Carter': {'pop': 57424, 'tracts': 17},
'Cheatham': {'pop': 39105, 'tracts': 9},
'Chester': {'pop': 17131, 'tracts': 3},
'Claiborne': {'pop': 32213, 'tracts': 9},
'Clay': {'pop': 7861, 'tracts': 2},
'Cocke': {'pop': 35662, 'tracts': 9},
'Coffee': {'pop': 52796, 'tracts': 12},
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'Cumberland': {'pop': 56053, 'tracts': 14},
'Davidson': {'pop': 626681, 'tracts': 161},
'DeKalb': {'pop': 18723, 'tracts': 4},
'Decatur': {'pop': 11757, 'tracts': 4},
'Dickson': {'pop': 49666, 'tracts': 10},
'Dyer': {'pop': 38335, 'tracts': 8},
'Fayette': {'pop': 38413, 'tracts': 11},
'Fentress': {'pop': 17959, 'tracts': 4},
'Franklin': {'pop': 41052, 'tracts': 9},
'Gibson': {'pop': 49683, 'tracts': 14},
'Giles': {'pop': 29485, 'tracts': 8},
'Grainger': {'pop': 22657, 'tracts': 5},
'Greene': {'pop': 68831, 'tracts': 15},
'Grundy': {'pop': 13703, 'tracts': 4},
'Hamblen': {'pop': 62544, 'tracts': 12},
'Hamilton': {'pop': 336463, 'tracts': 82},
'Hancock': {'pop': 6819, 'tracts': 2},
'Hardeman': {'pop': 27253, 'tracts': 6},
'Hardin': {'pop': 26026, 'tracts': 6},
'Hawkins': {'pop': 56833, 'tracts': 13},
'Haywood': {'pop': 18787, 'tracts': 6},
'Henderson': {'pop': 27769, 'tracts': 6},
'Henry': {'pop': 32330, 'tracts': 9},
'Hickman': {'pop': 24690, 'tracts': 6},
'Houston': {'pop': 8426, 'tracts': 3},
'Humphreys': {'pop': 18538, 'tracts': 5},
'Jackson': {'pop': 11638, 'tracts': 4},
'Jefferson': {'pop': 51407, 'tracts': 9},
'Johnson': {'pop': 18244, 'tracts': 5},
'Knox': {'pop': 432226, 'tracts': 112},
'Lake': {'pop': 7832, 'tracts': 2},
'Lauderdale': {'pop': 27815, 'tracts': 9},
'Lawrence': {'pop': 41869, 'tracts': 11},
'Lewis': {'pop': 12161, 'tracts': 2},
'Lincoln': {'pop': 33361, 'tracts': 9},
'Loudon': {'pop': 48556, 'tracts': 10},
'Macon': {'pop': 22248, 'tracts': 4},
'Madison': {'pop': 98294, 'tracts': 27},
'Marion': {'pop': 28237, 'tracts': 6},
'Marshall': {'pop': 30617, 'tracts': 6},
'Maury': {'pop': 80956, 'tracts': 17},
'McMinn': {'pop': 52266, 'tracts': 10},
'McNairy': {'pop': 26075, 'tracts': 7},
'Meigs': {'pop': 11753, 'tracts': 3},
'Monroe': {'pop': 44519, 'tracts': 7},
'Montgomery': {'pop': 172331, 'tracts': 39},
'Moore': {'pop': 6362, 'tracts': 2},
'Morgan': {'pop': 21987, 'tracts': 5},
'Obion': {'pop': 31807, 'tracts': 10},
'Overton': {'pop': 22083, 'tracts': 7},
'Perry': {'pop': 7915, 'tracts': 2},
'Pickett': {'pop': 5077, 'tracts': 1},
'Polk': {'pop': 16825, 'tracts': 5},
'Putnam': {'pop': 72321, 'tracts': 15},
'Rhea': {'pop': 31809, 'tracts': 6},
'Roane': {'pop': 54181, 'tracts': 11},
'Robertson': {'pop': 66283, 'tracts': 14},
'Rutherford': {'pop': 262604, 'tracts': 49},
'Scott': {'pop': 22228, 'tracts': 5},
'Sequatchie': {'pop': 14112, 'tracts': 3},
'Sevier': {'pop': 89889, 'tracts': 18},
'Shelby': {'pop': 927644, 'tracts': 221},
'Smith': {'pop': 19166, 'tracts': 5},
'Stewart': {'pop': 13324, 'tracts': 5},
'Sullivan': {'pop': 156823, 'tracts': 39},
'Sumner': {'pop': 160645, 'tracts': 42},
'Tipton': {'pop': 61081, 'tracts': 13},
'Trousdale': {'pop': 7870, 'tracts': 2},
'Unicoi': {'pop': 18313, 'tracts': 4},
'Union': {'pop': 19109, 'tracts': 4},
'Van Buren': {'pop': 5548, 'tracts': 2},
'Warren': {'pop': 39839, 'tracts': 9},
'Washington': {'pop': 122979, 'tracts': 23},
'Wayne': {'pop': 17021, 'tracts': 4},
'Weakley': {'pop': 35021, 'tracts': 11},
'White': {'pop': 25841, 'tracts': 6},
'Williamson': {'pop': 183182, 'tracts': 37},
'Wilson': {'pop': 113993, 'tracts': 21}},
'TX': {'Anderson': {'pop': 58458, 'tracts': 11},
'Andrews': {'pop': 14786, 'tracts': 4},
'Angelina': {'pop': 86771, 'tracts': 17},
'Aransas': {'pop': 23158, 'tracts': 5},
'Archer': {'pop': 9054, 'tracts': 3},
'Armstrong': {'pop': 1901, 'tracts': 1},
'Atascosa': {'pop': 44911, 'tracts': 8},
'Austin': {'pop': 28417, 'tracts': 6},
'Bailey': {'pop': 7165, 'tracts': 1},
'Bandera': {'pop': 20485, 'tracts': 5},
'Bastrop': {'pop': 74171, 'tracts': 10},
'Baylor': {'pop': 3726, 'tracts': 1},
'Bee': {'pop': 31861, 'tracts': 7},
'Bell': {'pop': 310235, 'tracts': 65},
'Bexar': {'pop': 1714773, 'tracts': 366},
'Blanco': {'pop': 10497, 'tracts': 2},
'Borden': {'pop': 641, 'tracts': 1},
'Bosque': {'pop': 18212, 'tracts': 7},
'Bowie': {'pop': 92565, 'tracts': 18},
'Brazoria': {'pop': 313166, 'tracts': 51},
'Brazos': {'pop': 194851, 'tracts': 42},
'Brewster': {'pop': 9232, 'tracts': 3},
'Briscoe': {'pop': 1637, 'tracts': 1},
'Brooks': {'pop': 7223, 'tracts': 2},
'Brown': {'pop': 38106, 'tracts': 12},
'Burleson': {'pop': 17187, 'tracts': 5},
'Burnet': {'pop': 42750, 'tracts': 8},
'Caldwell': {'pop': 38066, 'tracts': 8},
'Calhoun': {'pop': 21381, 'tracts': 6},
'Callahan': {'pop': 13544, 'tracts': 3},
'Cameron': {'pop': 406220, 'tracts': 86},
'Camp': {'pop': 12401, 'tracts': 3},
'Carson': {'pop': 6182, 'tracts': 2},
'Cass': {'pop': 30464, 'tracts': 7},
'Castro': {'pop': 8062, 'tracts': 3},
'Chambers': {'pop': 35096, 'tracts': 6},
'Cherokee': {'pop': 50845, 'tracts': 12},
'Childress': {'pop': 7041, 'tracts': 2},
'Clay': {'pop': 10752, 'tracts': 3},
'Cochran': {'pop': 3127, 'tracts': 1},
'Coke': {'pop': 3320, 'tracts': 2},
'Coleman': {'pop': 8895, 'tracts': 3},
'Collin': {'pop': 782341, 'tracts': 152},
'Collingsworth': {'pop': 3057, 'tracts': 1},
'Colorado': {'pop': 20874, 'tracts': 5},
'Comal': {'pop': 108472, 'tracts': 24},
'Comanche': {'pop': 13974, 'tracts': 4},
'Concho': {'pop': 4087, 'tracts': 1},
'Cooke': {'pop': 38437, 'tracts': 8},
'Coryell': {'pop': 75388, 'tracts': 19},
'Cottle': {'pop': 1505, 'tracts': 1},
'Crane': {'pop': 4375, 'tracts': 1},
'Crockett': {'pop': 3719, 'tracts': 1},
'Crosby': {'pop': 6059, 'tracts': 3},
'Culberson': {'pop': 2398, 'tracts': 1},
'Dallam': {'pop': 6703, 'tracts': 2},
'Dallas': {'pop': 2368139, 'tracts': 529},
'Dawson': {'pop': 13833, 'tracts': 4},
'DeWitt': {'pop': 20097, 'tracts': 5},
'Deaf Smith': {'pop': 19372, 'tracts': 4},
'Delta': {'pop': 5231, 'tracts': 2},
'Denton': {'pop': 662614, 'tracts': 137},
'Dickens': {'pop': 2444, 'tracts': 1},
'Dimmit': {'pop': 9996, 'tracts': 2},
'Donley': {'pop': 3677, 'tracts': 2},
'Duval': {'pop': 11782, 'tracts': 3},
'Eastland': {'pop': 18583, 'tracts': 5},
'Ector': {'pop': 137130, 'tracts': 28},
'Edwards': {'pop': 2002, 'tracts': 1},
'El Paso': {'pop': 800647, 'tracts': 161},
'Ellis': {'pop': 149610, 'tracts': 31},
'Erath': {'pop': 37890, 'tracts': 8},
'Falls': {'pop': 17866, 'tracts': 6},
'Fannin': {'pop': 33915, 'tracts': 9},
'Fayette': {'pop': 24554, 'tracts': 7},
'Fisher': {'pop': 3974, 'tracts': 2},
'Floyd': {'pop': 6446, 'tracts': 2},
'Foard': {'pop': 1336, 'tracts': 1},
'Fort Bend': {'pop': 585375, 'tracts': 76},
'Franklin': {'pop': 10605, 'tracts': 3},
'Freestone': {'pop': 19816, 'tracts': 7},
'Frio': {'pop': 17217, 'tracts': 3},
'Gaines': {'pop': 17526, 'tracts': 3},
'Galveston': {'pop': 291309, 'tracts': 67},
'Garza': {'pop': 6461, 'tracts': 1},
'Gillespie': {'pop': 24837, 'tracts': 5},
'Glasscock': {'pop': 1226, 'tracts': 1},
'Goliad': {'pop': 7210, 'tracts': 2},
'Gonzales': {'pop': 19807, 'tracts': 6},
'Gray': {'pop': 22535, 'tracts': 7},
'Grayson': {'pop': 120877, 'tracts': 26},
'Gregg': {'pop': 121730, 'tracts': 25},
'Grimes': {'pop': 26604, 'tracts': 6},
'Guadalupe': {'pop': 131533, 'tracts': 29},
'Hale': {'pop': 36273, 'tracts': 9},
'Hall': {'pop': 3353, 'tracts': 1},
'Hamilton': {'pop': 8517, 'tracts': 3},
'Hansford': {'pop': 5613, 'tracts': 2},
'Hardeman': {'pop': 4139, 'tracts': 1},
'Hardin': {'pop': 54635, 'tracts': 11},
'Harris': {'pop': 4092459, 'tracts': 786},
'Harrison': {'pop': 65631, 'tracts': 14},
'Hartley': {'pop': 6062, 'tracts': 1},
'Haskell': {'pop': 5899, 'tracts': 2},
'Hays': {'pop': 157107, 'tracts': 25},
'Hemphill': {'pop': 3807, 'tracts': 1},
'Henderson': {'pop': 78532, 'tracts': 17},
'Hidalgo': {'pop': 774769, 'tracts': 113},
'Hill': {'pop': 35089, 'tracts': 11},
'Hockley': {'pop': 22935, 'tracts': 7},
'Hood': {'pop': 51182, 'tracts': 10},
'Hopkins': {'pop': 35161, 'tracts': 9},
'Houston': {'pop': 23732, 'tracts': 7},
'Howard': {'pop': 35012, 'tracts': 10},
'Hudspeth': {'pop': 3476, 'tracts': 1},
'Hunt': {'pop': 86129, 'tracts': 19},
'Hutchinson': {'pop': 22150, 'tracts': 7},
'Irion': {'pop': 1599, 'tracts': 1},
'Jack': {'pop': 9044, 'tracts': 3},
'Jackson': {'pop': 14075, 'tracts': 3},
'Jasper': {'pop': 35710, 'tracts': 8},
'Jeff Davis': {'pop': 2342, 'tracts': 1},
'Jefferson': {'pop': 252273, 'tracts': 72},
'Jim Hogg': {'pop': 5300, 'tracts': 2},
'Jim Wells': {'pop': 40838, 'tracts': 7},
'Johnson': {'pop': 150934, 'tracts': 28},
'Jones': {'pop': 20202, 'tracts': 6},
'Karnes': {'pop': 14824, 'tracts': 4},
'Kaufman': {'pop': 103350, 'tracts': 18},
'Kendall': {'pop': 33410, 'tracts': 6},
'Kenedy': {'pop': 416, 'tracts': 1},
'Kent': {'pop': 808, 'tracts': 1},
'Kerr': {'pop': 49625, 'tracts': 10},
'Kimble': {'pop': 4607, 'tracts': 2},
'King': {'pop': 286, 'tracts': 1},
'Kinney': {'pop': 3598, 'tracts': 1},
'Kleberg': {'pop': 32061, 'tracts': 6},
'Knox': {'pop': 3719, 'tracts': 2},
'La Salle': {'pop': 6886, 'tracts': 1},
'Lamar': {'pop': 49793, 'tracts': 12},
'Lamb': {'pop': 13977, 'tracts': 5},
'Lampasas': {'pop': 19677, 'tracts': 5},
'Lavaca': {'pop': 19263, 'tracts': 6},
'Lee': {'pop': 16612, 'tracts': 4},
'Leon': {'pop': 16801, 'tracts': 3},
'Liberty': {'pop': 75643, 'tracts': 14},
'Limestone': {'pop': 23384, 'tracts': 8},
'Lipscomb': {'pop': 3302, 'tracts': 2},
'Live Oak': {'pop': 11531, 'tracts': 4},
'Llano': {'pop': 19301, 'tracts': 6},
'Loving': {'pop': 82, 'tracts': 1},
'Lubbock': {'pop': 278831, 'tracts': 68},
'Lynn': {'pop': 5915, 'tracts': 3},
'Madison': {'pop': 13664, 'tracts': 4},
'Marion': {'pop': 10546, 'tracts': 4},
'Martin': {'pop': 4799, 'tracts': 2},
'Mason': {'pop': 4012, 'tracts': 2},
'Matagorda': {'pop': 36702, 'tracts': 10},
'Maverick': {'pop': 54258, 'tracts': 9},
'McCulloch': {'pop': 8283, 'tracts': 3},
'McLennan': {'pop': 234906, 'tracts': 51},
'McMullen': {'pop': 707, 'tracts': 1},
'Medina': {'pop': 46006, 'tracts': 8},
'Menard': {'pop': 2242, 'tracts': 1},
'Midland': {'pop': 136872, 'tracts': 27},
'Milam': {'pop': 24757, 'tracts': 7},
'Mills': {'pop': 4936, 'tracts': 2},
'Mitchell': {'pop': 9403, 'tracts': 2},
'Montague': {'pop': 19719, 'tracts': 6},
'Montgomery': {'pop': 455746, 'tracts': 59},
'Moore': {'pop': 21904, 'tracts': 4},
'Morris': {'pop': 12934, 'tracts': 3},
'Motley': {'pop': 1210, 'tracts': 1},
'Nacogdoches': {'pop': 64524, 'tracts': 13},
'Navarro': {'pop': 47735, 'tracts': 10},
'Newton': {'pop': 14445, 'tracts': 4},
'Nolan': {'pop': 15216, 'tracts': 5},
'Nueces': {'pop': 340223, 'tracts': 81},
'Ochiltree': {'pop': 10223, 'tracts': 3},
'Oldham': {'pop': 2052, 'tracts': 1},
'Orange': {'pop': 81837, 'tracts': 21},
'Palo Pinto': {'pop': 28111, 'tracts': 9},
'Panola': {'pop': 23796, 'tracts': 6},
'Parker': {'pop': 116927, 'tracts': 19},
'Parmer': {'pop': 10269, 'tracts': 2},
'Pecos': {'pop': 15507, 'tracts': 4},
'Polk': {'pop': 45413, 'tracts': 10},
'Potter': {'pop': 121073, 'tracts': 34},
'Presidio': {'pop': 7818, 'tracts': 2},
'Rains': {'pop': 10914, 'tracts': 2},
'Randall': {'pop': 120725, 'tracts': 29},
'Reagan': {'pop': 3367, 'tracts': 1},
'Real': {'pop': 3309, 'tracts': 1},
'Red River': {'pop': 12860, 'tracts': 4},
'Reeves': {'pop': 13783, 'tracts': 5},
'Refugio': {'pop': 7383, 'tracts': 2},
'Roberts': {'pop': 929, 'tracts': 1},
'Robertson': {'pop': 16622, 'tracts': 5},
'Rockwall': {'pop': 78337, 'tracts': 11},
'Runnels': {'pop': 10501, 'tracts': 4},
'Rusk': {'pop': 53330, 'tracts': 13},
'Sabine': {'pop': 10834, 'tracts': 3},
'San Augustine': {'pop': 8865, 'tracts': 3},
'San Jacinto': {'pop': 26384, 'tracts': 4},
'San Patricio': {'pop': 64804, 'tracts': 16},
'San Saba': {'pop': 6131, 'tracts': 2},
'Schleicher': {'pop': 3461, 'tracts': 1},
'Scurry': {'pop': 16921, 'tracts': 4},
'Shackelford': {'pop': 3378, 'tracts': 1},
'Shelby': {'pop': 25448, 'tracts': 6},
'Sherman': {'pop': 3034, 'tracts': 1},
'Smith': {'pop': 209714, 'tracts': 41},
'Somervell': {'pop': 8490, 'tracts': 2},
'Starr': {'pop': 60968, 'tracts': 15},
'Stephens': {'pop': 9630, 'tracts': 3},
'Sterling': {'pop': 1143, 'tracts': 1},
'Stonewall': {'pop': 1490, 'tracts': 1},
'Sutton': {'pop': 4128, 'tracts': 1},
'Swisher': {'pop': 7854, 'tracts': 3},
'Tarrant': {'pop': 1809034, 'tracts': 357},
'Taylor': {'pop': 131506, 'tracts': 38},
'Terrell': {'pop': 984, 'tracts': 1},
'Terry': {'pop': 12651, 'tracts': 3},
'Throckmorton': {'pop': 1641, 'tracts': 1},
'Titus': {'pop': 32334, 'tracts': 8},
'Tom Green': {'pop': 110224, 'tracts': 25},
'Travis': {'pop': 1024266, 'tracts': 218},
'Trinity': {'pop': 14585, 'tracts': 5},
'Tyler': {'pop': 21766, 'tracts': 5},
'Upshur': {'pop': 39309, 'tracts': 7},
'Upton': {'pop': 3355, 'tracts': 2},
'Uvalde': {'pop': 26405, 'tracts': 5},
'Val Verde': {'pop': 48879, 'tracts': 10},
'Van Zandt': {'pop': 52579, 'tracts': 10},
'Victoria': {'pop': 86793, 'tracts': 23},
'Walker': {'pop': 67861, 'tracts': 10},
'Waller': {'pop': 43205, 'tracts': 6},
'Ward': {'pop': 10658, 'tracts': 3},
'Washington': {'pop': 33718, 'tracts': 6},
'Webb': {'pop': 250304, 'tracts': 61},
'Wharton': {'pop': 41280, 'tracts': 11},
'Wheeler': {'pop': 5410, 'tracts': 2},
'Wichita': {'pop': 131500, 'tracts': 37},
'Wilbarger': {'pop': 13535, 'tracts': 4},
'Willacy': {'pop': 22134, 'tracts': 6},
'Williamson': {'pop': 422679, 'tracts': 89},
'Wilson': {'pop': 42918, 'tracts': 11},
'Winkler': {'pop': 7110, 'tracts': 3},
'Wise': {'pop': 59127, 'tracts': 11},
'Wood': {'pop': 41964, 'tracts': 10},
'Yoakum': {'pop': 7879, 'tracts': 2},
'Young': {'pop': 18550, 'tracts': 4},
'Zapata': {'pop': 14018, 'tracts': 3},
'Zavala': {'pop': 11677, 'tracts': 4}},
'UT': {'Beaver': {'pop': 6629, 'tracts': 2},
'Box Elder': {'pop': 49975, 'tracts': 11},
'Cache': {'pop': 112656, 'tracts': 26},
'Carbon': {'pop': 21403, 'tracts': 5},
'Daggett': {'pop': 1059, 'tracts': 1},
'Davis': {'pop': 306479, 'tracts': 54},
'Duchesne': {'pop': 18607, 'tracts': 3},
'Emery': {'pop': 10976, 'tracts': 3},
'Garfield': {'pop': 5172, 'tracts': 2},
'Grand': {'pop': 9225, 'tracts': 2},
'Iron': {'pop': 46163, 'tracts': 8},
'Juab': {'pop': 10246, 'tracts': 2},
'Kane': {'pop': 7125, 'tracts': 2},
'Millard': {'pop': 12503, 'tracts': 3},
'Morgan': {'pop': 9469, 'tracts': 2},
'Piute': {'pop': 1556, 'tracts': 1},
'Rich': {'pop': 2264, 'tracts': 1},
'Salt Lake': {'pop': 1029655, 'tracts': 212},
'San Juan': {'pop': 14746, 'tracts': 4},
'Sanpete': {'pop': 27822, 'tracts': 5},
'Sevier': {'pop': 20802, 'tracts': 5},
'Summit': {'pop': 36324, 'tracts': 13},
'Tooele': {'pop': 58218, 'tracts': 11},
'Uintah': {'pop': 32588, 'tracts': 6},
'Utah': {'pop': 516564, 'tracts': 128},
'Wasatch': {'pop': 23530, 'tracts': 4},
'Washington': {'pop': 138115, 'tracts': 21},
'Wayne': {'pop': 2778, 'tracts': 1},
'Weber': {'pop': 231236, 'tracts': 50}},
'VA': {'Accomack': {'pop': 33164, 'tracts': 11},
'Albemarle': {'pop': 98970, 'tracts': 22},
'Alexandria': {'pop': 139966, 'tracts': 38},
'Alleghany': {'pop': 16250, 'tracts': 6},
'Amelia': {'pop': 12690, 'tracts': 2},
'Amherst': {'pop': 32353, 'tracts': 9},
'Appomattox': {'pop': 14973, 'tracts': 3},
'Arlington': {'pop': 207627, 'tracts': 59},
'Augusta': {'pop': 73750, 'tracts': 13},
'Bath': {'pop': 4731, 'tracts': 1},
'Bedford': {'pop': 68676, 'tracts': 16},
'Bedford City': {'pop': 6222, 'tracts': 1},
'Bland': {'pop': 6824, 'tracts': 2},
'Botetourt': {'pop': 33148, 'tracts': 8},
'Bristol': {'pop': 17835, 'tracts': 4},
'Brunswick': {'pop': 17434, 'tracts': 5},
'Buchanan': {'pop': 24098, 'tracts': 7},
'Buckingham': {'pop': 17146, 'tracts': 4},
'Buena Vista': {'pop': 6650, 'tracts': 1},
'Campbell': {'pop': 54842, 'tracts': 12},
'Caroline': {'pop': 28545, 'tracts': 7},
'Carroll': {'pop': 30042, 'tracts': 7},
'Charles City': {'pop': 7256, 'tracts': 3},
'Charlotte': {'pop': 12586, 'tracts': 3},
'Charlottesville': {'pop': 43475, 'tracts': 12},
'Chesapeake': {'pop': 222209, 'tracts': 41},
'Chesterfield': {'pop': 316236, 'tracts': 71},
'Clarke': {'pop': 14034, 'tracts': 3},
'Colonial Heights': {'pop': 17411, 'tracts': 5},
'Covington': {'pop': 5961, 'tracts': 2},
'Craig': {'pop': 5190, 'tracts': 1},
'Culpeper': {'pop': 46689, 'tracts': 8},
'Cumberland': {'pop': 10052, 'tracts': 2},
'Danville': {'pop': 43055, 'tracts': 16},
'Dickenson': {'pop': 15903, 'tracts': 4},
'Dinwiddie': {'pop': 28001, 'tracts': 7},
'Emporia': {'pop': 5927, 'tracts': 2},
'Essex': {'pop': 11151, 'tracts': 3},
'Fairfax': {'pop': 1081726, 'tracts': 258},
'Fairfax City': {'pop': 22565, 'tracts': 5},
'Falls Church': {'pop': 12332, 'tracts': 3},
'Fauquier': {'pop': 65203, 'tracts': 17},
'Floyd': {'pop': 15279, 'tracts': 3},
'Fluvanna': {'pop': 25691, 'tracts': 4},
'Franklin': {'pop': 56159, 'tracts': 10},
'Franklin City': {'pop': 8582, 'tracts': 2},
'Frederick': {'pop': 78305, 'tracts': 14},
'Fredericksburg': {'pop': 24286, 'tracts': 6},
'Galax': {'pop': 7042, 'tracts': 2},
'Giles': {'pop': 17286, 'tracts': 4},
'Gloucester': {'pop': 36858, 'tracts': 8},
'Goochland': {'pop': 21717, 'tracts': 5},
'Grayson': {'pop': 15533, 'tracts': 5},
'Greene': {'pop': 18403, 'tracts': 3},
'Greensville': {'pop': 12243, 'tracts': 3},
'Halifax': {'pop': 36241, 'tracts': 9},
'Hampton': {'pop': 137436, 'tracts': 34},
'Hanover': {'pop': 99863, 'tracts': 23},
'Harrisonburg': {'pop': 48914, 'tracts': 11},
'Henrico': {'pop': 306935, 'tracts': 64},
'Henry': {'pop': 54151, 'tracts': 14},
'Highland': {'pop': 2321, 'tracts': 1},
'Hopewell': {'pop': 22591, 'tracts': 7},
'Isle of Wight': {'pop': 35270, 'tracts': 8},
'James City': {'pop': 67009, 'tracts': 11},
'King George': {'pop': 23584, 'tracts': 5},
'King William': {'pop': 15935, 'tracts': 4},
'King and Queen': {'pop': 6945, 'tracts': 2},
'Lancaster': {'pop': 11391, 'tracts': 3},
'Lee': {'pop': 25587, 'tracts': 6},
'Lexington': {'pop': 7042, 'tracts': 1},
'Loudoun': {'pop': 312311, 'tracts': 65},
'Louisa': {'pop': 33153, 'tracts': 6},
'Lunenburg': {'pop': 12914, 'tracts': 3},
'Lynchburg': {'pop': 75568, 'tracts': 19},
'Madison': {'pop': 13308, 'tracts': 2},
'Manassas': {'pop': 37821, 'tracts': 7},
'Manassas Park': {'pop': 14273, 'tracts': 2},
'Martinsville': {'pop': 13821, 'tracts': 5},
'Mathews': {'pop': 8978, 'tracts': 2},
'Mecklenburg': {'pop': 32727, 'tracts': 9},
'Middlesex': {'pop': 10959, 'tracts': 4},
'Montgomery': {'pop': 94392, 'tracts': 16},
'Nelson': {'pop': 15020, 'tracts': 3},
'New Kent': {'pop': 18429, 'tracts': 3},
'Newport News': {'pop': 180719, 'tracts': 44},
'Norfolk': {'pop': 242803, 'tracts': 81},
'Northampton': {'pop': 12389, 'tracts': 4},
'Northumberland': {'pop': 12330, 'tracts': 3},
'Norton': {'pop': 3958, 'tracts': 1},
'Nottoway': {'pop': 15853, 'tracts': 4},
'Orange': {'pop': 33481, 'tracts': 5},
'Page': {'pop': 24042, 'tracts': 5},
'Patrick': {'pop': 18490, 'tracts': 4},
'Petersburg': {'pop': 32420, 'tracts': 11},
'Pittsylvania': {'pop': 63506, 'tracts': 16},
'Poquoson': {'pop': 12150, 'tracts': 3},
'Portsmouth': {'pop': 95535, 'tracts': 31},
'Powhatan': {'pop': 28046, 'tracts': 5},
'Prince Edward': {'pop': 23368, 'tracts': 5},
'Prince George': {'pop': 35725, 'tracts': 7},
'Prince William': {'pop': 402002, 'tracts': 83},
'Pulaski': {'pop': 34872, 'tracts': 10},
'Radford': {'pop': 16408, 'tracts': 3},
'Rappahannock': {'pop': 7373, 'tracts': 2},
'Richmond': {'pop': 9254, 'tracts': 2},
'Richmond City': {'pop': 204214, 'tracts': 66},
'Roanoke': {'pop': 92376, 'tracts': 18},
'Roanoke City': {'pop': 97032, 'tracts': 23},
'Rockbridge': {'pop': 22307, 'tracts': 4},
'Rockingham': {'pop': 76314, 'tracts': 19},
'Russell': {'pop': 28897, 'tracts': 7},
'Salem': {'pop': 24802, 'tracts': 5},
'Scott': {'pop': 23177, 'tracts': 6},
'Shenandoah': {'pop': 41993, 'tracts': 9},
'Smyth': {'pop': 32208, 'tracts': 9},
'Southampton': {'pop': 18570, 'tracts': 5},
'Spotsylvania': {'pop': 122397, 'tracts': 30},
'Stafford': {'pop': 128961, 'tracts': 27},
'Staunton': {'pop': 23746, 'tracts': 6},
'Suffolk': {'pop': 84585, 'tracts': 28},
'Surry': {'pop': 7058, 'tracts': 2},
'Sussex': {'pop': 12087, 'tracts': 5},
'Tazewell': {'pop': 45078, 'tracts': 11},
'Virginia Beach': {'pop': 437994, 'tracts': 100},
'Warren': {'pop': 37575, 'tracts': 8},
'Washington': {'pop': 54876, 'tracts': 13},
'Waynesboro': {'pop': 21006, 'tracts': 5},
'Westmoreland': {'pop': 17454, 'tracts': 4},
'Williamsburg': {'pop': 14068, 'tracts': 3},
'Winchester': {'pop': 26203, 'tracts': 5},
'Wise': {'pop': 41452, 'tracts': 11},
'Wythe': {'pop': 29235, 'tracts': 6},
'York': {'pop': 65464, 'tracts': 14}},
'VT': {'Addison': {'pop': 36821, 'tracts': 10},
'Bennington': {'pop': 37125, 'tracts': 12},
'Caledonia': {'pop': 31227, 'tracts': 10},
'Chittenden': {'pop': 156545, 'tracts': 35},
'Essex': {'pop': 6306, 'tracts': 3},
'Franklin': {'pop': 47746, 'tracts': 10},
'Grand Isle': {'pop': 6970, 'tracts': 2},
'Lamoille': {'pop': 24475, 'tracts': 7},
'Orange': {'pop': 28936, 'tracts': 10},
'Orleans': {'pop': 27231, 'tracts': 10},
'Rutland': {'pop': 61642, 'tracts': 20},
'Washington': {'pop': 59534, 'tracts': 19},
'Windham': {'pop': 44513, 'tracts': 18},
'Windsor': {'pop': 56670, 'tracts': 18}},
'WA': {'Adams': {'pop': 18728, 'tracts': 5},
'Asotin': {'pop': 21623, 'tracts': 6},
'Benton': {'pop': 175177, 'tracts': 37},
'Chelan': {'pop': 72453, 'tracts': 14},
'Clallam': {'pop': 71404, 'tracts': 22},
'Clark': {'pop': 425363, 'tracts': 104},
'Columbia': {'pop': 4078, 'tracts': 1},
'Cowlitz': {'pop': 102410, 'tracts': 24},
'Douglas': {'pop': 38431, 'tracts': 8},
'Ferry': {'pop': 7551, 'tracts': 3},
'Franklin': {'pop': 78163, 'tracts': 13},
'Garfield': {'pop': 2266, 'tracts': 1},
'Grant': {'pop': 89120, 'tracts': 16},
'Grays Harbor': {'pop': 72797, 'tracts': 17},
'Island': {'pop': 78506, 'tracts': 22},
'Jefferson': {'pop': 29872, 'tracts': 7},
'King': {'pop': 1931249, 'tracts': 397},
'Kitsap': {'pop': 251133, 'tracts': 55},
'Kittitas': {'pop': 40915, 'tracts': 8},
'Klickitat': {'pop': 20318, 'tracts': 3},
'Lewis': {'pop': 75455, 'tracts': 20},
'Lincoln': {'pop': 10570, 'tracts': 4},
'Mason': {'pop': 60699, 'tracts': 14},
'Okanogan': {'pop': 41120, 'tracts': 10},
'Pacific': {'pop': 20920, 'tracts': 8},
'Pend Oreille': {'pop': 13001, 'tracts': 5},
'Pierce': {'pop': 795225, 'tracts': 172},
'San Juan': {'pop': 15769, 'tracts': 5},
'Skagit': {'pop': 116901, 'tracts': 30},
'Skamania': {'pop': 11066, 'tracts': 5},
'Snohomish': {'pop': 713335, 'tracts': 151},
'Spokane': {'pop': 471221, 'tracts': 105},
'Stevens': {'pop': 43531, 'tracts': 12},
'Thurston': {'pop': 252264, 'tracts': 49},
'Wahkiakum': {'pop': 3978, 'tracts': 1},
'Walla Walla': {'pop': 58781, 'tracts': 12},
'Whatcom': {'pop': 201140, 'tracts': 34},
'Whitman': {'pop': 44776, 'tracts': 10},
'Yakima': {'pop': 243231, 'tracts': 45}},
'WI': {'Adams': {'pop': 20875, 'tracts': 7},
'Ashland': {'pop': 16157, 'tracts': 7},
'Barron': {'pop': 45870, 'tracts': 10},
'Bayfield': {'pop': 15014, 'tracts': 5},
'Brown': {'pop': 248007, 'tracts': 54},
'Buffalo': {'pop': 13587, 'tracts': 5},
'Burnett': {'pop': 15457, 'tracts': 6},
'Calumet': {'pop': 48971, 'tracts': 11},
'Chippewa': {'pop': 62415, 'tracts': 11},
'Clark': {'pop': 34690, 'tracts': 8},
'Columbia': {'pop': 56833, 'tracts': 12},
'Crawford': {'pop': 16644, 'tracts': 6},
'Dane': {'pop': 488073, 'tracts': 107},
'Dodge': {'pop': 88759, 'tracts': 20},
'Door': {'pop': 27785, 'tracts': 9},
'Douglas': {'pop': 44159, 'tracts': 12},
'Dunn': {'pop': 43857, 'tracts': 8},
'Eau Claire': {'pop': 98736, 'tracts': 20},
'Florence': {'pop': 4423, 'tracts': 2},
'Fond du Lac': {'pop': 101633, 'tracts': 20},
'Forest': {'pop': 9304, 'tracts': 4},
'Grant': {'pop': 51208, 'tracts': 12},
'Green': {'pop': 36842, 'tracts': 8},
'Green Lake': {'pop': 19051, 'tracts': 6},
'Iowa': {'pop': 23687, 'tracts': 6},
'Iron': {'pop': 5916, 'tracts': 3},
'Jackson': {'pop': 20449, 'tracts': 5},
'Jefferson': {'pop': 83686, 'tracts': 20},
'Juneau': {'pop': 26664, 'tracts': 7},
'Kenosha': {'pop': 166426, 'tracts': 35},
'Kewaunee': {'pop': 20574, 'tracts': 4},
'La Crosse': {'pop': 114638, 'tracts': 25},
'Lafayette': {'pop': 16836, 'tracts': 5},
'Langlade': {'pop': 19977, 'tracts': 6},
'Lincoln': {'pop': 28743, 'tracts': 10},
'Manitowoc': {'pop': 81442, 'tracts': 19},
'Marathon': {'pop': 134063, 'tracts': 27},
'Marinette': {'pop': 41749, 'tracts': 12},
'Marquette': {'pop': 15404, 'tracts': 5},
'Menominee': {'pop': 4232, 'tracts': 2},
'Milwaukee': {'pop': 947735, 'tracts': 297},
'Monroe': {'pop': 44673, 'tracts': 9},
'Oconto': {'pop': 37660, 'tracts': 10},
'Oneida': {'pop': 35998, 'tracts': 14},
'Outagamie': {'pop': 176695, 'tracts': 40},
'Ozaukee': {'pop': 86395, 'tracts': 18},
'Pepin': {'pop': 7469, 'tracts': 2},
'Pierce': {'pop': 41019, 'tracts': 8},
'Polk': {'pop': 44205, 'tracts': 10},
'Portage': {'pop': 70019, 'tracts': 14},
'Price': {'pop': 14159, 'tracts': 6},
'Racine': {'pop': 195408, 'tracts': 44},
'Richland': {'pop': 18021, 'tracts': 5},
'Rock': {'pop': 160331, 'tracts': 38},
'Rusk': {'pop': 14755, 'tracts': 5},
'Sauk': {'pop': 61976, 'tracts': 13},
'Sawyer': {'pop': 16557, 'tracts': 6},
'Shawano': {'pop': 41949, 'tracts': 11},
'Sheboygan': {'pop': 115507, 'tracts': 26},
'St. Croix': {'pop': 84345, 'tracts': 14},
'Taylor': {'pop': 20689, 'tracts': 6},
'Trempealeau': {'pop': 28816, 'tracts': 8},
'Vernon': {'pop': 29773, 'tracts': 7},
'Vilas': {'pop': 21430, 'tracts': 5},
'Walworth': {'pop': 102228, 'tracts': 22},
'Washburn': {'pop': 15911, 'tracts': 5},
'Washington': {'pop': 131887, 'tracts': 28},
'Waukesha': {'pop': 389891, 'tracts': 86},
'Waupaca': {'pop': 52410, 'tracts': 12},
'Waushara': {'pop': 24496, 'tracts': 7},
'Winnebago': {'pop': 166994, 'tracts': 41},
'Wood': {'pop': 74749, 'tracts': 17}},
'WV': {'Barbour': {'pop': 16589, 'tracts': 4},
'Berkeley': {'pop': 104169, 'tracts': 14},
'Boone': {'pop': 24629, 'tracts': 8},
'Braxton': {'pop': 14523, 'tracts': 3},
'Brooke': {'pop': 24069, 'tracts': 6},
'Cabell': {'pop': 96319, 'tracts': 29},
'Calhoun': {'pop': 7627, 'tracts': 2},
'Clay': {'pop': 9386, 'tracts': 3},
'Doddridge': {'pop': 8202, 'tracts': 2},
'Fayette': {'pop': 46039, 'tracts': 12},
'Gilmer': {'pop': 8693, 'tracts': 2},
'Grant': {'pop': 11937, 'tracts': 3},
'Greenbrier': {'pop': 35480, 'tracts': 7},
'Hampshire': {'pop': 23964, 'tracts': 5},
'Hancock': {'pop': 30676, 'tracts': 8},
'Hardy': {'pop': 14025, 'tracts': 3},
'Harrison': {'pop': 69099, 'tracts': 22},
'Jackson': {'pop': 29211, 'tracts': 6},
'Jefferson': {'pop': 53498, 'tracts': 15},
'Kanawha': {'pop': 193063, 'tracts': 53},
'Lewis': {'pop': 16372, 'tracts': 5},
'Lincoln': {'pop': 21720, 'tracts': 5},
'Logan': {'pop': 36743, 'tracts': 9},
'Marion': {'pop': 56418, 'tracts': 18},
'Marshall': {'pop': 33107, 'tracts': 9},
'Mason': {'pop': 27324, 'tracts': 6},
'McDowell': {'pop': 22113, 'tracts': 8},
'Mercer': {'pop': 62264, 'tracts': 16},
'Mineral': {'pop': 28212, 'tracts': 7},
'Mingo': {'pop': 26839, 'tracts': 7},
'Monongalia': {'pop': 96189, 'tracts': 24},
'Monroe': {'pop': 13502, 'tracts': 3},
'Morgan': {'pop': 17541, 'tracts': 4},
'Nicholas': {'pop': 26233, 'tracts': 7},
'Ohio': {'pop': 44443, 'tracts': 18},
'Pendleton': {'pop': 7695, 'tracts': 3},
'Pleasants': {'pop': 7605, 'tracts': 2},
'Pocahontas': {'pop': 8719, 'tracts': 4},
'Preston': {'pop': 33520, 'tracts': 8},
'Putnam': {'pop': 55486, 'tracts': 10},
'Raleigh': {'pop': 78859, 'tracts': 17},
'Randolph': {'pop': 29405, 'tracts': 7},
'Ritchie': {'pop': 10449, 'tracts': 3},
'Roane': {'pop': 14926, 'tracts': 4},
'Summers': {'pop': 13927, 'tracts': 4},
'Taylor': {'pop': 16895, 'tracts': 4},
'Tucker': {'pop': 7141, 'tracts': 3},
'Tyler': {'pop': 9208, 'tracts': 3},
'Upshur': {'pop': 24254, 'tracts': 6},
'Wayne': {'pop': 42481, 'tracts': 11},
'Webster': {'pop': 9154, 'tracts': 3},
'Wetzel': {'pop': 16583, 'tracts': 5},
'Wirt': {'pop': 5717, 'tracts': 2},
'Wood': {'pop': 86956, 'tracts': 26},
'Wyoming': {'pop': 23796, 'tracts': 6}},
'WY': {'Albany': {'pop': 36299, 'tracts': 10},
'Big Horn': {'pop': 11668, 'tracts': 3},
'Campbell': {'pop': 46133, 'tracts': 7},
'Carbon': {'pop': 15885, 'tracts': 5},
'Converse': {'pop': 13833, 'tracts': 4},
'Crook': {'pop': 7083, 'tracts': 2},
'Fremont': {'pop': 40123, 'tracts': 10},
'Goshen': {'pop': 13249, 'tracts': 4},
'Hot Springs': {'pop': 4812, 'tracts': 2},
'Johnson': {'pop': 8569, 'tracts': 2},
'Laramie': {'pop': 91738, 'tracts': 21},
'Lincoln': {'pop': 18106, 'tracts': 4},
'Natrona': {'pop': 75450, 'tracts': 18},
'Niobrara': {'pop': 2484, 'tracts': 1},
'Park': {'pop': 28205, 'tracts': 5},
'Platte': {'pop': 8667, 'tracts': 2},
'Sheridan': {'pop': 29116, 'tracts': 6},
'Sublette': {'pop': 10247, 'tracts': 2},
'Sweetwater': {'pop': 43806, 'tracts': 12},
'Teton': {'pop': 21294, 'tracts': 4},
'Uinta': {'pop': 21118, 'tracts': 3},
'Washakie': {'pop': 8533, 'tracts': 3},
'Weston': {'pop': 7208, 'tracts': 2}}} | [
439,
6601,
796,
1391,
6,
10206,
10354,
1391,
6,
37474,
315,
1547,
3687,
10354,
1391,
6,
12924,
10354,
513,
23756,
11,
705,
83,
974,
82,
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352,
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201,
198,
220,
220,
220,
220,
220,
220,
220,
705,
37474,
315,
1547,
2688,
1035... | 1.800097 | 86,317 |
# -*- coding: utf-8 -*-
"""Mock template engine, for use in tests."""
from piecutter.engines import Engine
#: Default value used as :py:attr:`MockEngine.render_result`
default_render_result = u'RENDER WITH ARGS={args!s} AND KWARGS={kwargs!s}'
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
37811,
44,
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11,
329,
779,
287,
5254,
526,
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198,
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2508,
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353,
13,
1516,
1127,
1330,
7117,
628,
198,
2,
25,
15161,
1988,
973,
355,
1058,... | 2.733333 | 90 |
#MCCA (Multiview Canonical Correlation Analysis)
import numpy as np
from scipy import linalg as lin
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
| [
2,
44,
4093,
32,
357,
15205,
452,
769,
19507,
605,
2744,
49501,
14691,
8,
198,
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299,
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355,
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220,
198,
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88,
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300,
1292,
70,
355,
9493,
220,
198,
6738,
1341,
35720,
13,
3866,
36948,
1330,
899... | 2.759036 | 83 |
import argparse
from sklearn.decomposition import LatentDirichletAllocation as LDA
import pickle
from biom import load_table
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--train-biom', help='Training biom file',
required=True)
parser.add_argument('--n-latent', type=int, help='Number of components')
parser.add_argument('--iterations', type=int,
default=10000, required=False,
help='Number of iterations.')
parser.add_argument('--batch-size', type=int,
default=256, required=False,
help='Batch size')
parser.add_argument('--n-jobs', type=int,
default=-1, required=False,
help='Number of concurrent jobs.')
parser.add_argument('--model-checkpoint',
required=True,
help='Location of saved model.')
args = parser.parse_args()
main(args)
| [
11748,
1822,
29572,
198,
6738,
1341,
35720,
13,
12501,
296,
9150,
1330,
5476,
298,
35277,
488,
1616,
3237,
5040,
355,
406,
5631,
198,
11748,
2298,
293,
198,
6738,
27488,
1330,
3440,
62,
11487,
628,
198,
198,
361,
11593,
3672,
834,
6624,... | 2.15304 | 477 |
'''
splitjoin.py
sonicskye@2018
The functions are used to split and join files
based on:
https://stonesoupprogramming.com/2017/09/16/python-split-and-join-file/
with modification by adding natural sort
'''
import os
import re
# https://stackoverflow.com/questions/11150239/python-natural-sorting
# example
'''
imageFilePath = os.path.join(os.path.dirname(__file__), 'cryptocurrency.jpg')
destinationFolderPath = os.path.join(os.path.dirname(__file__), 'tmp')
imageFilePath2 = os.path.join(os.path.dirname(__file__), 'cryptocurrency2.jpg')
split(imageFilePath, destinationFolderPath, 2350)
join(destinationFolderPath, imageFilePath2, 4700)
'''
| [
7061,
6,
198,
35312,
22179,
13,
9078,
198,
1559,
873,
2584,
68,
31,
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1378,
28750,
280,
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13,
785,
1... | 2.86087 | 230 |
# Mark Bundgus 2019
import luigi
import logging
from yarn_api_client import ResourceManager # https://python-client-for-hadoop-yarn-api.readthedocs.io
from datetime import datetime
from datetime import timedelta
import pandas as pd
from tabulate import tabulate
import os
import configuration
log = logging.getLogger("luigi-interface")
# create leader boards for the last 3 days
| [
2,
2940,
13319,
70,
385,
13130,
198,
11748,
300,
84,
25754,
198,
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220,
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29412,
12,
16366,
12,
1640,
12,
71,
4533,
404,
12,
88,
1501,
12,
15042,
... | 3.428571 | 112 |
from bfxhfindicators.indicator import Indicator
| [
6738,
275,
21373,
71,
19796,
44549,
13,
521,
26407,
1330,
1423,
26407,
198
] | 3.692308 | 13 |
import argparse
from datetime import datetime
import os
from catalyst import dl, utils
from catalyst.contrib.data import AllTripletsSampler
from catalyst.contrib.losses import TripletMarginLossWithSampler
from catalyst.data import BatchBalanceClassSampler
from torch import nn, optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
from src.modules import resnet9
from src.settings import LOGS_ROOT
if __name__ == "__main__":
parser = argparse.ArgumentParser()
utils.boolean_flag(parser, "use-ml", default=False)
args = parser.parse_args()
main(args.use_ml)
| [
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1330,
1439,
14824,
46916,
16305,
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198,
6738,
31357,
1... | 3.26455 | 189 |
"""
Manage AWS Batch jobs, queues, and compute environments.
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import os, sys, argparse, base64, collections, io, subprocess, json, time, re, hashlib, concurrent.futures, itertools
from botocore.exceptions import ClientError
from . import logger
from .ls import register_parser, register_listing_parser
from .ecr import ecr_image_name_completer
from .util import Timestamp, paginate, get_mkfs_command
from .util.crypto import ensure_ssh_key
from .util.cloudinit import get_user_data
from .util.exceptions import AegeaException
from .util.printing import page_output, tabulate, YELLOW, RED, GREEN, BOLD, ENDC
from .util.aws import (resources, clients, ensure_iam_role, ensure_instance_profile, make_waiter, ensure_vpc,
ensure_security_group, ensure_log_group, IAMPolicyBuilder, resolve_ami, instance_type_completer,
expect_error_codes, instance_storage_shellcode)
from .util.aws.spot import SpotFleetBuilder
from .util.aws.logs import CloudwatchLogReader
from .util.aws.batch import ensure_job_definition, get_command_and_env
batch_parser = register_parser(batch, help="Manage AWS Batch resources", description=__doc__)
parser = register_listing_parser(queues, parent=batch_parser, help="List Batch queues")
parser = register_parser(create_queue, parent=batch_parser, help="Create a Batch queue")
parser.add_argument("name")
parser.add_argument("--priority", type=int, default=5)
parser.add_argument("--compute-environments", nargs="+", required=True)
parser = register_parser(delete_queue, parent=batch_parser, help="Delete a Batch queue")
parser.add_argument("name").completer = complete_queue_name
parser = register_listing_parser(compute_environments, parent=batch_parser, help="List Batch compute environments")
cce_parser = register_parser(create_compute_environment, parent=batch_parser, help="Create a Batch compute environment")
cce_parser.add_argument("name")
cce_parser.add_argument("--type", choices={"MANAGED", "UNMANAGED"})
cce_parser.add_argument("--compute-type", choices={"EC2", "SPOT"})
cce_parser.add_argument("--min-vcpus", type=int)
cce_parser.add_argument("--desired-vcpus", type=int)
cce_parser.add_argument("--max-vcpus", type=int)
cce_parser.add_argument("--instance-types", nargs="+").completer = instance_type_completer
cce_parser.add_argument("--ssh-key-name")
cce_parser.add_argument("--instance-role", default=__name__ + ".ecs_container_instance")
cce_parser.add_argument("--service-role", default=__name__ + ".service")
cce_parser.add_argument("--ecs-container-instance-ami")
cce_parser.add_argument("--ecs-container-instance-ami-tags")
uce_parser = register_parser(update_compute_environment, parent=batch_parser, help="Update a Batch compute environment")
uce_parser.add_argument("name").completer = complete_ce_name
uce_parser.add_argument("--min-vcpus", type=int)
uce_parser.add_argument("--desired-vcpus", type=int)
uce_parser.add_argument("--max-vcpus", type=int)
parser = register_parser(delete_compute_environment, parent=batch_parser, help="Delete a Batch compute environment")
parser.add_argument("name").completer = complete_ce_name
submit_parser = register_parser(submit, parent=batch_parser, help="Submit a job to a Batch queue")
submit_parser.add_argument("--name")
submit_parser.add_argument("--queue", default=__name__.replace(".", "_")).completer = complete_queue_name
submit_parser.add_argument("--depends-on", nargs="+", metavar="JOB_ID", default=[])
submit_parser.add_argument("--job-definition-arn")
add_command_args(submit_parser)
group = submit_parser.add_argument_group(title="job definition parameters", description="""
See http://docs.aws.amazon.com/batch/latest/userguide/job_definitions.html""")
add_job_defn_args(group)
group.add_argument("--vcpus", type=int, default=1)
group.add_argument("--gpus", type=int, default=0)
group.add_argument("--privileged", action="store_true", default=False)
group.add_argument("--volume-type", choices={"standard", "io1", "gp2", "sc1", "st1"},
help="io1, PIOPS SSD; gp2, general purpose SSD; sc1, cold HDD; st1, throughput optimized HDD")
group.add_argument("--parameters", nargs="+", metavar="NAME=VALUE", type=lambda x: x.split("=", 1), default=[])
group.add_argument("--job-role", metavar="IAM_ROLE", default=__name__ + ".worker",
help="Name of IAM role to grant to the job")
group.add_argument("--storage", nargs="+", metavar="MOUNTPOINT=SIZE_GB",
type=lambda x: x.rstrip("GBgb").split("=", 1), default=[])
group.add_argument("--efs-storage", action="store", dest="efs_storage", default=False,
help="Mount EFS network filesystem to the mount point specified. Example: --efs-storage /mnt")
group.add_argument("--mount-instance-storage", nargs="?", const="/mnt",
help="Assemble (MD RAID0), format and mount ephemeral instance storage on this mount point")
submit_parser.add_argument("--timeout",
help="Terminate (and possibly restart) the job after this time (use suffix s, m, h, d, w)")
submit_parser.add_argument("--retry-attempts", type=int, default=1,
help="Number of times to restart the job upon failure")
submit_parser.add_argument("--dry-run", action="store_true", help="Gather arguments and stop short of submitting job")
parser = register_parser(terminate, parent=batch_parser, help="Terminate Batch jobs")
parser.add_argument("job_id", nargs="+")
parser.add_argument("--reason", help="A message to attach to the job that explains the reason for canceling it")
job_status_colors = dict(SUBMITTED=YELLOW(), PENDING=YELLOW(), RUNNABLE=BOLD() + YELLOW(),
STARTING=GREEN(), RUNNING=GREEN(),
SUCCEEDED=BOLD() + GREEN(), FAILED=BOLD() + RED())
job_states = job_status_colors.keys()
parser = register_listing_parser(ls, parent=batch_parser, help="List Batch jobs")
parser.add_argument("--queues", nargs="+").completer = complete_queue_name
parser.add_argument("--status", nargs="+", default=job_states, choices=job_states)
parser = register_parser(describe, parent=batch_parser, help="Describe a Batch job")
parser.add_argument("job_id")
get_logs_parser = register_parser(get_logs, parent=batch_parser, help="Retrieve logs for a Batch job")
get_logs_parser.add_argument("log_stream_name")
watch_parser = register_parser(watch, parent=batch_parser, help="Monitor a running Batch job and stream its logs")
watch_parser.add_argument("job_id")
for parser in get_logs_parser, watch_parser:
lines_group = parser.add_mutually_exclusive_group()
lines_group.add_argument("--head", type=int, nargs="?", const=10,
help="Retrieve this number of lines from the beginning of the log (default 10)")
lines_group.add_argument("--tail", type=int, nargs="?", const=10,
help="Retrieve this number of lines from the end of the log (default 10)")
ssh_parser = register_parser(ssh, parent=batch_parser, help="Log in to a running Batch job via SSH")
ssh_parser.add_argument("job_id")
ssh_parser.add_argument("ssh_args", nargs=argparse.REMAINDER)
| [
37811,
198,
5124,
496,
30865,
347,
963,
3946,
11,
43359,
11,
290,
24061,
12493,
13,
198,
37811,
198,
198,
6738,
11593,
37443,
834,
1330,
4112,
62,
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11,
7297,
11,
3601,
62,
8818,
11,
28000,
1098,
62,
17201,
874,
198,
198,
11748,
... | 2.863924 | 2,528 |
##############################################
# This code is based on samples from pytorch #
##############################################
# Writer: Kimin Lee
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import data_loader
import numpy as np
import torchvision.utils as vutils
import models
from torchvision import datasets, transforms
from torch.autograd import Variable
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = "4"
# Training settings
parser = argparse.ArgumentParser(description='Training code - joint confidence')
parser.add_argument('--batch-size', type=int, default=128, help='input batch size for training')
parser.add_argument('--save-interval', type=int, default=3, help='save interval')
parser.add_argument('--epochs', type=int, default=100, help='number of epochs to train')
parser.add_argument('--lr', type=float, default=0.0002, help='learning rate')
parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, help='random seed')
parser.add_argument('--log-interval', type=int, default=100,
help='how many batches to wait before logging training status')
parser.add_argument('--dataset', default='cifar10', help='mnist | cifar10 | svhn')
parser.add_argument('--dataroot', required=True, help='path to dataset')
parser.add_argument('--imageSize', type=int, default=32, help='the height / width of the input image to network')
parser.add_argument('--outf', default='.', help='folder to output images and model checkpoints')
parser.add_argument('--wd', type=float, default=0.0, help='weight decay')
parser.add_argument('--droprate', type=float, default=0.1, help='learning rate decay')
parser.add_argument('--decreasing_lr', default='60', help='decreasing strategy')
parser.add_argument('--num_classes', type=int, default=10, help='the # of classes')
parser.add_argument('--beta', type=float, default=8, help='penalty parameter for KL term')
args = parser.parse_args()
if args.dataset == 'cifar10':
args.beta = 0.1
args.batch_size = 64
print(args)
args.cuda = not args.no_cuda and torch.cuda.is_available()
print("Random Seed: ", args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
print('load data: ', args.dataset)
if args.dataset == 'mnist':
transform = transforms.Compose([
transforms.Scale(32),
transforms.ToTensor(),
transforms.Lambda(lambda x: x.repeat(3, 1, 1)),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('data', train=True, download=True, transform=transform),
batch_size=128, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('data', train=False, download=True, transform=transform),
batch_size=128, shuffle=True)
else:
train_loader, test_loader = data_loader.getTargetDataSet(args.dataset, args.batch_size, args.imageSize,
args.dataroot)
print('Load model')
model = models.vgg13()
print(model)
print('load GAN')
nz = 100
G = models.cGenerator(1, nz, 64, 3) # ngpu, nz, ngf, nc
D = models.cDiscriminator(1, 3, 64) # ngpu, nc, ndf
G.weight_init(mean=0.0, std=0.02)
D.weight_init(mean=0.0, std=0.02)
# Initial setup for GAN
real_label = 1
fake_label = 0
criterion = nn.BCELoss()
nz = 100
#fixed_noise = torch.FloatTensor(64, nz, 1, 1).normal_(0, 1)
# fixed_noise = torch.randn((128, 100)).view(-1, 100, 1, 1)
if args.cuda:
model.cuda()
D.cuda()
G.cuda()
criterion.cuda()
#fixed_noise = fixed_noise.cuda()
#fixed_noise = Variable(fixed_noise)
print('Setup optimizer')
lr = 0.0002
batch_size = 128
optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.wd)
G_optimizer = optim.Adam(G.parameters(), lr=lr, betas=(0.5, 0.999))
D_optimizer = optim.Adam(D.parameters(), lr=lr, betas=(0.5, 0.999))
decreasing_lr = list(map(int, args.decreasing_lr.split(',')))
img_size = 32
num_labels = 10
# os.environ["CUDA_LAUNCH_BLOCKING"]="1"
# Binary Cross Entropy loss
BCE_loss = nn.BCELoss()
# fixed_noise = torch.FloatTensor(64, nz, 1, 1).normal_(0, 1)
fixed_noise = torch.randn((64, 100)).view(-1, 100, 1, 1).cuda()
fixed_label = 0
first = True
for epoch in range(1, args.epochs + 1):
train(epoch)
test(epoch)
if epoch in decreasing_lr:
G_optimizer.param_groups[0]['lr'] *= args.droprate
D_optimizer.param_groups[0]['lr'] *= args.droprate
optimizer.param_groups[0]['lr'] *= args.droprate
if epoch % 20 == 0:
# do checkpointing
torch.save(G.state_dict(), '%s/2netG_epoch_%d.pth' % (args.outf, epoch))
torch.save(D.state_dict(), '%s/2netD_epoch_%d.pth' % (args.outf, epoch))
torch.save(model.state_dict(), '%s/2model_epoch_%d.pth' % (args.outf, epoch))
| [
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2,
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2438,
318,
1912,
319,
8405,
422,
12972,
13165,
354,
1303,
198,
29113,
7804,
4242,
2235,
198,
2,
26606,
25,
6502,
259,
5741,
220,
198,
198,
6738,
11593,
37443,
834,
1330,
3601,
62,
8818,
198,
11748... | 2.563464 | 2,009 |
#code https://practice.geeksforgeeks.org/problems/swap-and-maximize/0
for _ in range(int(input())):
n = int(input())
arr = list(map(int, input().split()))
arr.sort()
max = 0
for i in range(n//2):
max -= 2*arr[i]
max += 2*arr[n-i-1]
print(max) | [
2,
8189,
3740,
1378,
39541,
13,
469,
2573,
30293,
2573,
13,
2398,
14,
1676,
22143,
14,
2032,
499,
12,
392,
12,
9806,
48439,
14,
15,
198,
198,
1640,
4808,
287,
2837,
7,
600,
7,
15414,
28955,
2599,
198,
220,
220,
220,
299,
796,
493,... | 1.948387 | 155 |
from random import sample, shuffle
from ga4stpg.graph import UGraph
from ga4stpg.graph.disjointsets import DisjointSets
| [
6738,
4738,
1330,
6291,
11,
36273,
201,
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201,
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6738,
31986,
19,
301,
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1330,
471,
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201,
198,
6738,
31986,
19,
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6381,
73,
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28709,
1330,
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73,
1563,
50,
1039,
201,
198,
20... | 2.804348 | 46 |
import pandas as pd
import rapidfuzz
import math
import numpy as np
# ------------------------- #
# --------- DATA ---------- #
# ------------------------- #
# Read in mock census and PES data
CEN = pd.read_csv('Data/Mock_Rwanda_Data_Census.csv')
PES = pd.read_csv('Data/Mock_Rwanda_Data_Pes.csv')
# select needed columns
CEN = CEN[['id_indi_cen', 'firstnm_cen', 'lastnm_cen', 'age_cen', 'month_cen', 'year_cen', 'sex_cen', 'province_cen']]
PES = PES[['id_indi_pes', 'firstnm_pes', 'lastnm_pes', 'age_pes', 'month_pes', 'year_pes', 'sex_pes', 'province_pes']]
# ----------------------------- #
# --------- BLOCKING ---------- #
# ----------------------------- #
# Block on province geographic variable
BP1 = 'province'
# Combine
for i, BP in enumerate([BP1], 1):
if i == 1:
combined_blocks = PES.merge(CEN, left_on = BP + '_pes', right_on = BP + '_cen', how = 'inner').drop_duplicates(['id_indi_cen', 'id_indi_pes'])
print("1" + str(combined_blocks.count()))
# Count
len(combined_blocks) # 50042
# -------------------------------------------------- #
# --------------- AGREEMENT VECTORS ---------------- #
# -------------------------------------------------- #
# Agreement vector is created which is then inputted into the EM Algorithm.
# Set v1, v2,... vn as the agreement variables
# Select agreement variables
v1 = 'firstnm'
v2 = 'lastnm'
v3 = 'month'
v4 = 'year'
v5 = 'sex'
# All agreement variables used to calculate match weights & probabilities
all_variables = [v1, v2, v3, v4, v5]
# Variables using partial agreement (string similarity)
edit_distance_variables = [v1, v2]
dob_variables = [v3, v4]
remaining_variables = [v5]
# Cut off values for edit distance variables
cutoff_values = [0.45, 0.45]
# Replace NaN with blank spaces to assure the right data types for string similarity metrics
for variable in edit_distance_variables:
cen_var = variable+ '_cen'
pes_var = variable + '_pes'
combined_blocks[cen_var] = combined_blocks[cen_var].fillna("")
combined_blocks[pes_var] = combined_blocks[pes_var].fillna("")
# Create forename/ last name Edit Distance score columns for all pairs
combined_blocks['firstnm_agreement'] = combined_blocks.apply(lambda x: SLD(x['firstnm_pes'], x['firstnm_cen']), axis=1)
combined_blocks['lastnm_agreement'] = combined_blocks.apply(lambda x: SLD(x['lastnm_pes'], x['lastnm_cen']), axis=1)
# --------------------------------------------------------- #
# ---------------- INITIAL M & U VALUES ------------------- #
# --------------------------------------------------------- #
# Read in M and U values
m_values = pd.read_csv('Data/m_values.csv')
u_values = pd.read_csv('Data/u_values.csv')
# Save individual M values from file
FN_M = m_values[m_values.variable == 'firstnm'].iloc[0][1]
SN_M = m_values[m_values.variable == 'lastnm'].iloc[0][1]
SEX_M = m_values[m_values.variable == 'sex'].iloc[0][1]
MONTH_M = m_values[m_values.variable == 'month'].iloc[0][1]
YEAR_M = m_values[m_values.variable == 'year'].iloc[0][1]
# Save individual U values from file
FN_U = u_values[u_values.variable == 'firstnm'].iloc[0][1]
SN_U = u_values[u_values.variable == 'lastnm'].iloc[0][1]
SEX_U = u_values[u_values.variable == 'sex'].iloc[0][1]
MONTH_U = u_values[u_values.variable == 'month'].iloc[0][1]
YEAR_U = u_values[u_values.variable == 'year'].iloc[0][1]
# Add M values to unlinked data
combined_blocks['firstnm_m'] = FN_M
combined_blocks['lastnm_m'] = SN_M
combined_blocks['sex_m'] = SEX_M
combined_blocks['month_m'] = MONTH_M
combined_blocks['year_m'] = YEAR_M
# Add U values to unlinked data
combined_blocks['firstnm_u'] = FN_U
combined_blocks['lastnm_u'] = SN_U
combined_blocks['sex_u'] = SEX_U
combined_blocks['month_u'] = MONTH_U
combined_blocks['year_u'] = YEAR_U
# Add Agreement / Disagreement Weights
for var in all_variables:
# apply calculations: agreement weight = log base 2 (m/u)
combined_blocks[var + "_agreement_weight"] = combined_blocks.apply(lambda x: (math.log2(x[var + "_m"] / x[var + "_u"])), axis = 1)
# disagreement weight = log base 2 ((1-m)/(1-u))
combined_blocks[var + "_disagreement_weight"] = combined_blocks.apply(lambda x: (math.log2((1 - x[var + "_m"]) / (1 - x[var + "_u"]))), axis = 1)
# show sample of agreement/disagreement weights calculated
print(combined_blocks[[var + "_m", var + "_u", var + "_agreement_weight", var + "_disagreement_weight"]].head(1))
'''
Alter the M and U values above (i.e. FN_M, FN_U etc. currently lines 100 - 112) to see the effect on variable agreement/disagreement weights
'''
# --------------------------------------------------- #
# ------------------ MATCH SCORES ------------------ #
# --------------------------------------------------- #
''' An agreement value between 0 and 1 is calculated for each agreeement variable '''
''' This is done for every candidate record pair '''
# --------------------------------------- #
# ------------- DOB SCORE -------------- #
# --------------------------------------- #
# Partial scores
combined_blocks['month_agreement'] = np.where(combined_blocks['month_pes'] == combined_blocks['month_cen'], 1/3, 0)
combined_blocks['year_agreement'] = np.where(combined_blocks['year_pes'] == combined_blocks['year_cen'], 1/2, 0)
# Compute final Score and drop extra score columns
dob_score_columns = ['month_agreement', 'year_agreement']
combined_blocks['DOB_agreement'] = combined_blocks[dob_score_columns].sum(axis=1)
# combined_blocks = combined_blocks.drop(dob_score_columns, axis = 1)
# ---------------------------------------- #
# ---------- PARTIAL CUT OFFS ------------ #
# ---------------------------------------- #
# All partial variables except DOB
for variable, cutoff in zip(edit_distance_variables, cutoff_values):
# If agreement below a certain level, set agreement to 0. Else, leave agreeement as it is
combined_blocks[variable + '_agreement'] = np.where(combined_blocks[variable + "_agreement"] <= cutoff, 0, combined_blocks[variable + "_agreement"])
# Remaining variables (no partial scores)
for variable in remaining_variables:
# Calculate 1/0 Agreement Score (no partial scoring)
combined_blocks[variable + '_agreement'] = np.where(combined_blocks[variable + "_cen"] == combined_blocks[variable + "_pes"], 1, 0)
# ------------------------------------------------------------------ #
# ------------------------- WEIGHTS ------------------------------- #
# ------------------------------------------------------------------ #
# Start by giving all records agreement weights
for variable in all_variables:
combined_blocks[variable + "_weight"] = combined_blocks[variable + "_agreement_weight"]
# Update for partial agreement / disagreement (only when agreement < 1)
# source: https://www.census.gov/content/dam/Census/library/working-papers/1991/adrm/rr91-9.pdf
# weight = Agreement_Weight if Agreement = 1, and
# MAX{(Agreement_Weight - (Agreement_Weight - Disgreement_Weight)*(1-Agreement)*(9/2)), Disgreement_Weight} if 0 <= Agreement < 1.
for variable in all_variables:
combined_blocks[variable + "_weight"] = np.where(combined_blocks[variable + "_agreement"] < 1,
np.maximum(((combined_blocks[variable + "_agreement_weight"]) -
((combined_blocks[variable + "_agreement_weight"] - combined_blocks[variable + "_disagreement_weight"]) *
(1 - combined_blocks[variable + "_agreement"]) * (9/2))),
combined_blocks[variable + "_disagreement_weight"]),
combined_blocks[variable + "_weight"])
# Set weights to 0 (instead of disagreement_weight) if there is missingess in PES or CEN variable (agreement == 0 condition needed for DOB)
for variable in all_variables:
combined_blocks[variable + "_weight"] = np.where(combined_blocks[variable + '_pes'].isnull() | combined_blocks[variable + '_cen'].isnull() &
(combined_blocks[variable + '_agreement'] == 0), 0,
combined_blocks[variable + '_weight'])
# Sum column wise across the above columns - create match score
combined_blocks["match_score"] = combined_blocks[['firstnm_weight', 'lastnm_weight', 'month_weight', 'year_weight', 'sex_weight']].sum(axis=1)
# ------------------------------------------------------------------ #
# ----------------------- ADJUSTMENTS ----------------------------- #
# ------------------------------------------------------------------ #
# To reduce false matches going to clerical, if ages are dissimilar set score to 0
combined_blocks['match_score'] = np.where((combined_blocks['age_pes'].notnull() == False) &
combined_blocks['age_cen'].notnull() &
(combined_blocks['age_pes'] - combined_blocks['age_cen'] > 5),
0, combined_blocks['match_score'])
''' let's view some example clusters produced to check if the scores assigned are sensible'''
# high-scoring candidate record pairs
cen_vars = [s + '_cen' for s in all_variables]
pes_vars = [s + '_pes' for s in all_variables]
display(combined_blocks[cen_vars + pes_vars + ['match_score']].sort_values(by=['match_score'], ascending=False).head(50))
# and low-scoring candidate pairs
display(combined_blocks[cen_vars + pes_vars + ['match_score']].sort_values(by=['match_score']).head(50))
# -------------------------------------- #
# -------------- SAVE ----------------- #
# -------------------------------------- #
combined_blocks.to_csv('Data/Probabilistic_Scores.csv')
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2... | 2.749654 | 3,615 |
import time, sys, mmap
import subprocess
from flask import Flask, request
app = Flask(__name__)
import fcntl, time, struct
import redis
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
# executor = ProcessPoolExecutor(max_workers=2)
executor = ThreadPoolExecutor(max_workers=2)
MEMINFO = False
ENABLE_TCPDUMP = False
# DUMPPATH = '/dev/shm/dump'
if ENABLE_TCPDUMP:
dumpfile = open('/dev/shm/dump', 'w+')
tcpdump_proc = subprocess.Popen(['tcpdump', '--immediate-mode', '-l', '-i', 'any'], bufsize=0, shell=True, stdout=dumpfile, stderr=dumpfile, text=True)
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... | 2.689498 | 219 |
import csv
import numpy as np
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
datasets = ['alt.atheism', 'comp.graphics', 'comp.os.ms-windows.misc', 'comp.sys.ibm.pc.hardware', 'comp.sys.mac.hardware', 'comp.windows.x',
'misc.forsale', 'rec.autos', 'rec.motorcycles', 'rec.sport.baseball', 'rec.sport.hockey', 'sci.crypt', 'sci.electronics',
'sci.med', 'sci.space', 'soc.religion.christian', 'talk.politics.guns', 'talk.politics.mideast', 'talk.politics.misc', 'talk.religion.misc']
algorithms = ['MI-Kernel', 'mi-Graph', 'miFV', 'mi-Net', 'MI-Net', 'MI-Net \nwith DS', 'MI-Net \nwith RC',
'Res+pool', 'Res+pool\n-GCN', 'B-Res+pool\n-GCN (ours)']
my_pal = {'MI-Kernel': 'k', 'mi-Graph': 'gray', 'miFV': 'c', 'mi-Net': 'b', 'MI-Net': 'gold', 'MI-Net \nwith DS': 'teal', 'MI-Net \nwith RC': 'brown',
'Res+pool': 'darkgreen', 'Res+pool\n-GCN': 'm', 'B-Res+pool\n-GCN (ours)': 'r'}
num_data_set = len(datasets)
num_alg = len(algorithms)
acc_matrix = np.loadtxt('rank_box_results.txt', delimiter=' ', usecols=range(num_alg))
print(acc_matrix)
rank = num_alg - np.argsort(np.argsort(acc_matrix, axis=1), axis=1)
print(rank)
for data_id_, data in enumerate(datasets):
print('----------------------------------------------------------------')
print(data + ', first: ' + algorithms[int(np.where(rank[data_id_]==1)[0])].strip() + ', second: ' + algorithms[int(np.where(rank[data_id_]==2)[0])].strip())
rank = rank.transpose()
# print(rank.shape)
rank_mean = np.mean(rank, axis=1)
print('Average rank')
print(rank_mean)
# rank_std = np.std(rank, axis=1)
rank_median = np.median(rank, axis=1)
print('Median rank')
print(rank_median)
order = np.argsort(rank_mean)
rank = rank[order][0: num_alg]
algorithms = [algorithms[idx] for idx in order]
algorithms = [algorithms[idx_new] for idx_new in np.arange(num_alg)]
print(algorithms)
rank_df = pd.concat([pd.DataFrame({algorithms[i]: rank[i, :]}) for i in range(num_alg)], axis=1)
# print(rank_df.head)
data_df = rank_df.melt(var_name='algorithm', value_name='Rank')
fig, ax = plt.subplots(1, 1, figsize=(12, 9), dpi=75)
# plt.figure(figsize=(6, 9))
b = sns.boxplot(y="algorithm", x="Rank", data=data_df, showmeans=True, order=algorithms, whis=[0, 100],
meanprops={"markerfacecolor":"black", "markeredgecolor":"black", "markersize":"50"}, palette=my_pal, linewidth=6)
# plt.ylabel("algorithm", size=18)
plt.xticks(ticks=np.arange(1, num_alg + 1, 1))
plt.xlabel("Rank", size=40)
# plt.plot(rank.mean(axis=1), np.arange(num_alg), '--r*', lw=2)
b.tick_params(labelsize=30)
ax.set_ylabel('')
plt.tight_layout()
plt.show() | [
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... | 2.244243 | 1,216 |
'''logger.py - the Datadog logger'''
import collections
import datadog
import datetime
import os
import logging
import rh_logger
import rh_logger.api
import sys
import traceback
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... | 3.272727 | 55 |
import os
import pandas as pd
import spacy
from sklearn.feature_extraction.text import CountVectorizer
import datetime
import numpy as np
from processing import get_annee_scolaire
if __name__ == "__main__":
#print("files", os.listdir("data_processed"))
##########################
# Chargement des donnes
##########################
path_g = os.path.join("data_processed", "greves.pk")
g = pd.read_pickle(path_g)
g["ind"] = g.ind.map(lambda x: 1 if x == "GREVE" else 0)
g = g[["taux_grevistes", "nos", "ind", "greves_manquantes"]]
path_m = os.path.join("data_processed", "menus.pk")
m = pd.read_pickle(path_m)
path_fe = os.path.join("data_processed", "frequentation_effectif.pk")
fe = pd.read_pickle(path_fe)
path_ferie = os.path.join("data_processed", "feries.pk")
feries = pd.read_pickle(path_ferie)
path_vacs = os.path.join("data_processed", "vacances.pk")
vacances = pd.read_pickle(path_vacs)
path_epidemies = os.path.join("data_processed", "epidemies.pk")
epidemies = pd.read_pickle(path_epidemies)
path_religions = os.path.join("data_processed", "religions.pk")
religions = pd.read_pickle(path_religions)
##########################
# Join sur les dates des diffrentes BDD
##########################
df = fe.groupby("date")[["prevision", "reel", "effectif"]].sum().join(g).join(m).join(feries).join(vacances).join(epidemies).join(religions)
##########################
# Remplacement des valeurs manquantes
##########################
for col in df.isnull().sum()[df.isnull().sum()>0].index.drop("menu"):
df[col] = df[col].fillna(0)
df["menu"] = df["menu"].map(lambda x: x if type(x) == list else [])
####################################
# Ajout des jours, mois semaines, anne scolaire, repas noel
####################################
dic_jour = {0: "Lundi", 1: "Mardi", 2: "Mercredi", 3: "Jeudi", 4: "Vendredi", 5: "Samedi", 6: "Dimanche"}
dic_mois = {1: "Janvier", 2: "Fevrier", 3: "Mars", 4: "Avril", 5: "Mai", 6: "Juin", 7: "Juillet", 8: "Aout",
9: "Septembre", 10: "Octobre", 11: "Novembre", 12: "Decembre"}
df["jour"] = df.index.weekday
df["jour"] = df["jour"].apply(lambda x: dic_jour[x])
df["semaine"] = df.index.week
df["mois"] = df.index.month
df["mois"] = df["mois"].apply(lambda x: dic_mois[x])
df["annee_scolaire"] = df.index.to_series().map(get_annee_scolaire)
date_repas_noel = ["2012-12-20", "2013-12-19", "2014-12-18", "2015-12-17", "2016-12-15",
"2017-12-21", "2018-12-20"]
l_noel = [datetime.datetime.strptime(x, '%Y-%m-%d') for x in date_repas_noel]
df_noel = pd.DataFrame(l_noel, columns=["date"])
df_noel["repas_noel"] = 1
df = df.join(df_noel.set_index("date"))
df["repas_noel"] = df["repas_noel"].fillna(0)
####################################
# Ajout du gaspillage
####################################
assert df.isnull().sum().sum() == 0
df["gaspillage_volume"] = df["prevision"] - df["reel"]
df["gaspillage_pourcentage"] = 100 * (df["prevision"] - df["reel"]) / df["prevision"]
####################################
# Ajout des variables lies au menu
####################################
nlp = spacy.load("fr_core_news_sm")
corpus = df['menu'].apply(lambda x: "".join([i + " " for i in x]))
corpus = corpus.dropna()
# stop_word
liste = ['04', '10', '17', '18225', '2015', '2016', '220gr', '268', '29', '500', '500g', '5kg', '850''500', '500g',
'5kg', '850', 'ab', 'an', 'au', 'aux', 'avec', 'baut', 'bbc', 'de', 'des', 'du', 'en', 'et', 'gr', 'kg',
'la', 'le', 'les', 'ou', 'par', 's17', 'sa', 'sans', 'ses', 'son']
# Create CountVectorizer object
vectorizer = CountVectorizer(strip_accents='ascii', stop_words=liste, lowercase=True, ngram_range=(1, 1))
# Generate matrix of word vectors
bow_matrix = vectorizer.fit_transform(corpus)
# Convert bow_matrix into a DataFrame
bow_df = pd.DataFrame(bow_matrix.toarray())
# Map the column names to vocabulary
bow_df.columns = vectorizer.get_feature_names()
bow_df.index = df.index
# feature porc
l_porc = ["carbonara", "carbonata", "cassoulet", "chipo", "chipolatas", "choucroute",
"cordon", "croziflette", "francfort", "jambon", "knacks", "lardons", "porc", "rosette",
"saucisse", "saucisses", "tartiflette"]
df["porc"] = sum([bow_df[alim] for alim in l_porc])
df['porc'] = df['porc'] > 0
df['porc'] = df['porc'].astype('int')
# feature viande
l_viande = ["roti", "agneau", "blanquette", "boeuf", "boudin", "boulettes",
"bourguignon", "bourguignonne", "canard", "carne", "chapon", "colombo",
"couscous", "dinde", "escalope", "farci", "foie", "kebab", "lapin", "merguez",
"mouton", "napolitaines", "nuggets", "paupiette", "pintade",
"poulet", "steak", "stogonoff", "strogonoff", "tagine", "tajine",
"veau", "viande", "volaile", "volaille", "carbonara", "carbonata", "cassoulet", "chipo", "chipolatas",
"choucroute", "cordon", "croziflette", "francfort", "jambon", "knacks", "lardons", "porc", "rosette",
"saucisse", "saucisses", "tartiflette", "parmentier"]
df["viande"] = sum([bow_df[alim] for alim in l_viande])
df['viande'] = df['viande'] > 0
df['viande'] = df['viande'].astype('int')
df = df.reset_index().rename(columns = {"index":"date"})
l_index = ["2018-01-22", "2017-10-09", "2017-05-09", "2016-10-18", "2016-04-25", "2015-05-26", "2014-11-24",
"2014-05-26", "2014-03-31", "2014-01-20", "2012-01-16", "2012-01-30", "2012-07-02", "2012-10-01",
"2011-01-17", "2011-01-31", "2011-09-13", "2015-06-22", "2015-01-19", "2014-06-30", "2012-06-18",
"2011-06-20"]
index = [datetime.datetime.strptime(x, '%Y-%m-%d') for x in l_index]
for i in index:
df.loc[df[df["date"] == i].index, "viande"] = 1
# traitement particulier des lasagnes napolitaines pour viter les confusions avec les lasagnes de poisson
l_index = ["2016-02-22", "2016-02-04", "2015-11-23", "2015-11-17", "2015-10-05",
"2015-05-04", "2015-01-26", "2014-12-15", "2013-09-23", "2012-10-09", "2012-05-21", "2012-02-27",
"2011-11-03", "2011-09-05", "2011-05-09", "2012-12-10", "2013-12-02", "2014-05-12", "2016-05-09"]
index = [datetime.datetime.strptime(x, '%Y-%m-%d') for x in l_index]
for i in index:
df.loc[df[df["date"] == i].index, "viande"] = 1
# traitement particulier de certains termes qui peuvent tre utiliss pour du poisson ou de la viande sauts, chili, pot au feu, bolognaise, courgette farcie,ravioli
l_index = ["2016-01-28", "2016-03-17", "2016-03-07", "2015-09-15", "2012-12-06", "2012-05-03", "2012-02-09",
"2011-11-03",
"2011-09-13", "2011-06-07", "2011-04-04", "2014-06-12", "2012-11-12", "2015-06-22"]
index = [datetime.datetime.strptime(x, '%Y-%m-%d') for x in l_index]
for i in index:
df.loc[df[df["date"] == i].index, "viande"] = 1
# traitement particulier pour parmentier vgtale, steack de soja
l_index = ["2019-11-25", "2014-06-20"]
index = [datetime.datetime.strptime(x, '%Y-%m-%d') for x in l_index]
for i in index:
df.loc[df[df["date"] == i].index, "viande"] = 0
# feature poisson
l_poisson = ["poissons", "sardines", "perray", "thon", "calamar", "lieu", "colin", "crabe", "crevette", "crustace",
"dorade", "maquereau", "poisson", "rillette", "sardine", "saumon"]
df["poisson"] = sum([bow_df[alim] for alim in l_poisson])
df['poisson'] = df['poisson'] > 0
df['poisson'] = df['poisson'].astype('int')
df['poisson'][(df['viande'] == 1) & (df['poisson'] == 1)] = np.zeros(
len(df['poisson'][(df['viande'] == 1) & (df['poisson'] == 1)]))
# traitement particulier parmentier poisson #nuggets de poisson,steack de soja et sale au thon, carbo de saumon
l_index = ["2019-05-17", "2019-05-17", "2019-02-01", "2018-11-23", "2018-10-19", "2018-09-14", "2018-06-05",
"2018-03-27", "2018-01-16", "2017-12-01", "2017-09-22", "2017-05-05", "2016-05-03", "2016-02-26",
"2016-01-15", "2015-11-20", "2015-09-22", "2015-09-08", "2015-06-05", "2014-09-08", "2014-03-25",
"2014-02-18", "2014-01-24", "2013-12-10", "2013-11-29", "2013-10-01", "2012-12-14", "2012-10-19",
"2012-09-21", "2012-03-16", "2012-01-20", "2011-09-09", "2011-03-18", "2019-03-08"]
index = [datetime.datetime.strptime(x, '%Y-%m-%d') for x in l_index]
for i in index:
df.loc[df[df["date"] == i].index, "viande"] = 0
df.loc[df[df["date"] == i].index, "poisson"] = 1
# traitement particulier paella de la mer, filet
l_index = ['2011-01-10', '2012-01-09', '2011-01-07', "2012-01-06"]
index = [datetime.datetime.strptime(x, '%Y-%m-%d') for x in l_index]
for i in index:
df.loc[df[df["date"] == i].index, "poisson"] = 1
# 2 menus : vg et viande, on considre que c'est un menu vg
l_index = ["2015-11-13", "2015-09-11"]
index = [datetime.datetime.strptime(x, '%Y-%m-%d') for x in l_index]
for i in index:
df.loc[df[df["date"] == i].index, "poisson"] = 0
df.loc[df[df["date"] == i].index, "viande"] = 0
# 2 menus : poisson et viande, on considre que c'est un menu poisson
l_index = ["2015-11-20", "2015-10-16", "2015-10-02", "2015-09-25", "2015-09-18", "2015-09-04", "2015-06-25",
"2015-06-11"]
index = [datetime.datetime.strptime(x, '%Y-%m-%d') for x in l_index]
for i in index:
df.loc[df[df["date"] == i].index, "poisson"] = 1
df.loc[df[df["date"] == i].index, "viande"] = 0
# menu inconnu, mais probablement avec viande d'aprs le modle
df.loc[df[df["date"] == datetime.datetime.strptime("2015-10-15", "%Y-%m-%d")].index, "viande"] = 1
# feature bio
df['bio'] = bow_df["bio"]
# set date as index
df = df.set_index("date")
###############################################################
# Ajout des 4 premiers et 4 derniers jours de l'anne scolaire (grosse incertitude)
#############################################################
ind = []
temp = []
subset = df.copy()
#print("subset", subset["annee_scolaire"].unique()[1:])
for i in range(1, 5):
for annee in subset["annee_scolaire"].unique()[1:]:
temp.append(min(subset[(subset.index.year == min(subset[subset["annee_scolaire"] == annee].index.year)) & (
subset["annee_scolaire"] == annee)].index))
df.loc[temp, "4_premiers_jours"] = 1
ind.append(temp)
subset.drop(temp, inplace=True)
temp = []
for i in range(1, 5):
for annee in subset["annee_scolaire"].unique()[:-1]:
temp.append(max(subset[(subset.index.year == max(subset[subset["annee_scolaire"] == annee].index.year)) & (
subset["annee_scolaire"] == annee)].index))
df.loc[temp, "4_derniers_jours"] = 1
ind.append(temp)
subset.drop(temp, inplace=True)
temp = []
df["4_derniers_jours"].fillna(0, inplace=True)
df["4_premiers_jours"].fillna(0, inplace=True)
####################################
# Tests (longueur et valeurs manquantes)
####################################
assert len(df) == 1188
df.to_pickle("data_processed/global.pk")
df.to_excel("data_processed/global.xlsx")
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# -*- coding: utf-8 -*-
# Generated by Django 1.10.2 on 2017-01-03 15:34
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
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1... | 2.73913 | 69 |
import numpy as np
import matplotlib.pyplot as mp
import matplotlib.cm as mpcm
import matplotlib.colors as mpc
import scipy.stats as ss
# plotting settings
lw = 1.5
mp.rc('font', family = 'serif')
mp.rcParams['text.latex.preamble'] = [r'\boldmath']
mp.rcParams['axes.linewidth'] = lw
mp.rcParams['lines.linewidth'] = lw
cm = mpcm.get_cmap('plasma')
# datafiles
ppds = ['cmb', 'loc']
sums = ['ptes', 'prs']
# posterior summaries
post_means = np.genfromtxt('gw_grb_h_0_posterior_means.csv', \
delimiter=',')
post_vars = np.genfromtxt('gw_grb_h_0_posterior_vars.csv', \
delimiter=',')
n_h_0_true = post_means.shape[0]
n_bs = post_means.shape[1]
print n_bs
h_0_true_col = [cm(col) for col in np.linspace(0.2, 0.8, n_h_0_true)]
fig, axes = mp.subplots(1, 2, figsize=(12, 5))
for i in range(n_h_0_true):
print '* H_0 = {:5.2f}'.format(post_means[i, 0])
to_print = 'posterior mean = {:5.2f} +/- {:4.2f}'
print to_print.format(np.mean(post_means[i, 1:]), \
np.std(post_means[i, 1:]))
to_print = 'posterior sigma = {:5.2f} +/- {:4.2f}'
print to_print.format(np.mean(np.sqrt(post_vars[i, 1:])), \
np.std(np.sqrt(post_vars[i, 1:])))
kde = ss.gaussian_kde(post_means[i, 1:])
grid = np.linspace(np.min(post_means[i, 1:]), \
np.max(post_means[i, 1:]), \
1000)
axes[0].plot(grid, kde.evaluate(grid), color=h_0_true_col[i])
axes[0].axvline(post_means[i, 0], color=h_0_true_col[i], ls='--')
kde = ss.gaussian_kde(np.sqrt(post_vars[i, 1:]))
grid = np.linspace(np.min(np.sqrt(post_vars[i, 1:])), \
np.max(np.sqrt(post_vars[i, 1:])), \
1000)
axes[1].plot(grid, kde.evaluate(grid), color=h_0_true_col[i], \
label=r'$H_0 = {:5.2f}$'.format(post_vars[i, 0]))
axes[0].set_xlabel(r'$\bar{H}_0$', fontsize=18)
axes[0].set_ylabel(r'${\rm Pr}(\bar{H}_0)$', fontsize=18)
axes[0].tick_params(axis='both', which='major', labelsize=12)
axes[1].set_xlabel(r'$\sigma_{H_0}$', fontsize=18)
axes[1].set_ylabel(r'${\rm Pr}(\sigma_{H_0})$', fontsize=18)
axes[1].tick_params(axis='both', which='major', labelsize=12)
axes[1].legend(loc='upper right', fontsize=14)
fig.suptitle('Bootstrap-Averaged Posterior Means / Sigmas', \
fontsize=18)
fig.savefig('gw_grd_h_0_bs_avg_posterior_moments.pdf', \
bbox_inches = 'tight')
mp.close(fig)
# PPD summaries
for i in range(len(ppds)):
for j in range(len(sums)):
# read data
fname = 'gw_grb_h_0_' + ppds[i] + '_ppd_' + sums[j]
data = np.genfromtxt(fname + '.csv', delimiter=',')
n_bs = data.shape[1]
print n_bs
# plot
n_h_0_true = data.shape[0]
fig, axes = mp.subplots(1, n_h_0_true, \
figsize=(6 * n_h_0_true, 5))
if ppds[i] == 'cmb':
fig.suptitle(r'$\hat{H}_0^{\rm CMB}\, {\rm Prediction}$', \
fontsize=18)
else:
fig.suptitle(r'$\hat{H}_0^{\rm CDL}\, {\rm Prediction}$', \
fontsize=18)
if sums[j] == 'ptes':
x_label = r'$p$'
y_label = r'${\rm Pr}(p)$'
else:
x_label = r'$\rho$'
y_label = r'${\rm Pr}(\rho)$'
for k in range(n_h_0_true):
kde = ss.gaussian_kde(data[k, 1:])
grid = np.linspace(np.min(data[k, 1:]), \
np.max(data[k, 1:]), \
1000)
axes[k].plot(grid, kde.evaluate(grid), color=cm(0.5))
axes[k].set_xlabel(x_label, fontsize=18)
axes[k].set_ylabel(y_label, fontsize=18)
axes[k].tick_params(axis='both', which='major', labelsize=12)
axes[k].set_title(r'$H_0 = {:5.2f}$'.format(data[k, 0]), \
fontsize=18)
# finish plot
fig.savefig(fname + '.pdf', bbox_inches = 'tight')
mp.close(fig)
# quick check of required numbers of samples
n_ref = 51.0
mu_obs = np.array([67.81, 73.24])
sig_obs = np.array([0.92, 1.74])
n_sigma_sv = 1.0
n_sigma_thresh = 3.0
n_sigma_diff = [(mu_obs[1] - mu_obs[0]) / np.sqrt(post_vars[i, 1]), \
(mu_obs[0] - mu_obs[1]) / np.sqrt(post_vars[i, 1])]
var_ratio = [sig_obs[1] ** 2 / post_vars[i, 1], \
sig_obs[0] ** 2 / post_vars[i, 1]]
print n_sigma_diff
print var_ratio
n_req = np.zeros(2)
n_req[0] = n_ref * num_ratio(n_sigma_diff[0], n_sigma_sv, \
n_sigma_thresh, var_ratio[0])[0]
ln_rho = -2.0 * np.log(rho(n_sigma_diff[0], n_sigma_sv, \
var_ratio[0], n_ref, n_req[0]))
print n_req[0], ln_rho, n_sigma_thresh ** 2
n_req[1] = n_ref * num_ratio(n_sigma_diff[1], n_sigma_sv, \
n_sigma_thresh, var_ratio[1])[1]
ln_rho = -2.0 * np.log(rho(n_sigma_diff[1], n_sigma_sv, \
var_ratio[1], n_ref, n_req[1]))
print n_req[1], ln_rho, n_sigma_thresh ** 2
n_grid = np.arange(n_ref, 5000.0)
mp.loglog(n_grid, rho_num(n_sigma_diff[0], n_sigma_sv, n_ref / n_grid), 'r', lw=1.0)
mp.plot(n_grid, 1.0 / rho_den(var_ratio[0], n_ref / n_grid), 'g', lw=1.0)
mp.plot(n_grid, 1.0 / rho_den(var_ratio[1], n_ref / n_grid), 'b', lw=1.0)
mp.plot(n_grid, -2.0 * np.log(rho(n_sigma_diff[0], n_sigma_sv, var_ratio[0], \
n_ref, n_grid)), 'g')
mp.plot(n_grid, -2.0 * np.log(rho(n_sigma_diff[1], n_sigma_sv, var_ratio[1], \
n_ref, n_grid)), 'b')
mp.axhline(n_sigma_thresh ** 2, color='k', linestyle='-.')
mp.axvline(n_req[0], color='g', linestyle='-.')
mp.axvline(n_req[1], color='b', linestyle='-.')
mp.xlabel(r'$N$')
mp.ylabel(r'$f(N)$')
mp.xlim(n_ref, 5000)
mp.ylim(0.3, 40.0)
mp.savefig('gw_grb_h_0_ppd_samp_var_limits.pdf', bbox_inches='tight')
mp.show()
exit()
print num_ratio(4.53, n_sigma_sv, n_sigma_thresh, 2.1)
print 5.43, mu_obs[1] - mu_obs[0]
print 1.2, np.sqrt(post_vars[i, 1])
print 5.43 / 1.2, n_sigma_diff[0]
m = 3.0
n = 1.0
d = 3.77 # 4.53
vrat = 1.46 # 2.1
print ((d*n+np.sqrt((d*n)**2-(vrat*m**2-d**2)*(m**2-n**2)))/(vrat*m**2-d**2))**2
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... | 1.875211 | 2,957 |
import pygame
from laboratory.base import ChessBoard, ChessHorse, Grid
import os
os.environ["SDL_VIDEO_WINDOW_POS"] = "400, 100"
surface = pygame.display.set_mode((600, 600))
pygame.display.set_caption("Chess knight move")
pygame.init()
grid = ChessBoard()
horse = ChessHorse()
cells = Grid()
main()
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1... | 2.869159 | 107 |
from torch import nn
from torchvision.models.detection.backbone_utils import resnet_fpn_backbone
from torchvision.models.utils import load_state_dict_from_url
from .utils import pooling
from .utils.class_head import ClassificationHead
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116... | 3.590909 | 66 |