def __init__(self, tilesx, tilesy, posx, posy): self.tilesx = tilesx self.tilesy = tilesy self.font = pyglet.image.load(data_path("tiles.png")).get_texture() self.ch_tex = Texture(tilesx, tilesy, GL_RED, GL_UNSIGNED_BYTE) self.fg_tex = Texture(tilesx, tilesy, GL_RGB, GL_UNSIGNED_BYTE) self.bg_tex = Texture(tilesx, tilesy, GL_RGB, GL_UNSIGNED_BYTE) self.shader = Shader( data_file("tiles.vert"), data_file("tiles.frag") ) self.shader.bind() self.shader.uniformf("ntiles", tilesx, tilesy) self.shader.uniformf("potsize", self.ch_tex.pot_width, self.ch_tex.pot_height) self.shader.uniformf("fontscale", self.font.tex_coords[6], self.font.tex_coords[7]) self.shader.uniformf("fontbg", 1.0, 0.0, 1.0) self.shader.uniformf("nchars", 16.0, 16.0) self.shader.unbind() self.batch = pyglet.graphics.Batch() w = tilesx * self.CHAR_WIDTH; h = tilesy * self.CHAR_HEIGHT self.batch.add( 4, GL_QUADS, None, ("v2f/static", (posx, posy, posx+w, posy, posx+w, posy+h, posx, posy+h)), ("t2f/static", (0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0)) )
def __init__(self, tilesx, tilesy, posx, posy): self.tilesx = tilesx self.tilesy = tilesy self.font = pyglet.image.load(data_path("tiles.png")).get_texture() self.ch_tex = Texture(tilesx, tilesy, GL_RED, GL_UNSIGNED_BYTE) self.fg_tex = Texture(tilesx, tilesy, GL_RGB, GL_UNSIGNED_BYTE) self.bg_tex = Texture(tilesx, tilesy, GL_RGB, GL_UNSIGNED_BYTE) self.shader = Shader(data_file("tiles.vert"), data_file("tiles.frag")) self.shader.bind() self.shader.uniformf("ntiles", tilesx, tilesy) self.shader.uniformf("potsize", self.ch_tex.pot_width, self.ch_tex.pot_height) self.shader.uniformf("fontscale", self.font.tex_coords[6], self.font.tex_coords[7]) self.shader.uniformf("fontbg", 1.0, 0.0, 1.0) self.shader.uniformf("nchars", 16.0, 16.0) self.shader.unbind() self.batch = pyglet.graphics.Batch() w = tilesx * self.CHAR_WIDTH h = tilesy * self.CHAR_HEIGHT self.batch.add( 4, GL_QUADS, None, ("v2f/static", (posx, posy, posx + w, posy, posx + w, posy + h, posx, posy + h)), ("t2f/static", (0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0)))
def _load_data(fn): data = [] o = open(data_path(fn)) for w in re.split("\s+", o.read()): if len(w) > 0: data.append(w.lower().strip()) o.close() return list(set(data))
def __init__(self, filename): self.init() f = open(data_path(filename)) self.all = [] self.tags = {} for line in f.readlines(): if len(line.strip()) == 0: continue parts = line.split("|") w = self.add_word(parts[0].strip().split(":")) self.all.append(w) if len(parts) == 2: for tag in parts[1].strip().split(): self.tags.setdefault(tag, []).append(w) f.close()
from os.path import join import pandas as pd from econtools import load_or_build, loadbuild_cli, force_iterable from util import src_path, data_path from clean.psid.codebook import family_std, indiv_std PSID_PATH = src_path('psid') BANKVARS = ('bank_filed', 'bank_year', 'bank_state', 'bank_count', 'bank_debt_filed', 'bank_debt_remained',) @load_or_build(data_path('panel_full.p')) def load_full_panel(_rebuild_down=False): """ Create full PSID panel, conditional on being interviewed in 1996. This does not create any variables or restrict the sample in any way, other than starting with 1996 households. """ df = load_individual_panel(_rebuild=_rebuild_down) df.index.name = 'person_id' # Merge family_1996 and indiv, inner match_cols = ('interview_number', 1996) this_family = load_family_year(1996, _rebuild=_rebuild_down) this_family.index = this_family.pop(match_cols).squeeze().values df = df.join(this_family, on=[match_cols], how='inner')
from __future__ import division, print_function import numpy as np import pandas as pd from econtools import load_or_build from util import data_path from clean.psid import load_full_panel @load_or_build(data_path('nonbankruptpeople2.dta')) def load_nonbankrupt_panel(_rebuild_down=False): df = uniform_cleaning(_rebuild_down=_rebuild_down) df = df.query('bank_filed == 0').copy() return df @load_or_build(data_path('bankruptpeople2.dta')) def load_bankrupt_panel(_rebuild_down=False): df = uniform_cleaning(_rebuild_down=_rebuild_down) df = df.query('bank_filed == 1').copy() df['event_year'] = df['year'] - df['bank_year'] # Restrict to filling years after 1985 df = df[df['event_year'].notnull()].copy() df = df[df['bank_year'] >= 1985].copy()
def packages_path(*args): ''' Get path to the packages directory. ''' return util.data_path('packages')
def package_path(package, *args): ''' Get path to a package data directory or file. ''' return util.data_path('packages', package, *args)
from util import src_path, data_path from clean.psid.codebook import family_std, indiv_std PSID_PATH = src_path('psid') BANKVARS = ( 'bank_filed', 'bank_year', 'bank_state', 'bank_count', 'bank_debt_filed', 'bank_debt_remained', ) @load_or_build(data_path('panel_full.p')) def load_full_panel(_rebuild_down=False): """ Create full PSID panel, conditional on being interviewed in 1996. This does not create any variables or restrict the sample in any way, other than starting with 1996 households. """ df = load_individual_panel(_rebuild=_rebuild_down) df.index.name = 'person_id' # Merge family_1996 and indiv, inner match_cols = ('interview_number', 1996) this_family = load_family_year(1996, _rebuild=_rebuild_down) this_family.index = this_family.pop(match_cols).squeeze().values df = df.join(this_family, on=[match_cols], how='inner')
def service_path(service, *args): ''' Get path to a service data directory or file. ''' return util.data_path('service', service, *args)
def services_path(): ''' Get path to the services data directory. ''' return util.data_path('service')
def _logfile(): return util.data_path('monit.log')
def _cfgfile(): return util.data_path('monit.rc')