def get_wealth_data(bin_weights, J, flag_graphs, output_dir):
'''
Inputs:
bin_weights = ability weights (Jx1 array)
J = number of ability groups (scalar)
flag_graphs = whether or not to graph distribution (bool)
output_dir = path to the starting data
Output:
Saves a pickle of the desired wealth percentiles. Graphs those levels.
'''
if flag_graphs:
wealth_data_graphs(output_dir)
perc_array = np.zeros(J)
# convert bin_weights to integers to index the array of data moments
bins2 = (bin_weights * 100).astype(int)
perc_array = np.cumsum(bins2)
perc_array -= 1
wealth_data_array = np.zeros((78, J))
wealth_data_array[:, 0] = data2[:, :perc_array[0]].mean(axis=1)
# pull out the data moments for each percentile
for j in xrange(1, J):
wealth_data_array[:, j] = data2[
:, perc_array[j - 1]:perc_array[j]].mean(axis=1)
var_names = ['wealth_data_array']
dictionary = {}
for key in var_names:
dictionary[key] = locals()[key]
saved_moments_dir = os.path.join(output_dir, "Saved_moments")
utils.mkdirs(saved_moments_dir)
pkl_path = os.path.join(saved_moments_dir, "wealth_data_moments.pkl")
pickle.dump(dictionary, open(pkl_path, "wb"))
def test(vid_path="../examples/videos/sample_video1.avi"):
mkdirs("test_results")
start = time.time();
test_bgsub_fd(vid_path);
test_bgsub_es(vid_path);
test_bgsub_mog(vid_path);
time_taken = time.time() - start;
print "Tested all Background Subtraction methods ... [DONE] in " + str(time_taken) +" seconds"
def showEvent(self,evt):
self.first_run = True
mkdirs("data")
create_history_db()
self.message_loop_task.start()
self.urlComboBox.addItem("http://zc.trade.500.com/sfc/index.php")
urls = get_last_access_urls()
for url in urls:
self.urlComboBox.addItem(url)
def visualize_rep(VAE,
data_loader,
limit=3,
inter=2 / 3,
loc=-1,
z_dim=128,
output_dir='traverse_result'):
decoder = VAE.decode
encoder = VAE.encode
interpolation = torch.arange(-limit, limit + 0.1, inter)
fixed_idx = 0
fixed_img = data_loader.dataset.__getitem__(fixed_idx)
fixed_img = fixed_img.to('cpu').unsqueeze(0)
fixed_img_z = encoder(fixed_img)[:, :z_dim]
random_z = torch.rand(1, z_dim, 1, 1, device='cpu')
Z = {'fixed_img': fixed_img_z, 'random_z': random_z}
gifs = []
for key in Z:
z_ori = Z[key]
samples = []
for row in range(z_dim):
if loc != -1 and row != loc:
continue
z = z_ori.clone()
for val in interpolation:
z[:, row] = val
sample = F.sigmoid(decoder(z)).data
samples.append(sample)
gifs.append(sample)
samples = torch.cat(samples, dim=0).cpu()
title = '{}_latent_traversal(iter:{})'.format(key, 1)
output_dir = os.path.join(output_dir, str(1))
mkdirs(output_dir)
gifs = torch.cat(gifs)
print("gif size is {}".format(gifs.shape))
gifs = gifs.view(len(Z), z_dim, len(interpolation), 1, 256,
256).transpose(1, 2)
for i, key in enumerate(Z.keys()):
for j, val in enumerate(interpolation):
save_image(tensor=gifs[i][j].cpu(),
filename=os.path.join(output_dir,
'{}_{}.jpg'.format(key, j)),
nrow=z_dim,
pad_value=1)
def main():
mkdirs(INSTALLDIR)
os.environ.update(make_build_env())
args = parse_args()
if on_travis() and not args.test_only:
fetch_and_build()
if on_travis():
dbs = ('sqlite3', 'mysql')
else:
dbs = ('sqlite3', )
for db in dbs:
shell('rm -rf {}/*'.format(INSTALLDIR))
start_and_test_with_db(db)
def save(self):
cp = ConfigParser.RawConfigParser()
cp.add_section(self.name)
self._extensions and cp.set(self.name, 'extensions', self._extensions)
self._cache_size_limit and cp.set(self.name, 'cache_size_limit',
self._cache_size_limit)
self._cache_key and cp.set(self.name, 'cache_key', self._cache_key)
self._proxy_ip and cp.set(self.name, 'proxy_ip', self._proxy_ip)
if not os.path.exists(os.path.dirname(self._path)):
mkdirs(os.path.dirname(self._path))
with open(self._path, 'wb') as f:
cp.write(f)
def run(summ_path,
ref_path,
rouge_args=None,
verbose=False,
saveto=None,
eos=".",
ignore_empty=False,
stemming=True):
s = settings.Settings()
s._load()
stime = time()
with tempfile.TemporaryDirectory() as dirpath:
sys_root, model_root = [
os.path.join(dirpath, _) for _ in ["system", "model"]
]
print("Preparing documents...", end=" ")
utils.mkdirs([sys_root, model_root])
ignored = utils.split_files(model_file=ref_path,
system_file=summ_path,
model_dir=model_root,
system_dir=sys_root,
eos=eos,
ignore_empty=ignore_empty)
print("%d line(s) ignored" % len(ignored))
print("Running ROUGE...")
log_level = logging.ERROR if not verbose else None
r = pyrouge.Rouge155(rouge_dir=os.path.dirname(s.data['ROUGE_path']),
log_level=log_level,
stemming=stemming)
r.system_dir = sys_root
r.model_dir = model_root
r.system_filename_pattern = r's.(\d+).txt'
r.model_filename_pattern = 'm.[A-Z].#ID#.txt'
data_arg = "-e %s" % s.data['ROUGE_data']
if not rouge_args:
rouge_args = ['-c', 95, '-r', 1000, '-n', 2, '-a']
rouge_args_str = " ".join([str(_) for _ in rouge_args])
else:
rouge_args_str = rouge_args
rouge_args_str = "%s %s" % (data_arg, rouge_args_str)
output = r.convert_and_evaluate(rouge_args=rouge_args_str)
if saveto is not None:
saveto = open(saveto, 'w')
utils.tee(saveto, output)
print("Elapsed time: %.3f seconds" % (time() - stime))
def save_checkpoint(self, iteration):
encoderMx_path = os.path.join(self.ckpt_dir,
'iter_%s_encoderMx.pt' % iteration)
encoderMy_path = os.path.join(self.ckpt_dir,
'iter_%s_encoderMy.pt' % iteration)
decoderMy_path = os.path.join(self.ckpt_dir,
'iter_%s_decoderMy.pt' % iteration)
mkdirs(self.ckpt_dir)
torch.save(self.encoderMx, encoderMx_path)
torch.save(self.encoderMy, encoderMy_path)
torch.save(self.decoderMy, decoderMy_path)
def plot_result(self, data_plot, save_fig_dir, is_plot=True):
utils.mkdirs(save_fig_dir)
columns = list(data_plot.columns)
print("Start to plot and save {} figures to {} ...".format(
len(columns) - 1, save_fig_dir))
print("Head of data plot")
print(data_plot.head())
x_offset = -0.07
y_offset = 0.01
mpl.style.use("seaborn")
model_column = columns[0]
for score_solumn in columns[1:]:
# Sort by ascending score
data_plot.sort_values(score_solumn, ascending=True, inplace=True)
ax = data_plot.plot(kind="bar",
x=model_column,
y=score_solumn,
legend=None,
color='C1',
figsize=(len(self.models) + 1, 4),
width=0.3)
title = "Mean {} score - {} cross validation".format(
score_solumn, self.cv)
ax.set(title=title, xlabel=model_column, ylabel=score_solumn)
ax.tick_params(axis='x', rotation=0)
# Set lower and upper limit of y-axis
min_score = data_plot.loc[:, score_solumn].min()
max_score = data_plot.loc[:, score_solumn].max()
y_lim_min = (min_score - 0.2) if min_score > 0.2 else 0
y_lim_max = (max_score + 1) if max_score > 1 else 1
ax.set_ylim([y_lim_min, y_lim_max])
# Show value of each column to see clearly
for p in ax.patches:
b = p.get_bbox()
text_value = "{:.4f}".format(b.y1)
ax.annotate(text_value, xy=(b.x0 + x_offset, b.y1 + y_offset))
save_fig_path = os.path.join(save_fig_dir,
"{}.png".format(score_solumn))
plt.savefig(save_fig_path, dpi=800)
print("Plot and save {} figures to {} done".format(
len(columns) - 1, save_fig_dir))
if is_plot:
plt.show()
def __init__(self, datadir, db='sqlite3'):
self.db = db
self.datadir = datadir
self.central_conf_dir = join(datadir, 'conf')
self.seafile_conf_dir = join(datadir, 'seafile-data')
self.ccnet_conf_dir = join(datadir, 'ccnet')
self.log_dir = join(datadir, 'logs')
mkdirs(self.log_dir)
self.ccnet_log = join(self.log_dir, 'ccnet.log')
self.seafile_log = join(self.log_dir, 'seafile.log')
self.ccnet_proc = None
self.seafile_proc = None
def get_train_data_generator(data_dir, logger, save=False):
"""
Function to return data generators for train and valid data sets.
"""
train_dir = pjoin(data_dir, "train")
valid_dir = pjoin(data_dir, "valid")
logger.info("Summarizing sample info in train set.")
train_num = log_data_info(train_dir, logger)
logger.info("Summarizing sample info in valid set.")
valid_num = log_data_info(valid_dir, logger)
logger.info(
"Total number of training data: {}; Total number of validation data: {}."
.format(train_num, valid_num))
# prepare data augmentation configuration
train_datagen = ImageDataGenerator(
preprocessing_function=preprocess_input,
rotation_range=30, # 图片随机转动的角度
width_shift_range=0.2, # 图片随机水平偏移的幅度
height_shift_range=0.2, # 图片随机竖直偏移的幅度
shear_range=0.2, # 逆时针方向的剪切变换角度
zoom_range=0.2, # 随机缩放的幅度
horizontal_flip=True, # 随机水平翻转
vertical_flip=True # 随机竖直翻转
)
valid_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)
save_dir = None
if save:
save_dir = pjoin(data_dir, "gen_train")
utils.mkdirs(save_dir)
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(IMG_HEIGHT, IMG_WIDTH),
batch_size=PATCH_BATCH_SIZE,
class_mode='categorical',
save_to_dir=save_dir)
valid_generator = valid_datagen.flow_from_directory(
valid_dir,
target_size=(IMG_HEIGHT, IMG_WIDTH),
batch_size=PATCH_BATCH_SIZE,
class_mode='categorical')
return (train_generator, train_num), (valid_generator, valid_num)
def run(iterator, args=None):
sindarin = args.folder + '/run.sin'
runfolder = args.folder + '-' + str(iterator)
mkdirs(runfolder)
shutil.copyfile(sindarin, os.path.join(runfolder, 'run.sin'))
with cd(runfolder):
replace_file('run.sin', args.scan_object, iterator)
subprocess.call('rm -f *grid', shell=True)
if (not args.dryrun and not os.path.isfile('done')):
whizard_run('whizard -r', 'run.sin')
with open('done', 'a'):
os.utime('done', None)
print 'done with ' + runfolder
else:
print 'skipping ' + runfolder
def run(self):
mkdirs("cache_web_pages/html")
send_msg("开始分析基本面..")
matchs = self.extract_matchs(str(self.targetUrl))
self.batch_download_data_pages(matchs)
for match in matchs:
msg = "获取初赔 %s" % match["odds_url"]
send_msg(msg)
self.parse_odds(match, get_cache_web_file_name(match["odds_url"]))
msg = "分析基本面 %s" % match["base_face_url"]
send_msg(msg)
self.parse_baseface(match, get_cache_web_file_name(match["base_face_url"]))
self.dataset = matchs
send_msg("refresh_match_grid");
send_msg("完成.")
def save_checkpoint(self, iteration):
encoder_path = os.path.join(self.ckpt_dir,
'iter_%s_encoder.pt' % iteration)
decoder_path = os.path.join(self.ckpt_dir,
'iter_%s_decoder.pt' % iteration)
rvec_path = os.path.join(self.ckpt_dir, 'iter_%s_rvec.pt' % iteration)
D_path = os.path.join(self.ckpt_dir, 'iter_%s_D.pt' % iteration)
mkdirs(self.ckpt_dir)
torch.save(self.encoder, encoder_path)
torch.save(self.decoder, decoder_path)
torch.save(self.rvec, rvec_path)
torch.save(self.D, D_path)
def set_up_dir(self):
project_path = pjoin("Experiments/%s_%s" % (self.args.project_name, self.ExpID))
if hasattr(self.args, 'resume_ExpID') and self.args.resume_ExpID:
project_path = get_project_path(self.args.resume_ExpID)
if self.args.debug: # debug has the highest priority. If debug, all the things will be saved in Debug_dir
project_path = "Debug_Dir"
self.weights_path = pjoin(project_path, "weights")
self.gen_img_path = pjoin(project_path, "gen_img")
self.cache_path = pjoin(project_path, ".caches")
self.log_path = pjoin(project_path, "log")
self.logplt_path = pjoin(project_path, "log", "plot")
self.logtxt_path = pjoin(project_path, "log", "log.txt")
mkdirs(self.weights_path, self.gen_img_path, self.logplt_path, self.cache_path)
self.logtxt = open(self.logtxt_path, "a+")
self.script_hist = open('.script_history', 'a+') # save local script history, for convenience of check
def download_jdk():
if not search_jdk() or settings["force_install_jdk"]:
jdk_link = settings["jdk_link_x64"] if utils.is_x64() else settings["jdk_link_x86"]
jdk_zip_name = settings["jdk_zip_name"]
extracted = utils.download_file_and_extract(jdk_link, jdk_zip_name)
if not extracted:
print("Extracting JDK have failed. Redownloading...")
download_jdk()
else:
pass
os.rename("jdk-14.0.2+12", "jdk-14.0.2") if os.path.exists("jdk-14.0.2+12") else None
default_directory = os.environ.get("APPDATA") if os.environ.get("APPDATA") is not None else os.getcwd()
created = utils.mkdirs(default_directory)
if not created:
directory = os.getcwd() # failed to create directory, can't do much here
else:
directory = default_directory
new_directory = os.path.join(directory, "jdk-14.0.2")
if os.path.exists(new_directory): shutil.rmtree(new_directory)
try:
shutil.move("jdk-14.0.2", directory)
except:
directory = os.getcwd()
# Set JAVA_HOME and PATH
extend_path(os.path.join(directory, "jdk-14.0.2"))
settings.setKey("jdk_installation_path", directory, False)
def load_feature(config, train: bool) -> Union[Tuple[np.ndarray], np.ndarray]:
"""
从 "{config.feature_folder}/*.csv" 文件中加载特征数据
Args:
config: 配置项
train (bool): 是否为训练数据
Returns:
- X (Tuple[np.ndarray]): 训练特征、测试特征和对应的标签
- X (np.ndarray): 预测特征
"""
feature_path = os.path.join(
config.feature_folder, "train.csv" if train == True else "predict.csv")
# 加载特征数据
df = pd.read_csv(feature_path)
features = [
str(i) for i in range(1, FEATURE_NUM[config.opensmile_config] + 1)
]
X = df.loc[:, features].values
Y = df.loc[:, 'label'].values
# 标准化模型路径
scaler_path = os.path.join(config.checkpoint_path, 'SCALER_OPENSMILE.m')
if train == True:
# 标准化数据
scaler = StandardScaler().fit(X)
# 保存标准化模型
utils.mkdirs(config.checkpoint_path)
joblib.dump(scaler, scaler_path)
X = scaler.transform(X)
# 划分训练集和测试集
x_train, x_test, y_train, y_test = train_test_split(X,
Y,
test_size=0.2,
random_state=42)
return x_train, x_test, y_train, y_test
else:
# 标准化数据
# 加载标准化模型
scaler = joblib.load(scaler_path)
X = scaler.transform(X)
return X
def handle_results(self, runfolder, purpose):
done = os.path.isfile(os.path.join(runfolder, 'done'))
if (done and purpose == 'events'):
os.rename(
os.path.join(runfolder, runfolder) + '.hepmc',
os.path.join("../rivet", runfolder + '.hepmc'))
if (done and purpose == 'test_soft'):
ut.mkdirs("../scan-results")
soft_log = os.path.join(runfolder, 'soft.log')
if os.path.isfile(soft_log):
os.rename(
soft_log,
os.path.join("../scan-results",
runfolder.strip('--1') + '.soft.dat'))
else:
return FAIL
return SUCCESS
def save_checkpoint(self, iteration):
encoderA_path = os.path.join(self.ckpt_dir,
'iter_%s_encoderA.pt' % iteration)
encoderB_path = os.path.join(self.ckpt_dir,
'iter_%s_encoderB.pt' % iteration)
decoderA_path = os.path.join(self.ckpt_dir,
'iter_%s_decoderA.pt' % iteration)
decoderB_path = os.path.join(self.ckpt_dir,
'iter_%s_decoderB.pt' % iteration)
mkdirs(self.ckpt_dir)
torch.save(self.encoderA, encoderA_path)
torch.save(self.encoderB, encoderB_path)
torch.save(self.decoderA, decoderA_path)
torch.save(self.decoderB, decoderB_path)
def __init__(self, query='', lang='', top_tweet=False):
self.query = query
self.url = "https://twitter.com/i/search/timeline?l={}".format(lang)
self.lang = lang
if not top_tweet:
self.url = self.url + "&f=tweets"
self.url = self.url + "&q=%s&src=typed&max_position=%s"
path_here = os.path.abspath(os.path.dirname(__file__))
cache_directory = path_here + '/../../.caches'
mkdirs(cache_directory)
self.cache_filename = '%s/%s%s.cache'%(cache_directory,query,lang)
self.min_position = ''
if os.path.exists(self.cache_filename):
with open(self.cache_filename, 'r') as f:
self.min_position = f.read()
def __init__(self,
excelName,
sheetName,
dropboxDir,
accessToken,
batchSize_GB=1000,
sleepTime_min=10,
batchTimeLimit_hour=200):
""" Creates attribute variables and makes a log file
dropboxApp.log in the logs directory.
Uploads files to Dropbox using the desktop APP in batches.
"""
self.excelName = excelName
self.sheetName = sheetName
self.dropboxDir = dropboxDir
self.accessToken = accessToken
self.batchSize = batchSize_GB * 1024 * 1024 * 1024
self.sleepTime_min = sleepTime_min
self.batchTimeLimit_hour = batchTimeLimit_hour
self.names = ['inputFile', 'outputDir']
self.df = pandas.read_excel(self.excelName,
sheet_name=self.sheetName,
names=self.names)
self.dbx = dropbox.Dropbox(self.accessToken)
physicalDriveName, intervalBetweenProcessUsageCheck_s, minDataIOSpeed_mbps, maximumProcessStopDuration_min = 'PhysicalDrive3', 15, 5, 30
resourceMonitorThread = Thread(target=utils.resourceUsage,
args=[
physicalDriveName,
intervalBetweenProcessUsageCheck_s,
minDataIOSpeed_mbps,
maximumProcessStopDuration_min
],
daemon=True)
resourceMonitorThread.start()
print('Stage 2 - Data upload using APP')
self.logFileName = './logs/upload/' + datetime.datetime.now().strftime(
"%Y%m%d_%H%M%S") + '_DropboxAPP.log'
self.getFileList()
utils.mkdirs(self.dropboxDirList)
self.makeBatches()
self.uploadFiles()
def run( common_args, cmd_argv ):
args = docopt(scm.mount.USAGE, argv=cmd_argv)
# Success Msg
if ( args['get-success-msg'] ):
print( "Repo mounted and committed to your repo" )
return
# Error Msg
if ( args['get-error-msg'] ):
print( "" ) # No message
return
# Check if there are pending repo changes
cmd = f'git diff-index HEAD --exit-code --quiet'
t = utils.run_shell( cmd, False )
cmd = f'git diff-index --cached HEAD --exit-code --quiet'
t2 = utils.run_shell( cmd, False )
utils.check_results( t, "ERROR: Your local repo has pending tree modification (i.e. need to do a commit/revert)." )
utils.check_results( t2, "ERROR: Your local repo has pending index modification (i.e. need to do a commit/revert)." )
# -b option is not supported/needed
if ( args['-b'] != None ):
sys.exit( "The '-b' option is not supported/needed. Use a 'remote-ref' as the <id> argument" )
# Default Package name
pkg = args['<repo>']
if ( args['-p'] ):
pkg = args['-p']
# Make sure the Parent destination directory exists
dst = args['<dst>']
utils.mkdirs( dst )
# Set directory for the subtree directory
dst = os.path.join( dst, pkg )
dst = utils.force_unix_dir_sep(dst)
utils.print_verbose( f"Destination for the copy: {dst}" )
# Create a 'subtree'
cmd = f'git subtree add --prefix {dst} {args["<origin>"]}/_git/{args["<repo>"]} {args["<id>"]} --squash'
t = utils.run_shell( cmd, common_args['-v'] )
if ( utils.is_error(t) ): # Clean-up dst dir if there was failure
utils.remove_tree( dst )
utils.check_results( t, "ERROR: Failed to create a subtree for the specified package/repository." )
def get_data(config, data_path: str, train: bool) -> Union[Tuple[np.ndarray], np.ndarray]:
"""
提取所有音频的特征: 遍历所有文件夹, 读取每个文件夹中的音频, 提取每个音频的特征，把所有特征
保存在 "{config.feature_folder}/*.p" 文件中。
Args:
config: 配置项
data_path (str): 数据集文件夹/测试文件路径
train (bool): 是否为训练数据
Returns:
- train = True: 训练特征、测试特征和对应的标签
- train = False: 预测特征
"""
if train == True:
files = get_data_path(data_path, config.class_labels)
max_, min_ = get_max_min(files)
mfcc_data = []
for file in files:
label = re.findall(".*-(.*)-.*", file)[0]
# 三分类
# if(label == "sad" or label == "neutral"):
# label = "neutral"
# elif(label == "angry" or label == "fear"):
# label = "negative"
# elif(label == "happy" or label == "surprise"):
# label = "positive"
features = extract_features(file, max_)
mfcc_data.append([file, features, config.class_labels.index(label)])
else:
features = extract_features(data_path)
mfcc_data = [[data_path, features, -1]]
# 如果 config.feature_folder 文件夹不存在，则新建一个
utils.mkdirs(config.feature_folder)
# 特征存储路径
feature_path = os.path.join(config.feature_folder, "train.p" if train == True else "predict.p")
# 保存特征
pickle.dump(mfcc_data, open(feature_path, 'wb'))
return load_feature(config, train=train)
def run(common_args, cmd_argv):
args = docopt(scm.copy.USAGE, argv=cmd_argv)
# Use the mount command so as to have consistent pre/post GIT behavior with adopting non-integrated packages
if (not args['--force']):
cmd_argv[0] = 'mount'
cmd_argv.insert(1, '--noro')
scm.git.mount.run(common_args, cmd_argv)
# Do a brute force copy
else:
# -b option is not supported/needed
if (args['-b'] != None):
sys.exit(
"The '-b' option is not supported/needed. Use a 'remote-ref' as the <id> argument"
)
# Default Package name
pkg = args['<repo>']
if (args['-p']):
pkg = args['-p']
# Make sure the destination directory exists
dst = os.path.join(os.getcwd(), args['<dst>'])
utils.print_verbose(f"Destination for the copy: {dst}")
utils.mkdirs(dst)
# Create a clone of the repo
# NOTE: I hate cloning the entire repo - but I have not found a way to get JUST a snapshot by a remote-ref
cmd = f'git clone --branch {args["<id>"]} --depth=1 {args["<origin>"]}/_git/{args["<repo>"]} {pkg}'
utils.push_dir(dst)
t = utils.run_shell(cmd, common_args['-v'])
utils.pop_dir()
if (utils.is_error(t)): # Clean-up dst dir if there was failure
utils.remove_tree(dst)
utils.check_results(
t,
f"ERROR: Failed the retreive/clone the specified package/repository. Note: the <id> ({args['<id>']}) MUST be a git TAG."
)
# Remove the .git directoy since this is a non-tracked copy
gitdir = os.path.join(dst, pkg, ".git")
utils.remove_tree(
gitdir,
warn_msg="Not able to remove the .git directory for local copy")
def __new__(cls, name, type_name, when='M', interval=1440, backupCount=10):
logger_name = '%s_%s' % (name, type_name)
logger = cls.__loggers.get(logger_name, None)
if not logger:
logger = logging.getLogger(name)
the_game_log_dir = os.path.join(LOGS_DIR, name)
mkdirs(the_game_log_dir)
the_game_log_file_name = os.path.join(
the_game_log_dir, '%s.log' % type_name)
fileTimeHandler = MultiProcessTimedRotatingFileHandler(
the_game_log_file_name, when, interval, backupCount)
formatter = logging.Formatter(
'%(asctime)s pid:%(process)d %(name)s:%(lineno)d %(levelname)s %(message)s',
datefmt='[%Y-%m-%d %H:%M:%S %z]')
fileTimeHandler.setFormatter(formatter)
logger.addHandler(fileTimeHandler)
cls.__loggers[logger_name] = logger
return logger
def save_synth(self, iters, howmany=100):
decoder = self.decoder
Z = torch.randn(howmany, self.z_dim)
if self.use_cuda:
Z = Z.cuda()
# do synthesis
X = torch.sigmoid(decoder(Z)).data.cpu()
# save the results as image
fname = os.path.join(self.output_dir_synth, 'synth_%s.jpg' % iters)
mkdirs(self.output_dir_synth)
save_image(tensor=X,
filename=fname,
nrow=int(np.sqrt(howmany)),
pad_value=1)
def print_options(opt):
message = ''
message += '----------------- Options ---------------\n'
for k, v in sorted(vars(opt).items()):
comment = ''
default = parser.get_default(k)
if v != default:
comment = '\t[default: %s]' % str(default)
message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
message += '----------------- End -------------------'
print(message)
# save to the disk
expr_dir = opt.save_dir / opt.name
mkdirs(expr_dir)
file_name = expr_dir / 'opt.txt'
with open(file_name, 'wt') as opt_file:
opt_file.write(message)
opt_file.write('\n')
def __new__(cls, name, type_name, when='M', interval=1440, backupCount=10):
logger_name = '%s_%s' % (name, type_name)
logger = cls.__loggers.get(logger_name, None)
if not logger:
logger = logging.getLogger(name)
the_game_log_dir = os.path.join(LOGS_DIR, name)
mkdirs(the_game_log_dir)
the_game_log_file_name = os.path.join(the_game_log_dir,
'%s.log' % type_name)
fileTimeHandler = MultiProcessTimedRotatingFileHandler(
the_game_log_file_name, when, interval, backupCount)
formatter = logging.Formatter(
'%(asctime)s pid:%(process)d %(name)s:%(lineno)d %(levelname)s %(message)s',
datefmt='[%Y-%m-%d %H:%M:%S %z]')
fileTimeHandler.setFormatter(formatter)
logger.addHandler(fileTimeHandler)
cls.__loggers[logger_name] = logger
return logger
def save_predicted_data(df, save_dir):
predicted_data = []
for index, row in df.iterrows():
predicted_data.append({
"id": row["id"],
"label": row["LabelID_Pred"],
"content": row["content"]
})
# Save data to file
utils.mkdirs(save_dir)
save_path = os.path.join(
save_dir,
"predicted_data_{}_{}.txt".format(df.shape[0],
utils.get_format_time_now()))
with open(save_path, 'w') as f:
json.dump(predicted_data, f)
print("Save predicted data to {} done".format(save_path))
def _install(self,aid,retrieve,msg=None,archive=True):
d=self.path(aid)
if is_existing_directory(d):
return d
a=retrieve(aid)
if a==None: return None
utils.mkdirs(os.path.dirname(d))
f=tempfile.mkdtemp(suffix='.'+self._base(aid), dir=self._root)
try:
self._notify(aid,d,a,msg)
if not archive:
base=os.path.basename(a)
ix=base.rfind('.')
suf=base[ix:] if ix>=0 else ''
if suf=='.gz':
base=base[:ix]
ix=base.rfind('.')
suf=(base[ix:] if ix>=0 else '')+suf
shutil.copy(a,join(f,'artifact'+suf))
else:
utils.expandArchive(a, f)
if self._finalizer!=None:
self._finalizer(aid,f)
err=0
while not is_existing_directory(d):
try:
os.rename(f,d)
except OSError:
if not is_existing_directory(d):
err=err+1
else:
log.error( 'cannot rename directory '+f+':'+str(sys.exc_info()),log.INFRA)
break
utils.rmtree(f)
if is_existing_directory(d):
return d
log.error( 'no folder '+d,log.INFRA)
return None
except:
log.error( 'cannot expand archive '+a+':'+ str(sys.exc_info()),log.INFRA)
utils.rmtree(f)
raise XmakeException('ERR: cannot expand archive '+a+': '+str(sys.exc_info()))
def __init__(self, topdir, datadir, db='sqlite3', seaf_server_bin='seaf-server', ccnet_server_bin='ccnet-server'):
self.db = db
self.datadir = datadir
self.central_conf_dir = join(datadir, 'conf')
self.seafile_conf_dir = join(datadir, 'seafile-data')
self.ccnet_conf_dir = join(datadir, 'ccnet')
self.log_dir = join(datadir, 'logs')
mkdirs(self.log_dir)
self.ccnet_log = join(self.log_dir, 'ccnet.log')
self.seafile_log = join(self.log_dir, 'seafile.log')
self.ccnet_server_bin = ccnet_server_bin
self.seaf_server_bin = seaf_server_bin
self.sql_dir = join(topdir, 'seafile-server', 'scripts', 'sql')
self.ccnet_proc = None
self.seafile_proc = None
def save_checkpoint(self, iteration):
encoder_path = os.path.join(self.ckpt_dir,
'iter_%s_encoder.pt' % iteration)
decoder_path = os.path.join(self.ckpt_dir,
'iter_%s_decoder.pt' % iteration)
prior_alpha_path = os.path.join(self.ckpt_dir,
'iter_%s_prior_alpha.pt' % iteration)
post_alpha_path = os.path.join(self.ckpt_dir,
'iter_%s_post_alpha.pt' % iteration)
D_path = os.path.join(self.ckpt_dir, 'iter_%s_D.pt' % iteration)
mkdirs(self.ckpt_dir)
torch.save(self.encoder, encoder_path)
torch.save(self.decoder, decoder_path)
torch.save(self.prior_alpha, prior_alpha_path)
torch.save(self.post_alpha, post_alpha_path)
torch.save(self.D, D_path)
def __init__(self, db_dir):
mkdirs(db_dir)
self.path = os.path.join(db_dir, 'logdata.sqlite')
con = sqlite3.connect(self.path)
con.row_factory = sqlite3.Row
self.cursor = con.cursor()
create_script = relative_file(__file__, 'create.sql')
with open(create_script) as script:
self.cursor.executescript(script.read())
for host_info in [
HostInfo(shortname='bb', name='bitbucket',
urnpattern='https://bitbucket.org', vcs='hg',
lister_module='repoblick.lister.bitbucket'),
HostInfo(shortname='gh', name='github', urnpattern='https://github.com/%s.git', vcs='git'),
HostInfo(shortname='gc-hg', name='googlecode-mercurial', urnpattern='https://%s.googlecode.com/hg/', vcs='hg'),
HostInfo(shortname='gc-svn', name='googlecode-subversion', urnpattern='http://%s.googlecode.com/svn/trunk/', vcs='svn'),
]:
self.add_host(host_info)
self.commit()
def plot_multi_functions(functions, output_path="./Ex5_Output/plot.jpg"):
for func_name, (x, y) in functions.items():
print("Plot Func name : {}".format(func_name))
plt.plot(x, y, label=func_name)
plt.legend()
plt.title("Plot multi functions")
plt.xlabel("x")
plt.ylabel("y")
# Save figure to output path
dir_path = output_path[:output_path.rfind("/")]
utils.mkdirs(dir_path)
output_path = os.path.abspath(output_path)
print("Save file to {} done".format(output_path))
plt.savefig(output_path, dpi=200)
plt.show()
def load_feature(config, train: bool) -> Union[Tuple[np.ndarray], np.ndarray]:
"""
从 "{config.feature_folder}/*.p" 文件中加载特征数据
Args:
config: 配置项
train (bool): 是否为训练数据
Returns:
- X (Tuple[np.ndarray]): 训练特征、测试特征和对应的标签
- X (np.ndarray): 预测特征
"""
feature_path = os.path.join(config.feature_folder, "train.p" if train == True else "predict.p")
features = pd.DataFrame(
data = joblib.load(feature_path),
columns = ['file_name', 'features', 'emotion']
)
X = list(features['features'])
Y = list(features['emotion'])
# 标准化模型路径
scaler_path = os.path.join(config.checkpoint_path, 'SCALER_LIBROSA.m')
if train == True:
# 标准化数据
scaler = StandardScaler().fit(X)
# 保存标准化模型
utils.mkdirs(config.checkpoint_path)
joblib.dump(scaler, scaler_path)
X = scaler.transform(X)
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
return x_train, x_test, y_train, y_test
else:
# 标准化数据
# 加载标准化模型
scaler = joblib.load(scaler_path)
X = scaler.transform(X)
return X
def compareWithGroundtruth(sal,datasetPath,imageName):
print "Processing "+imageName +" ....";
confusion = getNewConfusion();
srcImgPath = os.path.join(datasetPath,imageName,"src_color",imageName+".png")
image = cv2.imread(srcImgPath)
mask = getSalienyMap(sal,image)
#write results
mask_image = image*mask[:,:,None];
mkdirs(os.path.join(datasetPath,imageName,"results"));
file_name = os.path.join(datasetPath,imageName,"results",imageName+"_"+sal.method+".png")
cv2.imwrite(file_name,mask_image)
humanSegPath = os.path.join(datasetPath,imageName,"human_seg");
for bgImg in os.listdir(humanSegPath):
if bgImg.endswith(".png"):
bgMask = getMask(cv2.imread(os.path.join(humanSegPath,bgImg)))
updateConfusion(confusion,comparator(bgMask,mask))
print "Confusion : ",confusion;
return confusion;
def save_recon(self, iters, true_images, recon_images):
# make a merge of true and recon, eg,
# merged[0,...] = true[0,...],
# merged[1,...] = recon[0,...],
# merged[2,...] = true[1,...],
# merged[3,...] = recon[1,...], ...
n = true_images.shape[0]
perm = torch.arange(0, 2 * n).view(2, n).transpose(1, 0)
perm = perm.contiguous().view(-1)
merged = torch.cat([true_images, recon_images], dim=0)
merged = merged[perm, :].cpu()
# save the results as image
fname = os.path.join(self.output_dir_recon, 'recon_%s.jpg' % iters)
mkdirs(self.output_dir_recon)
save_image(tensor=merged,
filename=fname,
nrow=2 * int(np.sqrt(n)),
pad_value=1)
def run(self):
if DownloadHistoryMatch.START_DATE=="":
self.backup_data()
create_history_db()
mkdirs("cache_web_pages/html")
send_msg("分析比赛..")
urls = self.last_days()
for url in urls:
#write_file("cache_history_data_startdate.log",day_format)
self.targetUrl = url#"http://live.500.com/wanchang.php?e=" + day_format
send_msg("获取赛事链接 " + self.targetUrl)
matchs = self.extract_matchs(self.targetUrl)
self.batch_download_data_pages(matchs)
for match in matchs:
msg = "分析赔率 %s" % match["odds_url"]
send_msg(msg)
self.parse_odds(match, get_cache_web_file_name(match["odds_url"]))
msg = "分析基本面 %s" % match["base_face_url"]
send_msg(msg)
self.parse_baseface(match, get_cache_web_file_name(match["base_face_url"]))
self.dataset = matchs
send_msg("cache_history_data");
send_msg("completed")
send_msg("完成.")
def run_TPI(income_tax_params, tpi_params, iterative_params, initial_values, SS_values, output_dir="./OUTPUT"):
# unpack tuples of parameters
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_params
maxiter, mindist_SS, mindist_TPI = iterative_params
J, S, T, BQ_dist, BW, beta, sigma, alpha, Z, delta, ltilde, nu, g_y,\
g_n_vector, tau_payroll, tau_bq, rho, omega, N_tilde, lambdas, e, retire, mean_income_data,\
factor, h_wealth, p_wealth, m_wealth, b_ellipse, upsilon, chi_b, chi_n = tpi_params
K0, b_sinit, b_splus1init, L0, Y0,\
w0, r0, BQ0, T_H_0, factor, tax0, c0, initial_b, initial_n = initial_values
Kss, Lss, rss, wss, BQss, T_Hss, bssmat_splus1, nssmat = SS_values
TPI_FIG_DIR = output_dir
# Initialize guesses at time paths
domain = np.linspace(0, T, T)
K_init = (-1 / (domain + 1)) * (Kss - K0) + Kss
K_init[-1] = Kss
K_init = np.array(list(K_init) + list(np.ones(S) * Kss))
L_init = np.ones(T + S) * Lss
K = K_init
L = L_init
Y_params = (alpha, Z)
Y = firm.get_Y(K, L, Y_params)
w = firm.get_w(Y, L, alpha)
r_params = (alpha, delta)
r = firm.get_r(Y, K, r_params)
BQ = np.zeros((T + S, J))
for j in xrange(J):
BQ[:, j] = list(np.linspace(BQ0[j], BQss[j], T)) + [BQss[j]] * S
BQ = np.array(BQ)
if T_Hss < 1e-13 and T_Hss > 0.0 :
T_Hss2 = 0.0 # sometimes SS is very small but not zero, even if taxes are zero, this get's rid of the approximation error, which affects the perc changes below
else:
T_Hss2 = T_Hss
T_H = np.ones(T + S) * T_Hss2
# Make array of initial guesses for labor supply and savings
domain2 = np.tile(domain.reshape(T, 1, 1), (1, S, J))
ending_b = bssmat_splus1
guesses_b = (-1 / (domain2 + 1)) * (ending_b - initial_b) + ending_b
ending_b_tail = np.tile(ending_b.reshape(1, S, J), (S, 1, 1))
guesses_b = np.append(guesses_b, ending_b_tail, axis=0)
domain3 = np.tile(np.linspace(0, 1, T).reshape(T, 1, 1), (1, S, J))
guesses_n = domain3 * (nssmat - initial_n) + initial_n
ending_n_tail = np.tile(nssmat.reshape(1, S, J), (S, 1, 1))
guesses_n = np.append(guesses_n, ending_n_tail, axis=0)
b_mat = np.zeros((T + S, S, J))
n_mat = np.zeros((T + S, S, J))
ind = np.arange(S)
TPIiter = 0
TPIdist = 10
PLOT_TPI = False
euler_errors = np.zeros((T, 2 * S, J))
TPIdist_vec = np.zeros(maxiter)
while (TPIiter < maxiter) and (TPIdist >= mindist_TPI):
# Plot TPI for K for each iteration, so we can see if there is a
# problem
if PLOT_TPI is True:
K_plot = list(K) + list(np.ones(10) * Kss)
L_plot = list(L) + list(np.ones(10) * Lss)
plt.figure()
plt.axhline(
y=Kss, color='black', linewidth=2, label=r"Steady State $\hat{K}$", ls='--')
plt.plot(np.arange(
T + 10), Kpath_plot[:T + 10], 'b', linewidth=2, label=r"TPI time path $\hat{K}_t$")
plt.savefig(os.path.join(TPI_FIG_DIR, "TPI_K"))
# Uncomment the following print statements to make sure all euler equations are converging.
# If they don't, then you'll have negative consumption or consumption spikes. If they don't,
# it is the initial guesses. You might need to scale them differently. It is rather delicate for the first
# few periods and high ability groups.
# theta_params = (e[-1, j], 1, omega[0].reshape(S, 1), lambdas[j])
# theta = tax.replacement_rate_vals(n, w, factor, theta_params)
theta = np.zeros((J,))
guesses = (guesses_b, guesses_n)
outer_loop_vars = (r, w, K, BQ, T_H)
inner_loop_params = (income_tax_params, tpi_params, initial_values, theta, ind)
# Solve HH problem in inner loop
euler_errors, b_mat, n_mat = inner_loop(guesses, outer_loop_vars, inner_loop_params)
# if euler_errors.max() > 1e-6:
# print 't-loop:', euler_errors.max()
# Force the initial distribution of capital to be as given above.
b_mat[0, :, :] = initial_b
K_params = (omega[:T].reshape(T, S, 1), lambdas.reshape(1, 1, J), g_n_vector[:T], 'TPI')
K[:T] = household.get_K(b_mat[:T], K_params)
L_params = (e.reshape(1, S, J), omega[:T, :].reshape(T, S, 1), lambdas.reshape(1, 1, J), 'TPI')
L[:T] = firm.get_L(n_mat[:T], L_params)
Y_params = (alpha, Z)
Ynew = firm.get_Y(K[:T], L[:T], Y_params)
wnew = firm.get_w(Ynew[:T], L[:T], alpha)
r_params = (alpha, delta)
rnew = firm.get_r(Ynew[:T], K[:T], r_params)
BQ_params = (omega[:T].reshape(T, S, 1), lambdas.reshape(1, 1, J), rho.reshape(1, S, 1),
g_n_vector[:T].reshape(T, 1), 'TPI')
BQnew = household.get_BQ(rnew[:T].reshape(T, 1), b_mat[:T,:,:], BQ_params)
bmat_s = np.zeros((T, S, J))
bmat_s[:, 1:, :] = b_mat[:T, :-1, :]
bmat_splus1 = np.zeros((T, S, J))
bmat_splus1[:, :, :] = b_mat[1:T + 1, :, :]
TH_tax_params = np.zeros((T,S,J,etr_params.shape[2]))
for i in range(etr_params.shape[2]):
TH_tax_params[:,:,:,i] = np.tile(np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))
T_H_params = (np.tile(e.reshape(1, S, J),(T,1,1)), BQ_dist, lambdas.reshape(1, 1, J), omega[:T].reshape(T, S, 1), 'TPI',
TH_tax_params, theta, tau_bq, tau_payroll, h_wealth, p_wealth, m_wealth, retire, T, S, J)
T_H_new = np.array(list(tax.get_lump_sum(np.tile(rnew[:T].reshape(T, 1, 1),(1,S,J)), np.tile(wnew[:T].reshape(T, 1, 1),(1,S,J)),
bmat_s, n_mat[:T,:,:], BQnew[:T].reshape(T, 1, J), factor, T_H_params)) + [T_Hss] * S)
w[:T] = utils.convex_combo(wnew[:T], w[:T], nu)
r[:T] = utils.convex_combo(rnew[:T], r[:T], nu)
BQ[:T] = utils.convex_combo(BQnew[:T], BQ[:T], nu)
T_H[:T] = utils.convex_combo(T_H_new[:T], T_H[:T], nu)
guesses_b = utils.convex_combo(b_mat, guesses_b, nu)
guesses_n = utils.convex_combo(n_mat, guesses_n, nu)
if T_H.all() != 0:
TPIdist = np.array(list(utils.pct_diff_func(rnew[:T], r[:T])) + list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten()) + list(
utils.pct_diff_func(wnew[:T], w[:T])) + list(utils.pct_diff_func(T_H_new[:T], T_H[:T]))).max()
else:
TPIdist = np.array(list(utils.pct_diff_func(rnew[:T], r[:T])) + list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten()) + list(
utils.pct_diff_func(wnew[:T], w[:T])) + list(np.abs(T_H_new[:T], T_H[:T]))).max()
TPIdist_vec[TPIiter] = TPIdist
# After T=10, if cycling occurs, drop the value of nu
# wait til after T=10 or so, because sometimes there is a jump up
# in the first couple iterations
# if TPIiter > 10:
# if TPIdist_vec[TPIiter] - TPIdist_vec[TPIiter - 1] > 0:
# nu /= 2
# print 'New Value of nu:', nu
TPIiter += 1
print '\tIteration:', TPIiter
print '\t\tDistance:', TPIdist
if ((TPIiter >= maxiter) or (np.absolute(TPIdist) > mindist_TPI)) and ENFORCE_SOLUTION_CHECKS :
raise RuntimeError("Transition path equlibrium not found")
Y[:T] = Ynew
# Solve HH problem in inner loop
guesses = (guesses_b, guesses_n)
outer_loop_vars = (r, w, K, BQ, T_H)
inner_loop_params = (income_tax_params, tpi_params, initial_values, theta, ind)
euler_errors, b_mat, n_mat = inner_loop(guesses, outer_loop_vars, inner_loop_params)
b_mat[0, :, :] = initial_b
K_params = (omega[:T].reshape(T, S, 1), lambdas.reshape(1, 1, J), g_n_vector[:T], 'TPI')
K[:T] = household.get_K(b_mat[:T], K_params) # this is what old code does, but it's strange - why use
# b_mat -- what is going on with initial period, etc.
etr_params_path = np.zeros((T,S,J,etr_params.shape[2]))
for i in range(etr_params.shape[2]):
etr_params_path[:,:,:,i] = np.tile(np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))
tax_path_params = (np.tile(e.reshape(1, S, J),(T,1,1)), BQ_dist, lambdas, 'TPI', retire, etr_params_path, h_wealth,
p_wealth, m_wealth, tau_payroll, theta, tau_bq, J, S)
tax_path = tax.total_taxes(np.tile(r[:T].reshape(T, 1, 1),(1,S,J)), np.tile(w[:T].reshape(T, 1, 1),(1,S,J)), bmat_s,
n_mat[:T,:,:], BQ[:T, :].reshape(T, 1, J), factor, T_H[:T].reshape(T, 1, 1), None, False, tax_path_params)
cons_params = (e.reshape(1, S, J), BQ_dist, lambdas.reshape(1, 1, J), g_y)
c_path = household.get_cons(omega[:T].reshape(T,S,1), r[:T].reshape(T, 1, 1), w[:T].reshape(T, 1, 1), bmat_s, bmat_splus1, n_mat[:T,:,:],
BQ[:T].reshape(T, 1, J), tax_path, cons_params)
C_params = (omega[:T].reshape(T, S, 1), lambdas, 'TPI')
C = household.get_C(c_path, C_params)
I_params = (delta, g_y, g_n_vector[:T])
I = firm.get_I(K[1:T+1], K[:T], I_params)
print 'Resource Constraint Difference:', Y[:T] - C[:T] - I[:T]
print'Checking time path for violations of constaints.'
for t in xrange(T):
household.constraint_checker_TPI(
b_mat[t], n_mat[t], c_path[t], t, ltilde)
eul_savings = euler_errors[:, :S, :].max(1).max(1)
eul_laborleisure = euler_errors[:, S:, :].max(1).max(1)
print 'Max Euler error, savings: ', eul_savings
print 'Max Euler error labor supply: ', eul_laborleisure
if ((np.any(np.absolute(eul_savings) >= mindist_TPI) or
(np.any(np.absolute(eul_laborleisure) > mindist_TPI)))
and ENFORCE_SOLUTION_CHECKS):
raise RuntimeError("Transition path equlibrium not found")
'''
------------------------------------------------------------------------
Save variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
output = {'Y': Y, 'K': K, 'L': L, 'C': C, 'I': I, 'BQ': BQ,
'T_H': T_H, 'r': r, 'w': w, 'b_mat': b_mat, 'n_mat': n_mat,
'c_path': c_path, 'tax_path': tax_path,
'eul_savings': eul_savings, 'eul_laborleisure': eul_laborleisure}
tpi_dir = os.path.join(output_dir, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_vars.pkl")
pickle.dump(output, open(tpi_vars, "wb"))
macro_output = {'Y': Y, 'K': K, 'L': L, 'C': C, 'I': I,
'BQ': BQ, 'T_H': T_H, 'r': r, 'w': w,
'tax_path': tax_path}
# Non-stationary output
# macro_ns_output = {'K_ns_path': K_ns_path, 'C_ns_path': C_ns_path, 'I_ns_path': I_ns_path,
# 'L_ns_path': L_ns_path, 'BQ_ns_path': BQ_ns_path,
# 'rinit': rinit, 'Y_ns_path': Y_ns_path, 'T_H_ns_path': T_H_ns_path,
# 'w_ns_path': w_ns_path}
return output, macro_output
def TP_solutions(winit, rinit, T_H_init, BQinit2, Kss, Lss, Yss, BQss, theta, income_tax_params, wealth_tax_params, ellipse_params, parameters, g_n_vector,
omega_stationary, K0, b_sinit, b_splus1init, L0, Y0, r0, BQ0,
T_H_0, tax0, c0, initial_b, initial_n, factor_ss, tau_bq, chi_b,
chi_n, output_dir="./OUTPUT", **kwargs):
'''
This function returns the solutions for all variables along the time path.
'''
J, S, T, BW, beta, sigma, alpha, Z, delta, ltilde, nu, g_y, g_n_ss, tau_payroll, retire, mean_income_data, \
h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = parameters
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_params
print 'Computing final solutions'
# Extend time paths past T
winit = np.array(list(winit) + list(np.ones(S) * wss))
rinit = np.array(list(rinit) + list(np.ones(S) * rss))
T_H_init = np.array(list(T_H_init) + list(np.ones(S) * T_Hss))
BQinit = np.zeros((T + S, J))
for j in xrange(J):
BQinit[:, j] = list(BQinit2[:,j]) + [BQss[j]] * S
BQinit = np.array(BQinit)
# Make array of initial guesses
domain = np.linspace(0, T, T)
domain2 = np.tile(domain.reshape(T, 1, 1), (1, S, J))
ending_b = bssmat_splus1
guesses_b = (-1 / (domain2 + 1)) * (ending_b - initial_b) + ending_b
ending_b_tail = np.tile(ending_b.reshape(1, S, J), (S, 1, 1))
guesses_b = np.append(guesses_b, ending_b_tail, axis=0)
domain3 = np.tile(np.linspace(0, 1, T).reshape(T, 1, 1), (1, S, J))
guesses_n = domain3 * (nssmat - initial_n) + initial_n
ending_n_tail = np.tile(nssmat.reshape(1, S, J), (S, 1, 1))
guesses_n = np.append(guesses_n, ending_n_tail, axis=0)
b_mat = np.zeros((T + S, S, J))
n_mat = np.zeros((T + S, S, J))
ind = np.arange(S)
# initialize array of Euler errors
euler_errors = np.zeros((T, 2 * S, J))
# As in SS, you need the final distributions of b and n to match the final
# w, r, BQ, etc. Otherwise the euler errors are large. You need one more
# fsolve.
for j in xrange(J):
b_mat[1, -1, j], n_mat[0, -1, j] = np.array(opt.fsolve(SS_TPI_firstdoughnutring, [guesses_b[1, -1, j], guesses_n[0, -1, j]],
args=(winit[1], rinit[1], BQinit[1, j], T_H_init[1], initial_b, factor_ss,
j, income_tax_params, parameters, theta, tau_bq), xtol=1e-13))
for s in xrange(S - 2): # Upper triangle
ind2 = np.arange(s + 2)
b_guesses_to_use = np.diag(guesses_b[1:S + 1, :, j], S - (s + 2))
n_guesses_to_use = np.diag(guesses_n[:S, :, j], S - (s + 2))
# initialize array of diagonal elements
length_diag = (np.diag(np.transpose(etr_params[:S,:,0]),S-(s+2))).shape[0]
etr_params_to_use = np.zeros((length_diag,etr_params.shape[2]))
mtrx_params_to_use = np.zeros((length_diag,mtrx_params.shape[2]))
mtry_params_to_use = np.zeros((length_diag,mtry_params.shape[2]))
for i in range(etr_params.shape[2]):
etr_params_to_use[:,i] = np.diag(np.transpose(etr_params[:S,:,i]),S-(s+2))
mtrx_params_to_use[:,i] = np.diag(np.transpose(mtrx_params[:S,:,i]),S-(s+2))
mtry_params_to_use[:,i] = np.diag(np.transpose(mtry_params[:S,:,i]),S-(s+2))
inc_tax_params_upper = (analytical_mtrs, etr_params_to_use, mtrx_params_to_use, mtry_params_to_use)
solutions = opt.fsolve(Steady_state_TPI_solver, list(
b_guesses_to_use) + list(n_guesses_to_use), args=(
winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, s, 0, inc_tax_params_upper, parameters, theta, tau_bq, rho, lambdas, e, initial_b, chi_b, chi_n), xtol=1e-13)
b_vec = solutions[:len(solutions) / 2]
b_mat[1 + ind2, S - (s + 2) + ind2, j] = b_vec
n_vec = solutions[len(solutions) / 2:]
n_mat[ind2, S - (s + 2) + ind2, j] = n_vec
for t in xrange(0, T):
b_guesses_to_use = .75 * np.diag(guesses_b[t + 1:t + S + 1, :, j])
n_guesses_to_use = np.diag(guesses_n[t:t + S, :, j])
# initialize array of diagonal elements
length_diag = (np.diag(np.transpose(etr_params[:,t:t+S,i]))).shape[0]
etr_params_to_use = np.zeros((length_diag,etr_params.shape[2]))
mtrx_params_to_use = np.zeros((length_diag,mtrx_params.shape[2]))
mtry_params_to_use = np.zeros((length_diag,mtry_params.shape[2]))
for i in range(etr_params.shape[2]):
etr_params_to_use[:,i] = np.diag(np.transpose(etr_params[:,t:t+S,i]))
mtrx_params_to_use[:,i] = np.diag(np.transpose(mtrx_params[:,t:t+S,i]))
mtry_params_to_use[:,i] = np.diag(np.transpose(mtry_params[:,t:t+S,i]))
inc_tax_params_TP = (analytical_mtrs, etr_params_to_use, mtrx_params_to_use, mtry_params_to_use)
solutions = opt.fsolve(Steady_state_TPI_solver, list(
b_guesses_to_use) + list(n_guesses_to_use), args=(
winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, None, t, inc_tax_params_TP, parameters, theta, tau_bq, rho, lambdas, e, None, chi_b, chi_n), xtol=1e-13)
b_vec = solutions[:S]
b_mat[t + 1 + ind, ind, j] = b_vec
n_vec = solutions[S:]
n_mat[t + ind, ind, j] = n_vec
inputs = list(solutions)
euler_errors[t, :, j] = np.abs(Steady_state_TPI_solver(
inputs, winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, None, t, inc_tax_params_TP, parameters, theta, tau_bq, rho, lambdas, e, None, chi_b, chi_n))
b_mat[0, :, :] = initial_b
'''
------------------------------------------------------------------------
Generate variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
Kinit = household.get_K(b_mat[:T], omega_stationary[:T].reshape(
T, S, 1), lambdas.reshape(1, 1, J), g_n_vector[:T], 'TPI')
Linit = firm.get_L(e.reshape(1, S, J), n_mat[:T], omega_stationary[
:T, :].reshape(T, S, 1), lambdas.reshape(1, 1, J), 'TPI')
Kpath_TPI = np.array(list(Kinit) + list(np.ones(10) * Kss))
Lpath_TPI = np.array(list(Linit) + list(np.ones(10) * Lss))
BQpath_TPI = np.array(list(BQinit) + list(np.ones((10, J)) * BQss))
b_s = np.zeros((T, S, J))
b_s[:, 1:, :] = b_mat[:T, :-1, :]
b_splus1 = np.zeros((T, S, J))
b_splus1[:, :, :] = b_mat[1:T + 1, :, :]
# initialize array
etr_params_path = np.zeros((T,S,J,etr_params.shape[2]))
for i in range(etr_params.shape[2]):
etr_params_path[:,:,:,i] = np.tile(np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))
tax_path_params = J, S, retire, etr_params_path, h_wealth, p_wealth, m_wealth, tau_payroll
tax_path = tax.total_taxes(np.tile(rinit[:T].reshape(T, 1, 1),(1,S,J)), b_s, np.tile(winit[:T].reshape(T, 1, 1),(1,S,J)),
np.tile(e.reshape(1, S, J),(T,1,1)), n_mat[:T,:,:], BQinit[:T, :].reshape(T, 1, J), lambdas,
factor_ss, T_H_init[:T].reshape(T, 1, 1), None, 'TPI', False, tax_path_params, theta, tau_bq)
c_path = household.get_cons(rinit[:T].reshape(T, 1, 1), b_s, winit[:T].reshape(T, 1, 1), e.reshape(
1, S, J), n_mat[:T], BQinit[:T].reshape(T, 1, J), lambdas.reshape(1, 1, J), b_splus1, parameters, tax_path)
Y_path = firm.get_Y(Kpath_TPI[:T], Lpath_TPI[:T], parameters)
C_path = household.get_C(c_path, omega_stationary[
:T].reshape(T, S, 1), lambdas, 'TPI')
I_path = firm.get_I(Kpath_TPI[1:T + 1],
Kpath_TPI[:T], delta, g_y, g_n_vector[:T])
print 'Resource Constraint Difference:', Y_path - C_path - I_path
print'Checking time path for violations of constaints.'
hh_constraint_params = ltilde
for t in xrange(T):
household.constraint_checker_TPI(
b_mat[t], n_mat[t], c_path[t], t, hh_constraint_params)
eul_savings = euler_errors[:, :S, :].max(1).max(1)
eul_laborleisure = euler_errors[:, S:, :].max(1).max(1)
print 'Max Euler error, savings: ', eul_savings
print 'Max Euler error labor supply: ', eul_laborleisure
'''
------------------------------------------------------------------------
Create the unstationarized versions of the paths of macro aggregates
------------------------------------------------------------------------
'''
# tvec = np.linspace(0, len(C_path), len(C_path))
# growth_path = np.exp(g_y*tvec)
# pop_path = np.zeros(len(C_path))
# for i in range(0,len(C_path)):
# pop_path[i] = np.exp(g_n_vector[:i].sum()) # note that this normalizes the pop in the initial period to one
# growth_pop_path = growth_path*pop_path
# C_ns_path = C_path * growth_pop_path
# K_ns_path = Kinit * growth_pop_path
# BQ_ns_path = growth_pop_path * BQinit[:T]
# L_ns_path = Linit * pop_path
# T_H_ns_path = T_H_init[:T] * growth_pop_path
# w_ns_path = winit*growth_path
# I_ns_path = I_path * growth_pop_path
# Y_ns_path = Y_path * growth_pop_path
'''
------------------------------------------------------------------------
Save variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
output = {'Kpath_TPI': Kpath_TPI, 'b_mat': b_mat, 'c_path': c_path,
'eul_savings': eul_savings, 'eul_laborleisure': eul_laborleisure,
'Lpath_TPI': Lpath_TPI, 'BQpath_TPI': BQpath_TPI, 'n_mat': n_mat,
'rinit': rinit, 'Y_path': Y_path, 'T_H_init': T_H_init,
'tax_path': tax_path, 'winit': winit}
macro_output = {'Kpath_TPI': Kpath_TPI, 'C_path': C_path, 'I_path': I_path,
'Lpath_TPI': Lpath_TPI, 'BQpath_TPI': BQpath_TPI,
'rinit': rinit, 'Y_path': Y_path, 'T_H_init': T_H_init,
'winit': winit, 'tax_path': tax_path}
# macro_ns_output = {'K_ns_path': K_ns_path, 'C_ns_path': C_ns_path, 'I_ns_path': I_ns_path,
# 'L_ns_path': L_ns_path, 'BQ_ns_path': BQ_ns_path,
# 'rinit': rinit, 'Y_ns_path': Y_ns_path, 'T_H_ns_path': T_H_ns_path,
# 'w_ns_path': w_ns_path}
tpi_dir = os.path.join(output_dir, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_vars.pkl")
pickle.dump(output, open(tpi_vars, "wb"))
tpi_dir = os.path.join(output_dir, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_macro_vars.pkl")
pickle.dump(macro_output, open(tpi_vars, "wb"))
def graph_income(ages, abil_midp, abil_pcts, emat, filesuffix=""):
'''
--------------------------------------------------------------------
This function graphs ability matrix in 3D, 2D, log, and nolog
--------------------------------------------------------------------
INPUTS:
ages = (S,) vector, ages represented in sample
abil_midp = (J,) vector, midpoints of income percentile bins in
each ability group
abil_pcts = (J,) vector, percent of population in each ability bin
emat = (S, J) matrix, lifetime ability paths
filesuffix = string, suffix to be added to plot files
OTHER FUNCTIONS AND FILES CALLED BY THIS FUNCTION:
utils.mkdirs()
OBJECTS CREATED WITHIN FUNCTION:
J = integer >= 1
abil_mesh = (S, J) matrix, meshgrid of abil_midp across the
columns, copied down each row
age_mesh = (S, J) matrix, meshgrid of ages down the rows, copied
across each column
cmap1 = matplotlib colormap for 3D plots
cmap2 = matplotlib colormap for 3D plots
output_dir = string, output directory to which figures are saved
filename = string, filename of figure file
fullpath = string, full path of output_dir and filename for figure
being saved
linestyles = (4,) string vector, line styles for plotting
markers = (6,) string vector, marker types for plotting
this_label = string, label for particular 2D line plot
pct_lb = scalar in [0, 100], lower bound of ability percentile
bin
FILES CREATED AND SAVED BY THIS FUNCTION:
.OUTPUT/ability/ability_2D_lev{filesuffix}.png
.OUTPUT/ability/ability_2D_log{filesuffix}.png
.OUTPUT/ability/ability_3D_lev{filesuffix}.png
.OUTPUT/ability/ability_3D_log{filesuffix}.png
Returns: None
--------------------------------------------------------------------
'''
J = abil_midp.shape[0]
abil_mesh, age_mesh = np.meshgrid(abil_midp, ages)
cmap1 = matplotlib.cm.get_cmap('summer')
cmap2 = matplotlib.cm.get_cmap('winter')
# Make sure that "./OUTPUT/ability" directory is created
output_dir = "./OUTPUT/ability"
utils.mkdirs(output_dir)
if J == 1:
# Plot of 2D, J=1 in levels
plt.figure()
plt.plot(ages, emat)
filename = "ability_2D_lev" + filesuffix
fullpath = os.path.join(output_dir, filename)
plt.savefig(fullpath)
plt.close()
# Plot of 2D, J=1 in logs
plt.figure()
plt.plot(ages, np.log(emat))
filename = "ability_2D_log" + filesuffix
fullpath = os.path.join(output_dir, filename)
plt.savefig(fullpath)
plt.close()
else:
# Plot of 3D, J>1 in levels
fig10 = plt.figure()
ax10 = fig10.gca(projection='3d')
ax10.plot_surface(age_mesh, abil_mesh, emat, rstride=8,
cstride=1, cmap=cmap1)
ax10.set_xlabel(r'age-$s$')
ax10.set_ylabel(r'ability type -$j$')
ax10.set_zlabel(r'ability $e_{j,s}$')
filename = "ability_3D_lev" + filesuffix
fullpath = os.path.join(output_dir, filename)
plt.savefig(fullpath)
plt.close()
# Plot of 3D, J>1 in logs
fig11 = plt.figure()
ax11 = fig11.gca(projection='3d')
ax11.plot_surface(age_mesh, abil_mesh, np.log(emat), rstride=8,
cstride=1, cmap=cmap1)
ax11.set_xlabel(r'age-$s$')
ax11.set_ylabel(r'ability type -$j$')
ax11.set_zlabel(r'log ability $log(e_{j,s})$')
filename = "ability_3D_log" + filesuffix
fullpath = os.path.join(output_dir, filename)
plt.savefig(fullpath)
plt.close()
if J <= 10: # Restricted because of line and marker types
# Plot of 2D lines from 3D version in logs
fig112 = plt.figure()
ax = plt.subplot(111)
linestyles = np.array(["-", "--", "-.", ":",])
markers = np.array(["x", "v", "o", "d", ">", "|"])
pct_lb = 0
for j in range(J):
this_label = (str(int(np.rint(pct_lb))) + " - " +
str(int(np.rint(pct_lb + 100*abil_pcts[j]))) + "%")
pct_lb += 100*abil_pcts[j]
if j <= 3:
ax.plot(ages, np.log(emat[:, j]), label=this_label,
linestyle=linestyles[j], color='black')
elif j > 3:
ax.plot(ages, np.log(emat[:, j]), label=this_label,
marker=markers[j-4], color='black')
ax.axvline(x=80, color='black', linestyle='--')
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
ax.set_xlabel(r'age-$s$')
ax.set_ylabel(r'log ability $log(e_{j,s})$')
filename = "ability_2D_log" + filesuffix
fullpath = os.path.join(output_dir, filename)
plt.savefig(fullpath)
plt.close()
def __init__(self,props):
self.method = None
self.props = props
if self.props.doProfile:
self.PROFILE_PATH = os.environ.get("PROFILE_PATH") + os.sep;
mkdirs(self.PROFILE_PATH);
import utils
utils.mkdirs()
#Let's create all needed directories before anything else!
import requests
import os
import enlist
import time
from bs4 import BeautifulSoup
welcome = \
"""
AMU B.Tech Results Downloader
This Python script downloads B.Tech Results of whole class based on information in attendance Excel file.
First you need to put Excel file in Input/ folder
Then type the name of file when asked (Default : store.xlsx)
This will load the information about students from the Excel file and stores it in students.db for future faster access.
Then you will prompted 3 options:
First option downloads the result of whole class and
stores them as html pages in Store/ folder.
Note : This option should be run at least once to
download all necessary result files for further options
If there are no result files in Store/ folder, then
script will not run properly.
Second option loads CPI and SPI from downloaded
def run_time_path_iteration(Kss, Lss, Yss, BQss, theta, parameters, g_n_vector, omega_stationary, K0, b_sinit, b_splus1init, L0, Y0, r0, BQ0, T_H_0, tax0, c0, initial_b, initial_n, factor_ss, tau_bq, chi_b, chi_n, get_baseline=False, output_dir="./OUTPUT", **kwargs):
TPI_FIG_DIR = output_dir
# Initialize Time paths
domain = np.linspace(0, T, T)
Kinit = (-1 / (domain + 1)) * (Kss - K0) + Kss
Kinit[-1] = Kss
Kinit = np.array(list(Kinit) + list(np.ones(S) * Kss))
Linit = np.ones(T + S) * Lss
Yinit = firm.get_Y(Kinit, Linit, parameters)
winit = firm.get_w(Yinit, Linit, parameters)
rinit = firm.get_r(Yinit, Kinit, parameters)
BQinit = np.zeros((T + S, J))
for j in xrange(J):
BQinit[:, j] = list(np.linspace(BQ0[j], BQss[j], T)) + [BQss[j]] * S
BQinit = np.array(BQinit)
T_H_init = np.ones(T + S) * T_Hss
# Make array of initial guesses
domain2 = np.tile(domain.reshape(T, 1, 1), (1, S, J))
ending_b = bssmat_splus1
guesses_b = (-1 / (domain2 + 1)) * (ending_b - initial_b) + ending_b
ending_b_tail = np.tile(ending_b.reshape(1, S, J), (S, 1, 1))
guesses_b = np.append(guesses_b, ending_b_tail, axis=0)
domain3 = np.tile(np.linspace(0, 1, T).reshape(T, 1, 1), (1, S, J))
guesses_n = domain3 * (nssmat - initial_n) + initial_n
ending_n_tail = np.tile(nssmat.reshape(1, S, J), (S, 1, 1))
guesses_n = np.append(guesses_n, ending_n_tail, axis=0)
b_mat = np.zeros((T + S, S, J))
n_mat = np.zeros((T + S, S, J))
ind = np.arange(S)
TPIiter = 0
TPIdist = 10
euler_errors = np.zeros((T, 2 * S, J))
TPIdist_vec = np.zeros(maxiter)
while (TPIiter < maxiter) and (TPIdist >= mindist_TPI):
Kpath_TPI = list(Kinit) + list(np.ones(10) * Kss)
Lpath_TPI = list(Linit) + list(np.ones(10) * Lss)
# Plot TPI for K for each iteration, so we can see if there is a
# problem
if PLOT_TPI is True:
plt.figure()
plt.axhline(
y=Kss, color='black', linewidth=2, label=r"Steady State $\hat{K}$", ls='--')
plt.plot(np.arange(
T + 10), Kpath_TPI[:T + 10], 'b', linewidth=2, label=r"TPI time path $\hat{K}_t$")
plt.savefig(os.path.join(TPI_FIG_DIR, "TPI_K"))
# Uncomment the following print statements to make sure all euler equations are converging.
# If they don't, then you'll have negative consumption or consumption spikes. If they don't,
# it is the initial guesses. You might need to scale them differently. It is rather delicate for the first
# few periods and high ability groups.
for j in xrange(J):
b_mat[1, -1, j], n_mat[0, -1, j] = np.array(opt.fsolve(SS_TPI_firstdoughnutring, [guesses_b[1, -1, j], guesses_n[0, -1, j]],
args=(winit[1], rinit[1], BQinit[1, j], T_H_init[1], initial_b, factor_ss, j, parameters, theta, tau_bq), xtol=1e-13))
# if np.array(SS_TPI_firstdoughnutring([b_mat[1, -1, j], n_mat[0, -1, j]], winit[1], rinit[1], BQinit[1, j], T_H_init[1], initial_b, factor_ss, j, parameters, theta, tau_bq)).max() > 1e-6:
# print 'minidoughnut:',
# np.array(SS_TPI_firstdoughnutring([b_mat[1, -1, j], n_mat[0, -1,
# j]], winit[1], rinit[1], BQinit[1, j], T_H_init[1], initial_b,
# factor_ss, j, parameters, theta, tau_bq)).max()
for s in xrange(S - 2): # Upper triangle
ind2 = np.arange(s + 2)
b_guesses_to_use = np.diag(
guesses_b[1:S + 1, :, j], S - (s + 2))
n_guesses_to_use = np.diag(guesses_n[:S, :, j], S - (s + 2))
solutions = opt.fsolve(Steady_state_TPI_solver, list(
b_guesses_to_use) + list(n_guesses_to_use), args=(
winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, s, 0, parameters, theta, tau_bq, rho, lambdas, e, initial_b, chi_b, chi_n), xtol=1e-13)
b_vec = solutions[:len(solutions) / 2]
b_mat[1 + ind2, S - (s + 2) + ind2, j] = b_vec
n_vec = solutions[len(solutions) / 2:]
n_mat[ind2, S - (s + 2) + ind2, j] = n_vec
# if abs(np.array(Steady_state_TPI_solver(solutions, winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, s, 0, parameters, theta, tau_bq, rho, lambdas, e, initial_b, chi_b, chi_n))).max() > 1e-6:
# print 's-loop:',
# abs(np.array(Steady_state_TPI_solver(solutions, winit, rinit,
# BQinit[:, j], T_H_init, factor_ss, j, s, 0, parameters,
# theta, tau_bq, rho, lambdas, e, initial_b, chi_b,
# chi_n))).max()
for t in xrange(0, T):
b_guesses_to_use = .75 * \
np.diag(guesses_b[t + 1:t + S + 1, :, j])
n_guesses_to_use = np.diag(guesses_n[t:t + S, :, j])
solutions = opt.fsolve(Steady_state_TPI_solver, list(
b_guesses_to_use) + list(n_guesses_to_use), args=(
winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, None, t, parameters, theta, tau_bq, rho, lambdas, e, None, chi_b, chi_n), xtol=1e-13)
b_vec = solutions[:S]
b_mat[t + 1 + ind, ind, j] = b_vec
n_vec = solutions[S:]
n_mat[t + ind, ind, j] = n_vec
inputs = list(solutions)
euler_errors[t, :, j] = np.abs(Steady_state_TPI_solver(
inputs, winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, None, t, parameters, theta, tau_bq, rho, lambdas, e, None, chi_b, chi_n))
# if euler_errors.max() > 1e-6:
# print 't-loop:', euler_errors.max()
# Force the initial distribution of capital to be as given above.
b_mat[0, :, :] = initial_b
Kinit = household.get_K(b_mat[:T], omega_stationary[:T].reshape(
T, S, 1), lambdas.reshape(1, 1, J), g_n_vector[:T], 'TPI')
Linit = firm.get_L(e.reshape(1, S, J), n_mat[:T], omega_stationary[
:T, :].reshape(T, S, 1), lambdas.reshape(1, 1, J), 'TPI')
Ynew = firm.get_Y(Kinit, Linit, parameters)
wnew = firm.get_w(Ynew, Linit, parameters)
rnew = firm.get_r(Ynew, Kinit, parameters)
# the following needs a g_n term
BQnew = household.get_BQ(rnew.reshape(T, 1), b_mat[:T], omega_stationary[:T].reshape(
T, S, 1), lambdas.reshape(1, 1, J), rho.reshape(1, S, 1), g_n_vector[:T].reshape(T, 1), 'TPI')
bmat_s = np.zeros((T, S, J))
bmat_s[:, 1:, :] = b_mat[:T, :-1, :]
T_H_new = np.array(list(tax.get_lump_sum(rnew.reshape(T, 1, 1), bmat_s, wnew.reshape(
T, 1, 1), e.reshape(1, S, J), n_mat[:T], BQnew.reshape(T, 1, J), lambdas.reshape(
1, 1, J), factor_ss, omega_stationary[:T].reshape(T, S, 1), 'TPI', parameters, theta, tau_bq)) + [T_Hss] * S)
winit[:T] = utils.convex_combo(wnew, winit[:T], parameters)
rinit[:T] = utils.convex_combo(rnew, rinit[:T], parameters)
BQinit[:T] = utils.convex_combo(BQnew, BQinit[:T], parameters)
T_H_init[:T] = utils.convex_combo(
T_H_new[:T], T_H_init[:T], parameters)
guesses_b = utils.convex_combo(b_mat, guesses_b, parameters)
guesses_n = utils.convex_combo(n_mat, guesses_n, parameters)
if T_H_init.all() != 0:
TPIdist = np.array(list(utils.perc_dif_func(rnew, rinit[:T])) + list(utils.perc_dif_func(BQnew, BQinit[:T]).flatten()) + list(
utils.perc_dif_func(wnew, winit[:T])) + list(utils.perc_dif_func(T_H_new, T_H_init))).max()
else:
TPIdist = np.array(list(utils.perc_dif_func(rnew, rinit[:T])) + list(utils.perc_dif_func(BQnew, BQinit[:T]).flatten()) + list(
utils.perc_dif_func(wnew, winit[:T])) + list(np.abs(T_H_new, T_H_init))).max()
TPIdist_vec[TPIiter] = TPIdist
# After T=10, if cycling occurs, drop the value of nu
# wait til after T=10 or so, because sometimes there is a jump up
# in the first couple iterations
if TPIiter > 10:
if TPIdist_vec[TPIiter] - TPIdist_vec[TPIiter - 1] > 0:
nu /= 2
print 'New Value of nu:', nu
TPIiter += 1
print '\tIteration:', TPIiter
print '\t\tDistance:', TPIdist
print 'Computing final solutions'
# As in SS, you need the final distributions of b and n to match the final
# w, r, BQ, etc. Otherwise the euler errors are large. You need one more
# fsolve.
for j in xrange(J):
b_mat[1, -1, j], n_mat[0, -1, j] = np.array(opt.fsolve(SS_TPI_firstdoughnutring, [guesses_b[1, -1, j], guesses_n[0, -1, j]],
args=(winit[1], rinit[1], BQinit[1, j], T_H_init[1], initial_b, factor_ss, j, parameters, theta, tau_bq), xtol=1e-13))
for s in xrange(S - 2): # Upper triangle
ind2 = np.arange(s + 2)
b_guesses_to_use = np.diag(guesses_b[1:S + 1, :, j], S - (s + 2))
n_guesses_to_use = np.diag(guesses_n[:S, :, j], S - (s + 2))
solutions = opt.fsolve(Steady_state_TPI_solver, list(
b_guesses_to_use) + list(n_guesses_to_use), args=(
winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, s, 0, parameters, theta, tau_bq, rho, lambdas, e, initial_b, chi_b, chi_n), xtol=1e-13)
b_vec = solutions[:len(solutions) / 2]
b_mat[1 + ind2, S - (s + 2) + ind2, j] = b_vec
n_vec = solutions[len(solutions) / 2:]
n_mat[ind2, S - (s + 2) + ind2, j] = n_vec
for t in xrange(0, T):
b_guesses_to_use = .75 * np.diag(guesses_b[t + 1:t + S + 1, :, j])
n_guesses_to_use = np.diag(guesses_n[t:t + S, :, j])
solutions = opt.fsolve(Steady_state_TPI_solver, list(
b_guesses_to_use) + list(n_guesses_to_use), args=(
winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, None, t, parameters, theta, tau_bq, rho, lambdas, e, None, chi_b, chi_n), xtol=1e-13)
b_vec = solutions[:S]
b_mat[t + 1 + ind, ind, j] = b_vec
n_vec = solutions[S:]
n_mat[t + ind, ind, j] = n_vec
inputs = list(solutions)
euler_errors[t, :, j] = np.abs(Steady_state_TPI_solver(
inputs, winit, rinit, BQinit[:, j], T_H_init, factor_ss, j, None, t, parameters, theta, tau_bq, rho, lambdas, e, None, chi_b, chi_n))
b_mat[0, :, :] = initial_b
'''
------------------------------------------------------------------------
Generate variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
Kpath_TPI = np.array(list(Kinit) + list(np.ones(10) * Kss))
Lpath_TPI = np.array(list(Linit) + list(np.ones(10) * Lss))
BQpath_TPI = np.array(list(BQinit) + list(np.ones((10, J)) * BQss))
b_s = np.zeros((T, S, J))
b_s[:, 1:, :] = b_mat[:T, :-1, :]
b_splus1 = np.zeros((T, S, J))
b_splus1[:, :, :] = b_mat[1:T + 1, :, :]
tax_path = tax.total_taxes(rinit[:T].reshape(T, 1, 1), b_s, winit[:T].reshape(T, 1, 1), e.reshape(
1, S, J), n_mat[:T], BQinit[:T, :].reshape(T, 1, J), lambdas, factor_ss, T_H_init[:T].reshape(T, 1, 1), None, 'TPI', False, parameters, theta, tau_bq)
c_path = household.get_cons(rinit[:T].reshape(T, 1, 1), b_s, winit[:T].reshape(T, 1, 1), e.reshape(
1, S, J), n_mat[:T], BQinit[:T].reshape(T, 1, J), lambdas.reshape(1, 1, J), b_splus1, parameters, tax_path)
Y_path = firm.get_Y(Kpath_TPI[:T], Lpath_TPI[:T], parameters)
C_path = household.get_C(c_path, omega_stationary[
:T].reshape(T, S, 1), lambdas, 'TPI')
I_path = firm.get_I(Kpath_TPI[1:T + 1],
Kpath_TPI[:T], delta, g_y, g_n_vector[:T])
print 'Resource Constraint Difference:', Y_path - C_path - I_path
print'Checking time path for violations of constaints.'
for t in xrange(T):
household.constraint_checker_TPI(
b_mat[t], n_mat[t], c_path[t], t, parameters)
eul_savings = euler_errors[:, :S, :].max(1).max(1)
eul_laborleisure = euler_errors[:, S:, :].max(1).max(1)
'''
------------------------------------------------------------------------
Save variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
output = {'Kpath_TPI': Kpath_TPI, 'b_mat': b_mat, 'c_path': c_path,
'eul_savings': eul_savings, 'eul_laborleisure': eul_laborleisure,
'Lpath_TPI': Lpath_TPI, 'BQpath_TPI': BQpath_TPI, 'n_mat': n_mat,
'rinit': rinit, 'Yinit': Yinit, 'T_H_init': T_H_init,
'tax_path': tax_path, 'winit': winit}
if get_baseline:
tpi_init_dir = os.path.join(output_dir, "TPIinit")
utils.mkdirs(tpi_init_dir)
tpi_init_vars = os.path.join(tpi_init_dir, "TPIinit_vars.pkl")
pickle.dump(output, open(tpi_init_vars, "wb"))
else:
tpi_dir = os.path.join(output_dir, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_vars.pkl")
pickle.dump(output, open(tpi_vars, "wb"))
def run_TPI(income_tax_params, tpi_params, iterative_params, small_open_params, initial_values, SS_values, fiscal_params, biz_tax_params, output_dir="./OUTPUT", baseline_spending=False):
# unpack tuples of parameters
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_params
maxiter, mindist_SS, mindist_TPI = iterative_params
J, S, T, BW, beta, sigma, alpha, gamma, epsilon, Z, delta, ltilde, nu, g_y,\
g_n_vector, tau_payroll, tau_bq, rho, omega, N_tilde, lambdas, imm_rates, e, retire, mean_income_data,\
factor, h_wealth, p_wealth, m_wealth, b_ellipse, upsilon, chi_b, chi_n, theta = tpi_params
# K0, b_sinit, b_splus1init, L0, Y0,\
# w0, r0, BQ0, T_H_0, factor, tax0, c0, initial_b, initial_n, omega_S_preTP = initial_values
small_open, tpi_firm_r, tpi_hh_r = small_open_params
B0, b_sinit, b_splus1init, factor, initial_b, initial_n, omega_S_preTP, initial_debt = initial_values
Kss, Bss, Lss, rss, wss, BQss, T_Hss, revenue_ss, bssmat_splus1, nssmat, Yss, Gss = SS_values
tau_b, delta_tau = biz_tax_params
if baseline_spending==False:
budget_balance, ALPHA_T, ALPHA_G, tG1, tG2, rho_G, debt_ratio_ss = fiscal_params
else:
budget_balance, ALPHA_T, ALPHA_G, tG1, tG2, rho_G, debt_ratio_ss, T_Hbaseline, Gbaseline = fiscal_params
print 'Government spending breakpoints are tG1: ', tG1, '; and tG2:', tG2
TPI_FIG_DIR = output_dir
# Initialize guesses at time paths
# Make array of initial guesses for labor supply and savings
domain = np.linspace(0, T, T)
domain2 = np.tile(domain.reshape(T, 1, 1), (1, S, J))
ending_b = bssmat_splus1
guesses_b = (-1 / (domain2 + 1)) * (ending_b - initial_b) + ending_b
ending_b_tail = np.tile(ending_b.reshape(1, S, J), (S, 1, 1))
guesses_b = np.append(guesses_b, ending_b_tail, axis=0)
domain3 = np.tile(np.linspace(0, 1, T).reshape(T, 1, 1), (1, S, J))
guesses_n = domain3 * (nssmat - initial_n) + initial_n
ending_n_tail = np.tile(nssmat.reshape(1, S, J), (S, 1, 1))
guesses_n = np.append(guesses_n, ending_n_tail, axis=0)
b_mat = guesses_b#np.zeros((T + S, S, J))
n_mat = guesses_n#np.zeros((T + S, S, J))
ind = np.arange(S)
L_init = np.ones((T+S,))*Lss
B_init = np.ones((T+S,))*Bss
L_params = (e.reshape(1, S, J), omega[:T, :].reshape(T, S, 1), lambdas.reshape(1, 1, J), 'TPI')
L_init[:T] = firm.get_L(n_mat[:T], L_params)
B_params = (omega[:T-1].reshape(T-1, S, 1), lambdas.reshape(1, 1, J), imm_rates[:T-1].reshape(T-1,S,1), g_n_vector[1:T], 'TPI')
B_init[1:T] = household.get_K(b_mat[:T-1], B_params)
B_init[0] = B0
if small_open == False:
if budget_balance:
K_init = B_init
else:
K_init = B_init * Kss/Bss
else:
K_params = (Z, gamma, epsilon, delta, tau_b, delta_tau)
K_init = firm.get_K(L_init, tpi_firm_r, K_params)
K = K_init
# if np.any(K < 0):
# print 'K_init has negative elements. Setting them positive to prevent NAN.'
# K[:T] = np.fmax(K[:T], 0.05*B[:T])
L = L_init
B = B_init
Y_params = (Z, gamma, epsilon)
Y = firm.get_Y(K, L, Y_params)
w_params = (Z, gamma, epsilon)
w = firm.get_w(Y, L, w_params)
if small_open == False:
r_params = (Z, gamma, epsilon, delta, tau_b, delta_tau)
r = firm.get_r(Y, K, r_params)
else:
r = tpi_hh_r
BQ = np.zeros((T + S, J))
BQ0_params = (omega_S_preTP.reshape(S, 1), lambdas, rho.reshape(S, 1), g_n_vector[0], 'SS')
BQ0 = household.get_BQ(r[0], initial_b, BQ0_params)
for j in xrange(J):
BQ[:, j] = list(np.linspace(BQ0[j], BQss[j], T)) + [BQss[j]] * S
BQ = np.array(BQ)
if budget_balance:
if np.abs(T_Hss) < 1e-13 :
T_Hss2 = 0.0 # sometimes SS is very small but not zero, even if taxes are zero, this get's rid of the approximation error, which affects the perc changes below
else:
T_Hss2 = T_Hss
T_H = np.ones(T + S) * T_Hss2
REVENUE = T_H
G = np.zeros(T + S)
elif baseline_spending==False:
T_H = ALPHA_T * Y
elif baseline_spending==True:
T_H = T_Hbaseline
T_H_new = T_H # Need to set T_H_new for later reference
G = Gbaseline
G_0 = Gbaseline[0]
# Initialize some inputs
# D = np.zeros(T + S)
D = debt_ratio_ss*Y
omega_shift = np.append(omega_S_preTP.reshape(1,S),omega[:T-1,:],axis=0)
BQ_params = (omega_shift.reshape(T, S, 1), lambdas.reshape(1, 1, J), rho.reshape(1, S, 1),
g_n_vector[:T].reshape(T, 1), 'TPI')
tax_params = np.zeros((T,S,J,etr_params.shape[2]))
for i in range(etr_params.shape[2]):
tax_params[:,:,:,i] = np.tile(np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))
REVENUE_params = (np.tile(e.reshape(1, S, J),(T,1,1)), lambdas.reshape(1, 1, J), omega[:T].reshape(T, S, 1), 'TPI',
tax_params, theta, tau_bq, tau_payroll, h_wealth, p_wealth, m_wealth, retire, T, S, J, tau_b, delta_tau)
# print 'D/Y:', D[:T]/Y[:T]
# print 'T/Y:', T_H[:T]/Y[:T]
# print 'G/Y:', G[:T]/Y[:T]
# print 'Int payments to GDP:', (r[:T]*D[:T])/Y[:T]
# quit()
TPIiter = 0
TPIdist = 10
PLOT_TPI = False
report_tG1 = False
euler_errors = np.zeros((T, 2 * S, J))
TPIdist_vec = np.zeros(maxiter)
print 'analytical mtrs in tpi = ', analytical_mtrs
while (TPIiter < maxiter) and (TPIdist >= mindist_TPI):
# Plot TPI for K for each iteration, so we can see if there is a
# problem
if PLOT_TPI is True:
#K_plot = list(K) + list(np.ones(10) * Kss)
D_plot = list(D) + list(np.ones(10) * Yss * debt_ratio_ss)
plt.figure()
plt.axhline(
y=Kss, color='black', linewidth=2, label=r"Steady State $\hat{K}$", ls='--')
plt.plot(np.arange(
T + 10), D_plot[:T + 10], 'b', linewidth=2, label=r"TPI time path $\hat{K}_t$")
plt.savefig(os.path.join(TPI_FIG_DIR, "TPI_D"))
if report_tG1 is True:
print '\tAt time tG1-1:'
print '\t\tG = ', G[tG1-1]
print '\t\tK = ', K[tG1-1]
print '\t\tr = ', r[tG1-1]
print '\t\tD = ', D[tG1-1]
guesses = (guesses_b, guesses_n)
outer_loop_vars = (r, w, K, BQ, T_H)
inner_loop_params = (income_tax_params, tpi_params, initial_values, ind)
# Solve HH problem in inner loop
euler_errors, b_mat, n_mat = inner_loop(guesses, outer_loop_vars, inner_loop_params)
bmat_s = np.zeros((T, S, J))
bmat_s[0, 1:, :] = initial_b[:-1, :]
bmat_s[1:, 1:, :] = b_mat[:T-1, :-1, :]
bmat_splus1 = np.zeros((T, S, J))
bmat_splus1[:, :, :] = b_mat[:T, :, :]
#L_params = (e.reshape(1, S, J), omega[:T, :].reshape(T, S, 1), lambdas.reshape(1, 1, J), 'TPI') # defined above
L[:T] = firm.get_L(n_mat[:T], L_params)
#B_params = (omega[:T-1].reshape(T-1, S, 1), lambdas.reshape(1, 1, J), imm_rates[:T-1].reshape(T-1,S,1), g_n_vector[1:T], 'TPI') # defined above
B[1:T] = household.get_K(bmat_splus1[:T-1], B_params)
if np.any(B) < 0:
print 'B has negative elements. B[0:9]:', B[0:9]
print 'B[T-2:T]:', B[T-2,T]
if small_open == False:
if budget_balance:
K[:T] = B[:T]
else:
if baseline_spending == False:
Y = T_H/ALPHA_T #SBF 3/3: This seems totally unnecessary as both these variables are defined above.
# tax_params = np.zeros((T,S,J,etr_params.shape[2]))
# for i in range(etr_params.shape[2]):
# tax_params[:,:,:,i] = np.tile(np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))
# REVENUE_params = (np.tile(e.reshape(1, S, J),(T,1,1)), lambdas.reshape(1, 1, J), omega[:T].reshape(T, S, 1), 'TPI',
# tax_params, theta, tau_bq, tau_payroll, h_wealth, p_wealth, m_wealth, retire, T, S, J, tau_b, delta_tau) # define above
REVENUE = np.array(list(tax.revenue(np.tile(r[:T].reshape(T, 1, 1),(1,S,J)), np.tile(w[:T].reshape(T, 1, 1),(1,S,J)),
bmat_s, n_mat[:T,:,:], BQ[:T].reshape(T, 1, J), Y[:T], L[:T], K[:T], factor, REVENUE_params)) + [revenue_ss] * S)
D_0 = initial_debt * Y[0]
other_dg_params = (T, r, g_n_vector, g_y)
if baseline_spending==False:
G_0 = ALPHA_G[0] * Y[0]
dg_fixed_values = (Y, REVENUE, T_H, D_0,G_0)
Dnew, G = fiscal.D_G_path(dg_fixed_values, fiscal_params, other_dg_params, baseline_spending=baseline_spending)
K[:T] = B[:T] - Dnew[:T]
if np.any(K < 0):
print 'K has negative elements. Setting them positive to prevent NAN.'
K[:T] = np.fmax(K[:T], 0.05*B[:T])
else:
# K_params previously set to = (Z, gamma, epsilon, delta, tau_b, delta_tau)
K[:T] = firm.get_K(L[:T], tpi_firm_r[:T], K_params)
Y_params = (Z, gamma, epsilon)
Ynew = firm.get_Y(K[:T], L[:T], Y_params)
Y = Ynew
w_params = (Z, gamma, epsilon)
wnew = firm.get_w(Ynew[:T], L[:T], w_params)
if small_open == False:
r_params = (Z, gamma, epsilon, delta, tau_b, delta_tau)
rnew = firm.get_r(Ynew[:T], K[:T], r_params)
else:
rnew = r.copy()
print 'Y and T_H: ', Y[3], T_H[3]
# omega_shift = np.append(omega_S_preTP.reshape(1,S),omega[:T-1,:],axis=0) # defined above
# BQ_params = (omega_shift.reshape(T, S, 1), lambdas.reshape(1, 1, J), rho.reshape(1, S, 1),
# g_n_vector[:T].reshape(T, 1), 'TPI') # defined above
b_mat_shift = np.append(np.reshape(initial_b,(1,S,J)),b_mat[:T-1,:,:],axis=0)
BQnew = household.get_BQ(rnew[:T].reshape(T, 1), b_mat_shift, BQ_params)
# tax_params = np.zeros((T,S,J,etr_params.shape[2]))
# for i in range(etr_params.shape[2]):
# tax_params[:,:,:,i] = np.tile(np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))
# REVENUE_params = (np.tile(e.reshape(1, S, J),(T,1,1)), lambdas.reshape(1, 1, J), omega[:T].reshape(T, S, 1), 'TPI',
# tax_params, theta, tau_bq, tau_payroll, h_wealth, p_wealth, m_wealth, retire, T, S, J, tau_b, delta_tau) # defined above
REVENUE = np.array(list(tax.revenue(np.tile(rnew[:T].reshape(T, 1, 1),(1,S,J)), np.tile(wnew[:T].reshape(T, 1, 1),(1,S,J)),
bmat_s, n_mat[:T,:,:], BQnew[:T].reshape(T, 1, J), Y[:T], L[:T], K[:T], factor, REVENUE_params)) + [revenue_ss] * S)
if budget_balance:
T_H_new = REVENUE
elif baseline_spending==False:
T_H_new = ALPHA_T[:T] * Y[:T]
# If baseline_spending==True, no need to update T_H, which remains fixed.
if small_open==True and budget_balance==False:
# Loop through years to calculate debt and gov't spending. This is done earlier when small_open=False.
D_0 = initial_debt * Y[0]
other_dg_params = (T, r, g_n_vector, g_y)
if baseline_spending==False:
G_0 = ALPHA_G[0] * Y[0]
dg_fixed_values = (Y, REVENUE, T_H, D_0,G_0)
Dnew, G = fiscal.D_G_path(dg_fixed_values, fiscal_params, other_dg_params, baseline_spending=baseline_spending)
w[:T] = utils.convex_combo(wnew[:T], w[:T], nu)
r[:T] = utils.convex_combo(rnew[:T], r[:T], nu)
BQ[:T] = utils.convex_combo(BQnew[:T], BQ[:T], nu)
# D[:T] = utils.convex_combo(Dnew[:T], D[:T], nu)
D = Dnew
Y[:T] = utils.convex_combo(Ynew[:T], Y[:T], nu)
if baseline_spending==False:
T_H[:T] = utils.convex_combo(T_H_new[:T], T_H[:T], nu)
guesses_b = utils.convex_combo(b_mat, guesses_b, nu)
guesses_n = utils.convex_combo(n_mat, guesses_n, nu)
print 'r diff: ', (rnew[:T]-r[:T]).max(), (rnew[:T]-r[:T]).min()
print 'w diff: ', (wnew[:T]-w[:T]).max(), (wnew[:T]-w[:T]).min()
print 'BQ diff: ', (BQnew[:T]-BQ[:T]).max(), (BQnew[:T]-BQ[:T]).min()
print 'T_H diff: ', (T_H_new[:T]-T_H[:T]).max(), (T_H_new[:T]-T_H[:T]).min()
if baseline_spending==False:
if T_H.all() != 0:
TPIdist = np.array(list(utils.pct_diff_func(rnew[:T], r[:T])) + list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten()) + list(
utils.pct_diff_func(wnew[:T], w[:T])) + list(utils.pct_diff_func(T_H_new[:T], T_H[:T]))).max()
else:
TPIdist = np.array(list(utils.pct_diff_func(rnew[:T], r[:T])) + list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten()) + list(
utils.pct_diff_func(wnew[:T], w[:T])) + list(np.abs(T_H[:T]))).max()
else:
# TPIdist = np.array(list(utils.pct_diff_func(rnew[:T], r[:T])) + list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten()) + list(
# utils.pct_diff_func(wnew[:T], w[:T])) + list(utils.pct_diff_func(Dnew[:T], D[:T]))).max()
TPIdist = np.array(list(utils.pct_diff_func(rnew[:T], r[:T])) + list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten()) + list(
utils.pct_diff_func(wnew[:T], w[:T])) + list(utils.pct_diff_func(Ynew[:T], Y[:T]))).max()
TPIdist_vec[TPIiter] = TPIdist
# After T=10, if cycling occurs, drop the value of nu
# wait til after T=10 or so, because sometimes there is a jump up
# in the first couple iterations
# if TPIiter > 10:
# if TPIdist_vec[TPIiter] - TPIdist_vec[TPIiter - 1] > 0:
# nu /= 2
# print 'New Value of nu:', nu
TPIiter += 1
print 'Iteration:', TPIiter
print '\tDistance:', TPIdist
# print 'D/Y:', (D[:T]/Ynew[:T]).max(), (D[:T]/Ynew[:T]).min(), np.median(D[:T]/Ynew[:T])
# print 'T/Y:', (T_H_new[:T]/Ynew[:T]).max(), (T_H_new[:T]/Ynew[:T]).min(), np.median(T_H_new[:T]/Ynew[:T])
# print 'G/Y:', (G[:T]/Ynew[:T]).max(), (G[:T]/Ynew[:T]).min(), np.median(G[:T]/Ynew[:T])
# print 'Int payments to GDP:', ((r[:T]*D[:T])/Ynew[:T]).max(), ((r[:T]*D[:T])/Ynew[:T]).min(), np.median((r[:T]*D[:T])/Ynew[:T])
#
# print 'D/Y:', (D[:T]/Ynew[:T])
# print 'T/Y:', (T_H_new[:T]/Ynew[:T])
# print 'G/Y:', (G[:T]/Ynew[:T])
#
# print 'deficit: ', REVENUE[:T] - T_H_new[:T] - G[:T]
# Loop through years to calculate debt and gov't spending. The re-assignment of G0 & D0 is necessary because Y0 may change in the TPI loop.
if budget_balance == False:
D_0 = initial_debt * Y[0]
other_dg_params = (T, r, g_n_vector, g_y)
if baseline_spending==False:
G_0 = ALPHA_G[0] * Y[0]
dg_fixed_values = (Y, REVENUE, T_H, D_0,G_0)
D, G = fiscal.D_G_path(dg_fixed_values, fiscal_params, other_dg_params, baseline_spending=baseline_spending)
# Solve HH problem in inner loop
guesses = (guesses_b, guesses_n)
outer_loop_vars = (r, w, K, BQ, T_H)
inner_loop_params = (income_tax_params, tpi_params, initial_values, ind)
euler_errors, b_mat, n_mat = inner_loop(guesses, outer_loop_vars, inner_loop_params)
bmat_s = np.zeros((T, S, J))
bmat_s[0, 1:, :] = initial_b[:-1, :]
bmat_s[1:, 1:, :] = b_mat[:T-1, :-1, :]
bmat_splus1 = np.zeros((T, S, J))
bmat_splus1[:, :, :] = b_mat[:T, :, :]
#L_params = (e.reshape(1, S, J), omega[:T, :].reshape(T, S, 1), lambdas.reshape(1, 1, J), 'TPI') # defined above
L[:T] = firm.get_L(n_mat[:T], L_params)
#B_params = (omega[:T-1].reshape(T-1, S, 1), lambdas.reshape(1, 1, J), imm_rates[:T-1].reshape(T-1,S,1), g_n_vector[1:T], 'TPI') # defined above
B[1:T] = household.get_K(bmat_splus1[:T-1], B_params)
if small_open == False:
K[:T] = B[:T] - D[:T]
else:
# K_params previously set to = (Z, gamma, epsilon, delta, tau_b, delta_tau)
K[:T] = firm.get_K(L[:T], tpi_firm_r[:T], K_params)
# Y_params previously set to = (Z, gamma, epsilon)
Ynew = firm.get_Y(K[:T], L[:T], Y_params)
# testing for change in Y
ydiff = Ynew[:T] - Y[:T]
ydiff_max = np.amax(np.abs(ydiff))
print 'ydiff_max = ', ydiff_max
w_params = (Z, gamma, epsilon)
wnew = firm.get_w(Ynew[:T], L[:T], w_params)
if small_open == False:
# r_params previously set to = (Z, gamma, epsilon, delta, tau_b, delta_tau)
rnew = firm.get_r(Ynew[:T], K[:T], r_params)
else:
rnew = r
# Note: previously, Y was not reassigned to equal Ynew at this point.
Y = Ynew[:]
# omega_shift = np.append(omega_S_preTP.reshape(1,S),omega[:T-1,:],axis=0)
# BQ_params = (omega_shift.reshape(T, S, 1), lambdas.reshape(1, 1, J), rho.reshape(1, S, 1),
# g_n_vector[:T].reshape(T, 1), 'TPI')
b_mat_shift = np.append(np.reshape(initial_b,(1,S,J)),b_mat[:T-1,:,:],axis=0)
BQnew = household.get_BQ(rnew[:T].reshape(T, 1), b_mat_shift, BQ_params)
# tax_params = np.zeros((T,S,J,etr_params.shape[2]))
# for i in range(etr_params.shape[2]):
# tax_params[:,:,:,i] = np.tile(np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))
# REVENUE_params = (np.tile(e.reshape(1, S, J),(T,1,1)), lambdas.reshape(1, 1, J), omega[:T].reshape(T, S, 1), 'TPI',
# tax_params, theta, tau_bq, tau_payroll, h_wealth, p_wealth, m_wealth, retire, T, S, J, tau_b, delta_tau)
REVENUE = np.array(list(tax.revenue(np.tile(rnew[:T].reshape(T, 1, 1),(1,S,J)), np.tile(wnew[:T].reshape(T, 1, 1),(1,S,J)),
bmat_s, n_mat[:T,:,:], BQnew[:T].reshape(T, 1, J), Ynew[:T], L[:T], K[:T], factor, REVENUE_params)) + [revenue_ss] * S)
etr_params_path = np.zeros((T,S,J,etr_params.shape[2]))
for i in range(etr_params.shape[2]):
etr_params_path[:,:,:,i] = np.tile(np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))
tax_path_params = (np.tile(e.reshape(1, S, J),(T,1,1)), lambdas, 'TPI', retire, etr_params_path, h_wealth,
p_wealth, m_wealth, tau_payroll, theta, tau_bq, J, S)
tax_path = tax.total_taxes(np.tile(r[:T].reshape(T, 1, 1),(1,S,J)), np.tile(w[:T].reshape(T, 1, 1),(1,S,J)), bmat_s,
n_mat[:T,:,:], BQ[:T, :].reshape(T, 1, J), factor, T_H[:T].reshape(T, 1, 1), None, False, tax_path_params)
cons_params = (e.reshape(1, S, J), lambdas.reshape(1, 1, J), g_y)
c_path = household.get_cons(r[:T].reshape(T, 1, 1), w[:T].reshape(T, 1, 1), bmat_s, bmat_splus1, n_mat[:T,:,:],
BQ[:T].reshape(T, 1, J), tax_path, cons_params)
C_params = (omega[:T].reshape(T, S, 1), lambdas, 'TPI')
C = household.get_C(c_path, C_params)
if budget_balance==False:
D_0 = initial_debt * Y[0]
other_dg_params = (T, r, g_n_vector, g_y)
if baseline_spending==False:
G_0 = ALPHA_G[0] * Y[0]
dg_fixed_values = (Y, REVENUE, T_H, D_0,G_0)
D, G = fiscal.D_G_path(dg_fixed_values, fiscal_params, other_dg_params, baseline_spending=baseline_spending)
if small_open == False:
I_params = (delta, g_y, omega[:T].reshape(T, S, 1), lambdas, imm_rates[:T].reshape(T, S, 1), g_n_vector[1:T+1], 'TPI')
I = firm.get_I(bmat_splus1[:T], K[1:T+1], K[:T], I_params)
rc_error = Y[:T] - C[:T] - I[:T] - G[:T]
else:
#InvestmentPlaceholder = np.zeros(bmat_splus1[:T].shape)
#I_params = (delta, g_y, omega[:T].reshape(T, S, 1), lambdas, imm_rates[:T].reshape(T, S, 1), g_n_vector[1:T+1], 'TPI')
I = (1+g_n_vector[:T])*np.exp(g_y)*K[1:T+1] - (1.0 - delta) * K[:T] #firm.get_I(InvestmentPlaceholder, K[1:T+1], K[:T], I_params)
BI_params = (0.0, g_y, omega[:T].reshape(T, S, 1), lambdas, imm_rates[:T].reshape(T, S, 1), g_n_vector[1:T+1], 'TPI')
BI = firm.get_I(bmat_splus1[:T], B[1:T+1], B[:T], BI_params)
new_borrowing = D[1:T]*(1+g_n_vector[1:T])*np.exp(g_y) - D[:T-1]
rc_error = Y[:T-1] + new_borrowing - (C[:T-1] + BI[:T-1] + G[:T-1] ) + (tpi_hh_r[:T-1] * B[:T-1] - (delta + tpi_firm_r[:T-1])*K[:T-1] - tpi_hh_r[:T-1]*D[:T-1])
#print 'Y(T-1):', Y[T-1], '\n','C(T-1):', C[T-1], '\n','K(T-1):', K[T-1], '\n','B(T-1):', B[T-1], '\n','BI(T-1):', BI[T-1], '\n','I(T-1):', I[T-1]
rce_max = np.amax(np.abs(rc_error))
print 'Max absolute value resource constraint error:', rce_max
print'Checking time path for violations of constraints.'
for t in xrange(T):
household.constraint_checker_TPI(
b_mat[t], n_mat[t], c_path[t], t, ltilde)
eul_savings = euler_errors[:, :S, :].max(1).max(1)
eul_laborleisure = euler_errors[:, S:, :].max(1).max(1)
# print 'Max Euler error, savings: ', eul_savings
# print 'Max Euler error labor supply: ', eul_laborleisure
'''
------------------------------------------------------------------------
Save variables/values so they can be used in other modules
------------------------------------------------------------------------
'''
output = {'Y': Y, 'K': K, 'L': L, 'C': C, 'I': I, 'BQ': BQ,
'REVENUE': REVENUE, 'T_H': T_H, 'G': G, 'D': D,
'r': r, 'w': w, 'b_mat': b_mat, 'n_mat': n_mat,
'c_path': c_path, 'tax_path': tax_path,
'eul_savings': eul_savings, 'eul_laborleisure': eul_laborleisure}
tpi_dir = os.path.join(output_dir, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_vars.pkl")
pickle.dump(output, open(tpi_vars, "wb"))
macro_output = {'Y': Y, 'K': K, 'L': L, 'C': C, 'I': I,
'BQ': BQ, 'T_H': T_H, 'r': r, 'w': w,
'tax_path': tax_path}
growth = (1+g_n_vector)*np.exp(g_y)
with open('TPI_output.csv', 'wb') as csvfile:
tpiwriter = csv.writer(csvfile)
tpiwriter.writerow(Y)
tpiwriter.writerow(D)
tpiwriter.writerow(REVENUE)
tpiwriter.writerow(G)
tpiwriter.writerow(T_H)
tpiwriter.writerow(C)
tpiwriter.writerow(K)
tpiwriter.writerow(I)
tpiwriter.writerow(r)
if small_open == True:
tpiwriter.writerow(B)
tpiwriter.writerow(BI)
tpiwriter.writerow(new_borrowing)
tpiwriter.writerow(growth)
tpiwriter.writerow(rc_error)
tpiwriter.writerow(ydiff)
if np.any(G) < 0:
print 'Government spending is negative along transition path to satisfy budget'
if ((TPIiter >= maxiter) or (np.absolute(TPIdist) > mindist_TPI)) and ENFORCE_SOLUTION_CHECKS :
raise RuntimeError("Transition path equlibrium not found (TPIdist)")
if ((np.any(np.absolute(rc_error) >= mindist_TPI))
and ENFORCE_SOLUTION_CHECKS):
raise RuntimeError("Transition path equlibrium not found (rc_error)")
if ((np.any(np.absolute(eul_savings) >= mindist_TPI) or
(np.any(np.absolute(eul_laborleisure) > mindist_TPI)))
and ENFORCE_SOLUTION_CHECKS):
raise RuntimeError("Transition path equlibrium not found (eulers)")
# Non-stationary output
# macro_ns_output = {'K_ns_path': K_ns_path, 'C_ns_path': C_ns_path, 'I_ns_path': I_ns_path,
# 'L_ns_path': L_ns_path, 'BQ_ns_path': BQ_ns_path,
# 'rinit': rinit, 'Y_ns_path': Y_ns_path, 'T_H_ns_path': T_H_ns_path,
# 'w_ns_path': w_ns_path}
return output, macro_output
# @Time : 2019-03-14 17:59 # @Author : xzr # @Contact : [email protected] # @Desc : 日志类 import logging import os import sys import time from utils import mkdirs from utils.multiprocesslogging import MultiProcessTimedRotatingFileHandler LOGS_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'logs') mkdirs(LOGS_DIR) class Logger(object): '''#使用django 的logging @name 记录的名字 需在setting里设置 ''' __loggers = {} def __new__(cls, name, type_name, when='M', interval=1440, backupCount=10): logger_name = '%s_%s' % (name, type_name) logger = cls.__loggers.get(logger_name, None) if not logger: logger = logging.getLogger(name) the_game_log_dir = os.path.join(LOGS_DIR, name) mkdirs(the_game_log_dir)
def run_steady_state(ss_parameters, iterative_params, get_baseline=False, calibrate_model=False,
output_dir="./OUTPUT"):
'''
------------------------------------------------------------------------
Run SS
------------------------------------------------------------------------
'''
if get_baseline:
# Generate initial guesses for chi^b_j and chi^n_s
chi_params = np.zeros(S + J)
chi_params[:J] = chi_b_guess
chi_params[J:] = chi_n_guess
# First run SS simulation with guesses at initial values for b, n, w, r, etc
# For inital guesses of b and n, we choose very small b, and medium n
b_guess = np.ones((S, J)).flatten() * .01
n_guess = np.ones((S, J)).flatten() * .5 * ltilde
# For initial guesses of w, r, T_H, and factor, we use values that are close
# to some steady state values.
wguess = 1.2
rguess = .06
T_Hguess = 0
factorguess = 100000
solutions = SS_solver(b_guess.reshape(S, J), n_guess.reshape(S, J), wguess, rguess, T_Hguess, factorguess, chi_params[
J:], chi_params[:J], ss_parameters, iterative_params, tau_bq, rho, lambdas, omega_SS, e)
if calibrate_model:
outputs = {'solutions': solutions, 'chi_params': chi_params}
ss_init_path = os.path.join(
output_dir, "Saved_moments/SS_init_solutions.pkl")
pickle.dump(outputs, open(ss_init_path, "wb"))
function_to_minimize_X = lambda x: function_to_minimize(
x, chi_params, ss_parameters, iterative_params, omega_SS, rho, lambdas, tau_bq, e, output_dir)
bnds = tuple([(1e-6, None)] * (S + J))
# In order to scale all the parameters to estimate in the minimizer, we have the minimizer fit a vector of ones that
# will be multiplied by the chi initial guesses inside the function. Otherwise, if chi^b_j=1e5 for some j, and the
# minimizer peturbs that value by 1e-8, the % difference will be extremely small, outside of the tolerance of the
# minimizer, and it will not change that parameter.
chi_params_scalars = np.ones(S + J)
chi_params_scalars = opt.minimize(function_to_minimize_X, chi_params_scalars,
method='TNC', tol=MINIMIZER_TOL, bounds=bnds, options=MINIMIZER_OPTIONS).x
chi_params *= chi_params_scalars
print 'The final scaling params', chi_params_scalars
print 'The final bequest parameter values:', chi_params
solutions_dict = pickle.load(open(ss_init_path, "rb"))
solutions = solutions_dict['solutions']
b_guess = solutions[:S * J]
n_guess = solutions[S * J:2 * S * J]
wguess, rguess, factorguess, T_Hguess = solutions[2 * S * J:]
solutions = SS_solver(b_guess.reshape(S, J), n_guess.reshape(S, J), wguess, rguess, T_Hguess, factorguess, chi_params[
J:], chi_params[:J], ss_parameters, iterative_params, tau_bq, rho, lambdas, omega_SS, e)
else:
variables = pickle.load(open(ss_init_path, "rb"))
solutions = solutions_dict['solutions']
chi_params = solutions_dict['chi_params']
b_guess = solutions[:S * J]
n_guess = solutions[S * J:2 * S * J]
wguess, rguess, factorguess, T_Hguess = solutions[2 * S * J:]
solutions = SS_solver(b_guess.reshape(S, J), n_guess.reshape(S, J), wguess, rguess, T_Hguess, factorguess, chi_params[
J:], chi_params[:J], ss_parameters, iterative_params, tau_bq, rho, lambdas, omega_SS, e)
'''
------------------------------------------------------------------------
Generate the SS values of variables, including euler errors
------------------------------------------------------------------------
'''
if get_baseline:
outputs = {'solutions': solutions, 'chi_params': chi_params}
ss_init_dir = os.path.join(
output_dir, "Saved_moments/SS_init_solutions.pkl")
pickle.dump(outputs, open(ss_init_dir, "wb"))
else:
outputs = {'solutions': solutions, 'chi_params': chi_params}
ss_exp_dir = os.path.join(
output_dir, "Saved_moments/SS_experiment_solutions.pkl")
pickle.dump(outputs, open(ss_exp_dir, "wb"))
bssmat = solutions[0:(S - 1) * J].reshape(S - 1, J)
bq = solutions[(S - 1) * J:S * J]
bssmat_s = np.array(list(np.zeros(J).reshape(1, J)) + list(bssmat))
bssmat_splus1 = np.array(list(bssmat) + list(bq.reshape(1, J)))
nssmat = solutions[S * J:2 * S * J].reshape(S, J)
wss, rss, factor_ss, T_Hss = solutions[2 * S * J:]
Kss = household.get_K(bssmat_splus1, omega_SS.reshape(
S, 1), lambdas, g_n_ss, 'SS')
Lss = firm.get_L(e, nssmat, omega_SS.reshape(S, 1), lambdas, 'SS')
Yss = firm.get_Y(Kss, Lss, ss_parameters)
Iss = firm.get_I(Kss, Kss, delta, g_y, g_n_ss)
theta = tax.replacement_rate_vals(
nssmat, wss, factor_ss, e, J, omega_SS.reshape(S, 1), lambdas)
BQss = household.get_BQ(rss, bssmat_splus1, omega_SS.reshape(
S, 1), lambdas, rho.reshape(S, 1), g_n_ss, 'SS')
b_s = np.array(list(np.zeros(J).reshape((1, J))) + list(bssmat))
taxss = tax.total_taxes(rss, b_s, wss, e, nssmat, BQss, lambdas,
factor_ss, T_Hss, None, 'SS', False, ss_parameters, theta, tau_bq)
cssmat = household.get_cons(rss, b_s, wss, e, nssmat, BQss.reshape(
1, J), lambdas.reshape(1, J), bssmat_splus1, ss_parameters, taxss)
Css = household.get_C(cssmat, omega_SS.reshape(S, 1), lambdas, 'SS')
resource_constraint = Yss - (Css + Iss)
print 'Resource Constraint Difference:', resource_constraint
household.constraint_checker_SS(bssmat, nssmat, cssmat, ss_parameters)
b_s = np.array(list(np.zeros(J).reshape((1, J))) + list(bssmat))
b_splus1 = bssmat_splus1
b_splus2 = np.array(
list(bssmat_splus1[1:]) + list(np.zeros(J).reshape((1, J))))
chi_b = np.tile(chi_params[:J].reshape(1, J), (S, 1))
chi_n = np.array(chi_params[J:])
euler_savings = np.zeros((S, J))
euler_labor_leisure = np.zeros((S, J))
for j in xrange(J):
euler_savings[:, j] = household.euler_savings_func(wss, rss, e[:, j], nssmat[:, j], b_s[:, j], b_splus1[:, j], b_splus2[
:, j], BQss[j], factor_ss, T_Hss, chi_b[:, j], ss_parameters, theta[j], tau_bq[j], rho, lambdas[j])
euler_labor_leisure[:, j] = household.euler_labor_leisure_func(wss, rss, e[:, j], nssmat[:, j], b_s[
:, j], b_splus1[:, j], BQss[j], factor_ss, T_Hss, chi_n, ss_parameters, theta[j], tau_bq[j], lambdas[j])
'''
------------------------------------------------------------------------
Save the values in various ways, depending on the stage of
the simulation, to be used in TPI or graphing functions
------------------------------------------------------------------------
'''
# Pickle variables
output = {'Kss': Kss, 'bssmat': bssmat, 'Lss': Lss, 'nssmat': nssmat, 'Yss': Yss,
'wss': wss, 'rss': rss, 'theta': theta, 'BQss': BQss, 'factor_ss': factor_ss,
'bssmat_s': bssmat_s, 'cssmat': cssmat, 'bssmat_splus1': bssmat_splus1,
'T_Hss': T_Hss, 'euler_savings': euler_savings,
'euler_labor_leisure': euler_labor_leisure, 'chi_n': chi_n,
'chi_b': chi_b}
if get_baseline:
utils.mkdirs(os.path.join(output_dir, "SSinit"))
ss_init_dir = os.path.join(output_dir, "SSinit/ss_init_vars.pkl")
pickle.dump(output, open(ss_init_dir, "wb"))
bssmat_init = bssmat_splus1
nssmat_init = nssmat
# Pickle variables for TPI initial values
output2 = {'bssmat_init': bssmat_init, 'nssmat_init': nssmat_init}
ss_init_tpi = os.path.join(output_dir, "SSinit/ss_init_tpi_vars.pkl")
pickle.dump(output2, open(ss_init_tpi, "wb"))
else:
utils.mkdirs(os.path.join(output_dir, "SS"))
ss_vars = os.path.join(output_dir, "SS/ss_vars.pkl")
pickle.dump(output, open(ss_vars, "wb"))
return output
def run_steady_state(income_tax_parameters, ss_parameters, iterative_params, get_baseline=False, calibrate_model=False, output_dir="./OUTPUT"):
'''
------------------------------------------------------------------------
Run SS
------------------------------------------------------------------------
'''
J, S, T, BW, beta, sigma, alpha, Z, delta, ltilde, nu, g_y,\
g_n_ss, tau_payroll, retire, mean_income_data,\
h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = ss_parameters
analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_parameters
# Generate initial guesses for chi^b_j and chi^n_s
chi_params = np.zeros(S + J)
chi_params[:J] = chi_b_guess
chi_params[J:] = chi_n_guess
# First run SS simulation with guesses at initial values for b, n, w, r, etc
# For inital guesses of b and n, we choose very small b, and medium n
b_guess = np.ones((S, J)).flatten() * 0.05
n_guess = np.ones((S, J)).flatten() * .4 * ltilde
# For initial guesses of w, r, T_H, and factor, we use values that are close
# to some steady state values.
wguess = 1.2
rguess = .06
T_Hguess = 0.12
factorguess = 70000.0
guesses = [wguess, rguess, T_Hguess, factorguess]
args_ = (b_guess.reshape(S, J), n_guess.reshape(S, J), chi_params[J:], chi_params[:J],
income_tax_parameters, ss_parameters, iterative_params, tau_bq, rho, lambdas, omega_SS, e)
[solutions, infodict, ier, message] = opt.fsolve(SS_fsolve, guesses, args=args_, xtol=mindist_SS, full_output=True)
[wguess, rguess, T_Hguess, factorguess] = solutions
fsolve_flag = True
solutions = SS_solver(b_guess.reshape(S, J), n_guess.reshape(S, J), wguess, rguess, T_Hguess, factorguess, chi_params[
J:], chi_params[:J], income_tax_parameters, ss_parameters, iterative_params, tau_bq, rho, lambdas, omega_SS, e, fsolve_flag)
if calibrate_model:
global Nfeval, value_all, chi_params_all
Nfeval = 1
value_all = np.zeros((10000))
chi_params_all = np.zeros((S+J,10000))
outputs = {'solutions': solutions, 'chi_params': chi_params}
ss_init_path = os.path.join(
output_dir, "Saved_moments/SS_init_solutions.pkl")
pickle.dump(outputs, open(ss_init_path, "wb"))
function_to_minimize_X = lambda x: function_to_minimize(
x, chi_params, income_tax_parameters, ss_parameters, iterative_params, omega_SS, rho, lambdas, tau_bq, e, output_dir)
bnds = tuple([(1e-6, None)] * (S + J))
# In order to scale all the parameters to estimate in the minimizer, we have the minimizer fit a vector of ones that
# will be multiplied by the chi initial guesses inside the function. Otherwise, if chi^b_j=1e5 for some j, and the
# minimizer peturbs that value by 1e-8, the % difference will be extremely small, outside of the tolerance of the
# minimizer, and it will not change that parameter.
chi_params_scalars = np.ones(S + J)
#chi_params_scalars = opt.minimize(function_to_minimize_X, chi_params_scalars,
# method='TNC', tol=MINIMIZER_TOL, bounds=bnds, callback=callbackF(chi_params_scalars), options=MINIMIZER_OPTIONS).x
# chi_params_scalars = opt.minimize(function_to_minimize, chi_params_scalars,
# args=(chi_params, income_tax_parameters, ss_parameters, iterative_params,
# omega_SS, rho, lambdas, tau_bq, e, output_dir),
# method='TNC', tol=MINIMIZER_TOL, bounds=bnds,
# callback=callbackF(chi_params_scalars,chi_params, income_tax_parameters,
# ss_parameters, iterative_params, omega_SS, rho, lambdas, tau_bq, e, output_dir),
# options=MINIMIZER_OPTIONS).x
chi_params_scalars = opt.minimize(function_to_minimize, chi_params_scalars,
args=(chi_params, income_tax_parameters, ss_parameters, iterative_params,
omega_SS, rho, lambdas, tau_bq, e, output_dir),
method='TNC', tol=MINIMIZER_TOL, bounds=bnds,
options=MINIMIZER_OPTIONS).x
chi_params *= chi_params_scalars
print 'The final scaling params', chi_params_scalars
print 'The final bequest parameter values:', chi_params
solutions_dict = pickle.load(open(ss_init_path, "rb"))
solutions = solutions_dict['solutions']
b_guess = solutions[:S * J]
n_guess = solutions[S * J:2 * S * J]
wguess, rguess, factorguess, T_Hguess = solutions[2 * S * J:]
guesses = [wguess, rguess, T_Hguess, factorguess]
args_ = (b_guess.reshape(S, J), n_guess.reshape(S, J), chi_params[J:], chi_params[:J],
income_tax_parameters, ss_parameters, iterative_params, tau_bq, rho, lambdas, omega_SS, e)
[solutions, infodict, ier, message] = opt.fsolve(SS_fsolve, guesses, args=args_, xtol=mindist_SS, full_output=True)
[wguess, rguess, T_Hguess, factorguess] = solutions
fsolve_flag = True
solutions = SS_solver(b_guess.reshape(S, J), n_guess.reshape(S, J), wguess, rguess, T_Hguess, factorguess, chi_params[
J:], chi_params[:J], income_tax_parameters, ss_parameters, iterative_params, tau_bq, rho, lambdas, omega_SS, e, fsolve_flag)
'''
------------------------------------------------------------------------
Generate the SS values of variables, including euler errors
------------------------------------------------------------------------
'''
if get_baseline:
outputs = {'solutions': solutions, 'chi_params': chi_params}
ss_init_dir = os.path.join(
output_dir, "Saved_moments/SS_baseline_solutions.pkl")
pickle.dump(outputs, open(ss_init_dir, "wb"))
else:
outputs = {'solutions': solutions, 'chi_params': chi_params}
ss_exp_dir = os.path.join(
output_dir, "Saved_moments/SS_reform_solutions.pkl")
pickle.dump(outputs, open(ss_exp_dir, "wb"))
bssmat = solutions[0:(S - 1) * J].reshape(S - 1, J)
bq = solutions[(S - 1) * J:S * J] # technically, this is just the intentional bequests - wealth of those with max age
bssmat_s = np.array(list(np.zeros(J).reshape(1, J)) + list(bssmat))
bssmat_splus1 = np.array(list(bssmat) + list(bq.reshape(1, J)))
nssmat = solutions[S * J:2 * S * J].reshape(S, J)
wss, rss, factor_ss, T_Hss = solutions[2 * S * J:]
Kss = household.get_K(bssmat_splus1, omega_SS.reshape(
S, 1), lambdas, g_n_ss, 'SS')
Lss = firm.get_L(e, nssmat, omega_SS.reshape(S, 1), lambdas, 'SS')
Yss = firm.get_Y(Kss, Lss, ss_parameters)
Iss = firm.get_I(Kss, Kss, delta, g_y, g_n_ss)
theta = np.zeros(J) #tax.replacement_rate_vals(
#nssmat, wss, factor_ss, e, J, omega_SS.reshape(S, 1), lambdas)
BQss = household.get_BQ(rss, bssmat_splus1, omega_SS.reshape(
S, 1), lambdas, rho.reshape(S, 1), g_n_ss, 'SS')
b_s = np.array(list(np.zeros(J).reshape((1, J))) + list(bssmat))
etr_params_3D = np.tile(np.reshape(etr_params,(S,1,etr_params.shape[1])),(1,J,1))
mtrx_params_3D = np.tile(np.reshape(mtrx_params,(S,1,mtrx_params.shape[1])),(1,J,1))
etr_params_extended = np.append(etr_params,np.reshape(etr_params[-1,:],(1,etr_params.shape[1])),axis=0)[1:,:]
etr_params_extended_3D = np.tile(np.reshape(etr_params_extended,(S,1,etr_params_extended.shape[1])),(1,J,1))
mtry_params_extended = np.append(mtry_params,np.reshape(mtry_params[-1,:],(1,mtry_params.shape[1])),axis=0)[1:,:]
mtry_params_extended_3D = np.tile(np.reshape(mtry_params_extended,(S,1,mtry_params_extended.shape[1])),(1,J,1))
e_extended = np.array(list(e) + list(np.zeros(J).reshape(1, J)))
nss_extended = np.array(list(nssmat) + list(np.zeros(J).reshape(1, J)))
mtry_ss = tax.MTR_capital(rss, bssmat_splus1, wss, e_extended[1:,:], nss_extended[1:,:], factor_ss,
analytical_mtrs, etr_params_extended_3D, mtry_params_extended_3D)
mtrx_ss = tax.MTR_labor(rss, bssmat_s, wss, e, nssmat, factor_ss, analytical_mtrs, etr_params_3D, mtrx_params_3D)
#np.savetxt("mtr_ss_capital.csv", mtry_ss, delimiter=",")
#np.savetxt("mtr_ss_labor.csv", mtrx_ss, delimiter=",")
taxss_params = (J,S, retire, np.tile(np.reshape(etr_params,(S,1,etr_params.shape[1])),(1,J,1)),
h_wealth, p_wealth, m_wealth, tau_payroll)
taxss = tax.total_taxes(rss, b_s, wss, e, nssmat, BQss, lambdas,
factor_ss, T_Hss, None, 'SS', False, taxss_params, theta, tau_bq)
cssmat = household.get_cons(rss, b_s, wss, e, nssmat, BQss.reshape(
1, J), lambdas.reshape(1, J), bssmat_splus1, ss_parameters, taxss)
Css = household.get_C(cssmat, omega_SS.reshape(S, 1), lambdas, 'SS')
resource_constraint = Yss - (Css + Iss)
print 'Resource Constraint Difference:', resource_constraint
constraint_params = ltilde
household.constraint_checker_SS(bssmat, nssmat, cssmat, constraint_params)
b_s = np.array(list(np.zeros(J).reshape((1, J))) + list(bssmat))
b_splus1 = bssmat_splus1
b_splus2 = np.array(list(bssmat_splus1[1:]) + list(np.zeros(J).reshape((1, J))))
chi_b = np.tile(chi_params[:J].reshape(1, J), (S, 1))
chi_n = np.array(chi_params[J:])
euler_savings = np.zeros((S, J))
euler_labor_leisure = np.zeros((S, J))
for j in xrange(J):
euler_savings[:, j] = household.euler_savings_func(wss, rss, e[:, j], nssmat[:, j], b_s[:, j], b_splus1[:, j],
b_splus2[:, j], BQss[j], factor_ss, T_Hss, chi_b[:, j], income_tax_parameters, ss_parameters,
theta[j], tau_bq[j], rho, lambdas[j])
euler_labor_leisure[:, j] = household.euler_labor_leisure_func(wss, rss, e[:, j], nssmat[:, j], b_s[:, j],
b_splus1[:, j], BQss[j], factor_ss, T_Hss, chi_n, income_tax_parameters,
ss_parameters, theta[j], tau_bq[j], lambdas[j])
'''
------------------------------------------------------------------------
Save the values in various ways, depending on the stage of
the simulation, to be used in TPI or graphing functions
------------------------------------------------------------------------
'''
# Pickle variables
output = {'Kss': Kss, 'bssmat': bssmat, 'Lss': Lss, 'Css':Css, 'nssmat': nssmat, 'Yss': Yss,
'wss': wss, 'rss': rss, 'theta': theta, 'BQss': BQss, 'factor_ss': factor_ss,
'bssmat_s': bssmat_s, 'cssmat': cssmat, 'bssmat_splus1': bssmat_splus1,
'T_Hss': T_Hss, 'euler_savings': euler_savings,
'euler_labor_leisure': euler_labor_leisure, 'chi_n': chi_n,
'chi_b': chi_b}
utils.mkdirs(os.path.join(output_dir, "SSinit"))
ss_init_dir = os.path.join(output_dir, "SSinit/ss_init_vars.pkl")
pickle.dump(output, open(ss_init_dir, "wb"))
bssmat_init = bssmat_splus1
nssmat_init = nssmat
# Pickle variables for TPI initial values
output2 = {'bssmat_init': bssmat_init, 'nssmat_init': nssmat_init}
ss_init_tpi = os.path.join(output_dir, "SSinit/ss_init_tpi_vars.pkl")
pickle.dump(output2, open(ss_init_tpi, "wb"))
return output
