def run_rolodex(input_file, output_file):
"""
This program takes an input file of personal information in multiple formats
It normalize every valid entry and dumps the ordered result into an output file
@param string input_file input file name with path from current dir
@param string output_file output file name with path from current dir
"""
persons = []
error_indices = []
normalizer = Normalizer()
with open(input_file) as input_file:
for line_number, line in enumerate(input_file, start=1):
try:
person = normalizer.normalize(line.rstrip())
persons.append(person)
except NormalizationException:
error_indices.append(line_number)
sorted_persons = sorted(persons, key=lambda person: person.__str__())
output_dict = {
"entries": sorted_persons,
"errors": error_indices
}
logging.info("Completed, please check output file.")
with open(output_file, 'w') as output_file:
json.dump(output_dict, output_file, indent=2, sort_keys=True)
def plot_shot_old(self,shot,save_fig=True,normalize=True,truth=None,prediction=None,P_thresh_opt=None,prediction_type='',extra_filename=''):
if self.normalizer is None and normalize:
if self.conf is not None:
self.saved_conf['paths']['normalizer_path'] = self.conf['paths']['normalizer_path']
nn = Normalizer(self.saved_conf)
nn.train()
self.normalizer = nn
self.normalizer.set_inference_mode(True)
if(shot.previously_saved(self.shots_dir)):
shot.restore(self.shots_dir)
t_disrupt = shot.t_disrupt
is_disruptive = shot.is_disruptive
if normalize:
self.normalizer.apply(shot)
use_signals = self.saved_conf['paths']['use_signals']
f,axarr = plt.subplots(len(use_signals)+1,1,sharex=True,figsize=(13,13))#, squeeze=False)
#plt.title(prediction_type)
#all files must agree on T_warning due to output of truth vs. normalized shot ttd.
assert(np.all(shot.ttd.flatten() == truth.flatten()))
for i,sig in enumerate(use_signals):
num_channels = sig.num_channels
ax = axarr[i]
sig_arr = shot.signals_dict[sig]
if num_channels == 1:
ax.plot(sig_arr[:,0],label = sig.description)
else:
ax.imshow(sig_arr[:,:].T, aspect='auto', label = sig.description + " (profile)")
ax.set_ylim([0,num_channels])
ax.legend(loc='best',fontsize=8)
plt.setp(ax.get_xticklabels(),visible=False)
plt.setp(ax.get_yticklabels(),fontsize=7)
f.subplots_adjust(hspace=0)
#print(sig)
#print('min: {}, max: {}'.format(np.min(sig_arr), np.max(sig_arr)))
ax = axarr[-1]
if self.pred_ttd:
ax.semilogy((-truth+0.0001),label='ground truth')
ax.plot(-prediction+0.0001,'g',label='neural net prediction')
ax.axhline(-P_thresh_opt,color='k',label='trigger threshold')
else:
ax.plot((truth+0.001),label='ground truth')
ax.plot(prediction,'g',label='neural net prediction')
ax.axhline(P_thresh_opt,color='k',label='trigger threshold')
#ax.set_ylim([1e-5,1.1e0])
ax.set_ylim([-2,2])
if len(truth)-self.T_max_warn >= 0:
ax.axvline(len(truth)-self.T_max_warn,color='r',label='min warning time')
ax.axvline(len(truth)-self.T_min_warn,color='r',label='max warning time')
ax.set_xlabel('T [ms]')
#ax.legend(loc = 'lower left',fontsize=10)
plt.setp(ax.get_yticklabels(),fontsize=7)
# ax.grid()
if save_fig:
plt.savefig('sig_fig_{}{}.png'.format(shot.number,extra_filename),bbox_inches='tight')
# np.savez('sig_{}{}.npz'.format(shot.number,extra_filename),shot=shot,T_min_warn=self.T_min_warn,T_max_warn=self.T_max_warn,prediction=prediction,truth=truth,use_signals=use_signals,P_thresh=P_thresh_opt)
plt.close()
else:
print("Shot hasn't been processed")
def save_shot(self, shot, P_thresh_opt=0, extra_filename=""):
if self.normalizer is None:
if self.conf is not None:
self.saved_conf["paths"]["normalizer_path"] = self.conf[
"paths"]["normalizer_path"]
nn = Normalizer(self.saved_conf)
nn.train()
self.normalizer = nn
self.normalizer.set_inference_mode(True)
shot.restore(self.shots_dir)
# t_disrupt = shot.t_disrupt
# is_disruptive = shot.is_disruptive
self.normalizer.apply(shot)
pred, truth, is_disr = self.get_pred_truth_disr_by_shot(shot)
use_signals = self.saved_conf["paths"]["use_signals"]
np.savez(
"sig_{}{}.npz".format(shot.number, extra_filename),
shot=shot,
T_min_warn=self.T_min_warn,
T_max_warn=self.T_max_warn,
prediction=pred,
truth=truth,
use_signals=use_signals,
P_thresh=P_thresh_opt,
)
def kmeans(request):
k = 2
global norm, champs, name_champ
norm = Normalizer()
workpath = os.path.dirname(os.path.abspath(__file__)) #Returns the Path your .py file is in
datafile = os.path.join(workpath, 'dataset/spambase.data.txt')
norm.load_csv(os.path.join(workpath, 'dataset/spambase.data.txt'))
champs = []
name_champ = []
if request.method == 'GET':
if(request.GET['nb'] == '3'):
champs.append(int(request.GET['champs1']))
champs.append(int(request.GET['champs2']))
champs.append(int(request.GET['champs3']))
name_champ.append(nomChamp[champs[0]])
name_champ.append(nomChamp[champs[1]])
name_champ.append(nomChamp[champs[2]])
elif (request.GET['nb'] == '2'):
champs.append(int(request.GET['champs1']))
champs.append(int(request.GET['champs2']))
name_champ.append(nomChamp[champs[0]])
name_champ.append(nomChamp[champs[1]])
global kMeanClusterer
kMeanClusterer = KMeanClusterer(k, datafile, champs)
kMeanClusterer.assignement()
centroids = []
clusters = []
for i in range(k):
centroids.append(kMeanClusterer.getCluster(i).getCentroid())
#centroids.append(kMeanClusterer.getCluster(i).normalizeCentroid(0.0, 1.0, len(champs)))
for i in range(k):
clusters.append(kMeanClusterer.getCluster(i).getPoints())
#clusters.append(norm.normalization(kMeanClusterer.getCluster(i).getPoints(), 0.0, 1.0, len(champs)))
splitedData = norm.get_splitedData(champs)
spams = splitedData[0]
nospams = splitedData[1]
html = render_to_string('kmeans.html', {'k': len(champs), 'centroids': centroids, 'clusters': clusters,
'spams': spams, 'no_spams': nospams, 'nomChamps': name_champ})
return HttpResponse(html)
else:
form = DocumentForm() # A empty, unbound form
return redirect('index.html', {'form': form})
def get_stock_data():
print "Loading data"
# Import the data from a CSV file coming from Yahoo Finance.
data_csv = pd.read_csv('all_stocks_5yr.csv') # Change file path as needed
# Define a function to remove any NaNs and Infs from an array
def remove_nan_inf(a):
s = a[~np.isnan(a).any(axis=1)]
t = s[~np.isinf(s).any(axis=1)]
return t
# Define a Numpy array to hold the information for the NNs.
n_features = 5
data = np.empty((data_csv.shape[0], n_features))
L = 1259 # Number of observations per subject (more info in README)
print "Loading data"
# Load the data and normalize it per subject (modify if necessary)
for i in tqdm(range(int(float(data_csv.shape[0]) / L))):
holder_array = np.empty(
(data.shape[1], L)) # Array to temporarily hold information
for info, j in zip(['open', 'close', 'high', 'low', 'volume'],
range(n_features)):
for k in range(L):
holder_array[j][k] = np.array(data_csv[info][i * L + k],
dtype=np.float)
holder_array = remove_nan_inf(holder_array.T)
scaled_info = Normalizer().fit_transform(holder_array)
for j in range(scaled_info.shape[0]):
data[i * L + j] = scaled_info[j]
# Set batch size, input shape and train/test prop.
batch_size = 1
train_test_prop = 0.8
time_step = 1
# Organize the data so that inputs are 3D and work with LSTMs.
data_train = data[:int(data.shape[0] * train_test_prop)]
data_test = data[int(data.shape[0] * train_test_prop):]
x_train = data_train[:-1].reshape(data_train[:-1].shape[0], time_step,
data_train[:-1].shape[1])
y_train = data_train[1:][:, 0]
x_test = data_test[:-1].reshape(data_test[:-1].shape[0], time_step,
data_test[:-1].shape[1])
y_test = data_test[1:][:, 0]
print "Data successfully loaded"
return (batch_size, x_train, x_test, y_train, y_test)
def train(train_env_id: str,
eval_env_id: str,
logdir: str,
cfg: ExperimentConfig,
save_path: str,
pretrain_path: Optional[str] = None) -> DDPGAgent:
pretrain = torch.load(os.path.join(pretrain_path)) \
if pretrain_path is not None \
else None
env = set_env_metadata(train_env_id, cfg)
train_env = make_vec_env(train_env_id,
num_envs=cfg.episodes_per_cycle,
no_timeout=True,
seed=cfg.seed)
eval_env = make_vec_env(eval_env_id,
num_envs=cfg.num_eval_envs,
no_timeout=True,
seed=cfg.seed + 100)
replay = HERReplayBuffer(cfg=cfg)
tf_logger = TensorboardLogger(logdir)
actor = ActorNet(obs_dim=cfg.obs_dim,
goal_dim=cfg.goal_dim,
action_dim=cfg.action_dim,
action_range=cfg.action_range,
zero_last=(pretrain_path is not None))
critic = CriticNet(obs_dim=cfg.obs_dim,
goal_dim=cfg.goal_dim,
action_dim=cfg.action_dim,
action_range=cfg.action_range)
normalizer = Normalizer(cfg.obs_dim+cfg.goal_dim) \
if pretrain is None \
else pretrain.normalizer
agent = DDPGAgent(cfg=cfg,
actor=actor,
critic=critic,
normalizer=normalizer,
reward_fn=env.compute_reward,
pretrain=getattr(pretrain, 'actor', None))
engine = DDPGEngine(cfg=cfg,
agent=agent,
train_env=train_env,
eval_env=eval_env,
replay=replay,
tf_logger=tf_logger)
engine.train()
env.close()
train_env.close()
eval_env.close()
torch.save(agent, os.path.join(save_path))
return agent
def generate(self, edgeCount, tfidf = False, window_size = 0, degree = False, closeness = False, groups= False):
parser = XMLDataframeParser()
text = parser.getText("./data/smokingRecords.xml")
parser.addFeatureFromText(text, "HISTORY OF PRESENT ILLNESS :", "", True, True, "illness")
df = parser.getDataframe()
df_xml = parser.removeEmptyEntries(df, "illness")
normalizer = Normalizer()
if tfidf:
if window_size == 0:
vectorizer = TfidfVectorizer(tokenizer = lambda text: normalizer.normalize(text, True, False), ngram_range = (2, 2))
mostFreq2Grams = self.get_first_n_words(vectorizer, df_xml.illness, edgeCount)
else:
vectorizer = TfidfVectorizer(analyzer = lambda text: self.custom_analyser(text, 2, int(window_size)))
mostFreq2Grams = self.get_first_n_words(vectorizer, normalizer.normalizeArray(df_xml.illness, True, False), edgeCount)
else:
if window_size == 0:
vectorizer = CountVectorizer(tokenizer = lambda text: normalizer.normalize(text, True, False), ngram_range = (2, 2))
mostFreq2Grams = self.get_first_n_words(vectorizer, df_xml.illness, edgeCount)
else:
vectorizer = CountVectorizer(analyzer = lambda text: self.custom_analyser(text, 2, int(window_size)))
mostFreq2Grams = self.get_first_n_words(vectorizer, normalizer.normalizeArray(df_xml.illness, True, False), edgeCount)
df_graph = self.create_dataframe(mostFreq2Grams)
GF = nx.from_pandas_edgelist(df_graph, 'Node1', 'Node2', ["Weight"])
if degree:
# calculate degree centrality
degree_centrality = nx.degree_centrality(GF)
nx.set_node_attributes(GF, degree_centrality, "degree_centrality")
if closeness:
# calculate closeness centrality
closeness_centrality = nx.closeness_centrality(GF)
nx.set_node_attributes(GF, closeness_centrality, "closeness_centrality")
if groups:
# calculate partitions
partition = community.best_partition(GF)
nx.set_node_attributes(GF, partition, "group")
payload = json_graph.node_link_data(GF)
return payload
def __init__(self, params):
print(datetime.now().strftime(params.time_format), 'Starting..')
# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # verbose off(info, warning)
random.seed(params.seed)
np.random.seed(params.seed)
tf.set_random_seed(params.seed)
print("A GPU is{} available".format(
"" if tf.test.is_gpu_available() else " NOT"))
stm_dict = dict()
stm_dict['params'] = params
FLAGS.model_dir = './biobert_ner/pretrainedBERT/'
FLAGS.bert_config_file = './biobert_ner/conf/bert_config.json'
FLAGS.vocab_file = './biobert_ner/conf/vocab.txt'
FLAGS.init_checkpoint = \
'./biobert_ner/pretrainedBERT/pubmed_pmc_470k/biobert_model.ckpt'
FLAGS.ip = params.ip
FLAGS.port = params.port
FLAGS.gnormplus_home = params.gnormplus_home
FLAGS.gnormplus_host = params.gnormplus_host
FLAGS.gnormplus_port = params.gnormplus_port
FLAGS.tmvar2_home = params.tmvar2_home
FLAGS.tmvar2_host = params.tmvar2_host
FLAGS.tmvar2_port = params.tmvar2_port
# import pprint
# pprint.PrettyPrinter().pprint(FLAGS.__flags)
stm_dict['biobert'] = BioBERT(FLAGS)
stm_dict['gnormplus_home'] = params.gnormplus_home
stm_dict['gnormplus_host'] = params.gnormplus_host
stm_dict['gnormplus_port'] = params.gnormplus_port
stm_dict['tmvar2_home'] = params.tmvar2_home
stm_dict['tmvar2_host'] = params.tmvar2_host
stm_dict['tmvar2_port'] = params.tmvar2_port
stm_dict['max_word_len'] = params.max_word_len
stm_dict['ner_model'] = params.ner_model
stm_dict['n_pmid_limit'] = params.n_pmid_limit
stm_dict['time_format'] = params.time_format
stm_dict['available_formats'] = params.available_formats
if not os.path.exists('./output'):
os.mkdir('output')
else:
# delete prev. version outputs
delete_files('./output')
#delete_files(os.path.join(params.gnormplus_home, 'input'))
#delete_files(os.path.join(params.tmvar2_home, 'input'))
#ceshi_pdf = '/home/yg/work/BioNLP/NER_RE_DB/pdf_data/z.R.scholar/data/rs993419_DNMT3B/A_new_and_a_reclassified_ICF_patient_without_mutations_in_DNMT3B_and_its_interacting_proteins_SUMO‐1_and_UBC9.pdf'
ner = NER()
ner.stm_dict = stm_dict
ner.normalizer = Normalizer()
input_db_path = '/home/yg/work/BioNLP/NER_RE_DB/pdf_data/z.R.scholar/instance/paper.sqlite'
pdf_data_path = '/home/yg/work/BioNLP/NER_RE_DB/pdf_data/z.R.scholar/data'
res = ner.run_pipeline(input_db_path, pdf_data_path)
stm_dict['biobert'].close()
return
print("任务开始")
res = ceshi_run(stm_dict)
print('以下是本次测试的结果:')
print(res)
stm_dict['biobert'].close()
def main(p):
start = time.time()
# 选择文件名以'json.gz'结尾的记录
file_name_list = filter(lambda x: x.endswith('json.gz'), os.listdir(p))
# TODO 添加文件是否是24个的判断(glob模块)
for file_name in file_name_list:
with open(os.path.join(p, file_name), 'r') as f:
raw_json_file = gzip.GzipFile(fileobj=f)
record_cleaner = Cleaner()
record_grouper = Grouper(db)
record_normalizer = Normalizer(db)
mongo_helper = MongoHelper(db)
counter = ActorCounter()
evaluater = Evaluater()
# 数据清洗
record_cleaner.set_dirty_data(raw_json_file)
record_cleaner.clean()
clean_record = record_cleaner.get_clean_data()
log.log('clean record %s' % len(clean_record))
# 数据处理
# 分组
record_grouper.set_records(clean_record)
record_grouper.group()
record_actor_exist = record_grouper.get_group_1()
record_actor_new= record_grouper.get_group_2()
log.log('record_actor_exist: %s' % len(record_actor_exist))
log.log('record_actor_new: %s' % len(record_actor_new))
# 处理记录的actor已存在的记录
log.log('Begin processing actor-exist records...')
# 只需要删掉记录的actor_attrs即可
for record in record_actor_exist:
del record['actor_attributes']
log.log('Finished.')
# 处理记录的actor不存在的记录
record_normalizer.set_records(record_actor_new)
record_normalizer.normalize()
record_actor_new = record_normalizer.get_record_actor_new()
new_actors = record_normalizer.get_new_actors()
# 把本地的今日新增的Actor更新到数据库
actors = new_actors.values()
mongo_helper.insert_new_actors(actors)
# 对新增的Actor, 改变Redis中相应的计数
counter.count_actor_list(actors)
# 计算每条记录的val
evaluater.set_records(record_actor_exist)
evaluater.evaluate()
val_actor_exist = evaluater.get_val_cache()
evaluater.set_records(record_actor_new)
evaluater.evaluate()
val_actor_new = evaluater.get_val_cache()
# 将记录插入数据库
mongo_helper.insert_new_reocrds(record_actor_new)
mongo_helper.insert_new_reocrds(record_actor_exist)
# 将今日用户新增的val更新到数据库
mongo_helper.update_val(val_actor_new)
mongo_helper.update_val(val_actor_exist)
record_cleaner.free_mem()
del record_cleaner
del record_grouper
del record_normalizer
del mongo_helper
del counter
del evaluater
# 生成CSV文件
util.grcount2csv()
end = time.time()
log.log('total: %s s' % (end - start))
def index(request):
# Handle file upload
if request.method == 'POST':
form = DocumentForm(request.POST, request.FILES)
if form.is_valid():
handle_uploaded_file(request.FILES['docfile'], request.FILES['docfile'].name)
# Redirect to the document list after POST
norm = Normalizer()
data_save = []
workpath = os.path.dirname(os.path.abspath(__file__)) #Returns the Path your .py file is in
data = norm.load_csv(os.path.join(workpath, 'dataset/'+request.FILES['docfile'].name))
for line in data:
try:
data_save.append(line)
except IndexError:
pass
'''
data_normalized = norm.normalization(data_save, 0.0, 1.0, 58)
stats = norm.statistics(data_normalized, 58)
'''
normalizedData = norm.normalization()
normSplitedData = norm.split(normalizedData)
normNospams = normSplitedData[1]
normSpams = normSplitedData[0]
stats = norm.stats(normSpams, normNospams)
spam = []
for i in range(0, 58):
line = []
line.append(i)
for j in range(0, 4):
line.append(stats[0][j][i])
spam.append(line)
no_spam = []
for i in range(0, 58):
line = []
line.append(i)
for j in range(0, 4):
line.append(stats[1][j][i])
no_spam.append(line)
new_stats = []
for i in range(0, 58):
line = []
line.append(i)
for j in range(0, 4):
line.append(stats[0][j][i])
for j in range(0, 4):
line.append(stats[1][j][i])
new_stats.append(line)
global nomChamp
nomChamp = ['word_freq_make', 'word_freq_address', 'word_freq_all',
'word_freq_3d', 'word_freq_our', 'word_freq_over', 'word_freq_remove',
'word_freq_internet', 'word_freq_order', 'word_freq_mail',
'word_freq_receive', 'word_freq_will', 'word_freq_people',
'word_freq_report', 'word_freq_addresses', 'word_freq_free',
'word_freq_business', 'word_freq_email', 'word_freq_you',
'word_freq_credit', 'word_freq_your', 'word_freq_font',
'word_freq_000', 'word_freq_money', 'word_freq_hp', 'word_freq_hpl',
'word_freq_george', 'word_freq_650', 'word_freq_lab',
'word_freq_labs', 'word_freq_telnet', 'word_freq_857',
'word_freq_data', 'word_freq_415', 'word_freq_85',
'word_freq_technology', 'word_freq_1999', 'word_freq_parts',
'word_freq_pm', 'word_freq_direct', 'word_freq_cs',
'word_freq_meeting', 'word_freq_original', 'word_freq_project',
'word_freq_re', 'word_freq_edu', 'word_freq_table',
'word_freq_conference', 'char_freq_semi', 'char_freq_lparen',
'char_freq_lbrack', 'char_freq_bang', 'char_freq_dollar',
'char_freq_hash', 'capital_run_length_average',
'capital_run_length_longest', 'capital_run_length_total',
'spam']
stats_names = []
stats_names = zip(nomChamp, new_stats)
return render(request, 'stats.html', {'data': stats_names})
else:
form = DocumentForm() # A empty, unbound form
return render(request, 'index.html', {'form': form})
def main(p):
start = time.time()
# 选择文件名以'json.gz'结尾的记录
file_name_list = filter(lambda x: x.endswith('json.gz'), os.listdir(p))
# TODO 添加文件是否是24个的判断(glob模块)
for file_name in file_name_list:
with open(os.path.join(p, file_name), 'r') as f:
raw_json_file = gzip.GzipFile(fileobj=f)
record_cleaner = Cleaner()
record_grouper = Grouper(db)
record_normalizer = Normalizer(db)
mongo_helper = MongoHelper(db)
counter = ActorCounter()
evaluater = Evaluater()
# 数据清洗
record_cleaner.set_dirty_data(raw_json_file)
record_cleaner.clean()
clean_record = record_cleaner.get_clean_data()
log.log('clean record %s' % len(clean_record))
# 数据处理
# 分组
record_grouper.set_records(clean_record)
record_grouper.group()
record_actor_exist = record_grouper.get_group_1()
record_actor_new = record_grouper.get_group_2()
log.log('record_actor_exist: %s' % len(record_actor_exist))
log.log('record_actor_new: %s' % len(record_actor_new))
# 处理记录的actor已存在的记录
log.log('Begin processing actor-exist records...')
# 只需要删掉记录的actor_attrs即可
for record in record_actor_exist:
del record['actor_attributes']
log.log('Finished.')
# 处理记录的actor不存在的记录
record_normalizer.set_records(record_actor_new)
record_normalizer.normalize()
record_actor_new = record_normalizer.get_record_actor_new()
new_actors = record_normalizer.get_new_actors()
# 把本地的今日新增的Actor更新到数据库
actors = new_actors.values()
mongo_helper.insert_new_actors(actors)
# 对新增的Actor, 改变Redis中相应的计数
counter.count_actor_list(actors)
# 计算每条记录的val
evaluater.set_records(record_actor_exist)
evaluater.evaluate()
val_actor_exist = evaluater.get_val_cache()
evaluater.set_records(record_actor_new)
evaluater.evaluate()
val_actor_new = evaluater.get_val_cache()
# 将记录插入数据库
mongo_helper.insert_new_reocrds(record_actor_new)
mongo_helper.insert_new_reocrds(record_actor_exist)
# 将今日用户新增的val更新到数据库
mongo_helper.update_val(val_actor_new)
mongo_helper.update_val(val_actor_exist)
record_cleaner.free_mem()
del record_cleaner
del record_grouper
del record_normalizer
del mongo_helper
del counter
del evaluater
# 生成CSV文件
util.grcount2csv()
end = time.time()
log.log('total: %s s' % (end - start))
def plot_shot(self,shot,save_fig=True,normalize=True,truth=None,prediction=None,P_thresh_opt=None,prediction_type='',extra_filename=''):
print('plotting shot,',shot,prediction_type,prediction.shape)
if self.normalizer is None and normalize:
if self.conf is not None:
self.saved_conf['paths']['normalizer_path'] = self.conf['paths']['normalizer_path']
nn = Normalizer(self.saved_conf)
nn.train()
self.normalizer = nn
self.normalizer.set_inference_mode(True)
if(shot.previously_saved(self.shots_dir)):
shot.restore(self.shots_dir)
if shot.signals_dict is not None: #make sure shot was saved with data
t_disrupt = shot.t_disrupt
is_disruptive = shot.is_disruptive
if normalize:
self.normalizer.apply(shot)
use_signals = self.saved_conf['paths']['use_signals']
all_signals = self.saved_conf['paths']['all_signals']
fontsize= 18
lower_lim = 0 #len(pred)
plt.close()
colors = ['b','green','red','c','m','orange','k','y']
lss = ["-","--"]
#f,axarr = plt.subplots(len(use_signals)+1,1,sharex=True,figsize=(10,15))#, squeeze=False)
f,axarr = plt.subplots(4+1,1,sharex=True,figsize=(18,18))#,squeeze=False)#, squeeze=False)
#plt.title(prediction_type)
#assert(np.all(shot.ttd.flatten() == truth.flatten()))
xx = range((prediction.shape[0]))
j=0 #list(reversed(range(len(pred))))
j1=0
p0=0
for i,sig_target in enumerate(all_signals):
if sig_target.description== 'n1 finite frequency signals':
#'Locked mode amplitude':
target_plot=shot.signals_dict[sig_target]##[:,0]
target_plot=target_plot[:,0]
print(target_plot.shape)
elif sig_target.description== 'Locked mode amplitude':
lm_plot=shot.signals_dict[sig_target]##[:,0]
lm_plot=lm_plot[:,0]
for i,sig in enumerate(use_signals):
num_channels = sig.num_channels
sig_arr = shot.signals_dict[sig]
legend=[]
if num_channels == 1:
j=i//7
ax = axarr[j]
# if j == 0:
ax.plot(xx,sig_arr[:,0],linewidth=2,color=colors[i%7],label=sig.description)#,linestyle=lss[j],color=colors[j])
# else:
# ax.plot(xx,sig_arr[:,0],linewidth=2)#,linestyle=lss[j],color=colors[j],label = labels[sig])
#if np.min(sig_arr[:,0]) < -100000:
if j==0:
ax.set_ylim([-3,15])
ax.set_yticks([0,5,10])
else:
ax.set_ylim([-15,15])
ax.set_yticks([-10,-5,0,5,10])
# ax.set_ylabel(labels[sig],size=fontsize)
ax.legend()
else:
j=-2-j1
j1+=1
ax = axarr[j]
ax.imshow(sig_arr[:,:].T, aspect='auto', label = sig.description,cmap="inferno" )
ax.set_ylim([0,num_channels])
ax.text(lower_lim+200, 45, sig.description, bbox={'facecolor': 'white', 'pad': 10},fontsize=fontsize)
ax.set_yticks([0,num_channels/2])
ax.set_yticklabels(["0","0.5"])
ax.set_ylabel("$\\rho$",size=fontsize)
ax.legend(loc="center left",labelspacing=0.1,bbox_to_anchor=(1,0.5),fontsize=fontsize,frameon=False)
# ax.axvline(len(truth)-self.T_min_warn,color='r')
# ax.axvline(p0,linestyle='--',color='darkgrey')
plt.setp(ax.get_xticklabels(),visible=False)
plt.setp(ax.get_yticklabels(),fontsize=fontsize)
f.subplots_adjust(hspace=0)
#print(sig)
#print('min: {}, max: {}'.format(np.min(sig_arr), np.max(sig_arr)))
ax = axarr[-1]
# ax.semilogy((-truth+0.0001),label='ground truth')
# ax.plot(-prediction+0.0001,'g',label='neural net prediction')
# ax.axhline(-P_thresh_opt,color='k',label='trigger threshold')
# nn = np.min(pred)
# ax.plot(xx,truth,'g',label='target',linewidth=2)
# ax.axhline(0.4,linestyle="--",color='k',label='threshold')
print('predictions shape:',prediction.shape)
print('truth shape:',truth.shape)
# prediction=prediction[:,0]
prediction=prediction#-1.5
prediction[prediction<0]=0.0
minii,maxii= np.amin(prediction),np.amax(prediction)
lm_plot_max=np.amax(lm_plot)
lm_plot=lm_plot/lm_plot_max*maxii
truth_plot_max=np.amax(truth[:,1])
truth_plot=truth[:,1]/truth_plot_max*maxii
print('******************************************************')
print('******************************************************')
print('******************************************************')
print('Truth_plot',truth_plot[:-10])
print('lm_plot',lm_plot[:-10])
print('******************************************************')
target_plot_max=np.amax(target_plot)
target_plot=target_plot/target_plot_max*maxii
ax.plot(xx,truth_plot,'yellow',label='truth')
ax.plot(xx,lm_plot,'pink',label='Locked mode amplitude')
ax.plot(xx,target_plot,'cyan',label='n1rms')
#ax.plot(xx,truth,'pink',label='target')
ax.plot(xx,prediction[:,0],'blue',label='FRNN-U predicted n=1 mode ',linewidth=2)
ax.plot(xx,prediction[:,1],'red',label='FRNN-U predicted locked mode ',linewidth=2)
#ax.axhline(P_thresh_opt,linestyle="--",color='k',label='threshold',zorder=2)
#ax.axvline(p0,linestyle='--',color='darkgrey')
# ax.set_ylim([np.amin(prediction,truth),np.amax(prediction,truth)])
ax.set_ylim([0,maxii])
print('predictions:',shot.number,prediction)
# ax.set_ylim([np.min([prediction,target_plot,lm_plot]),np.max([prediction,target_plot,lm_plot])])
#ax.set_yticks([0,1])
#if p0>0:
# ax.scatter(xx[k],p,s=300,marker='*',color='r',zorder=3)
# if len(truth)-T_max_warn >= 0:
# ax.axvline(len(truth)-T_max_warn,color='r')#,label='max warning time')
# ax.axvline(len(truth)-self.T_min_warn,color='r',linewidth=0.5)#,label='min warning time')
ax.set_xlabel('T [ms]',size=fontsize)
# ax.axvline(2400)
ax.legend(loc="center left",labelspacing=0.1,bbox_to_anchor=(1,0.5),fontsize=fontsize+2,frameon=False)
plt.setp(ax.get_yticklabels(),fontsize=fontsize)
plt.setp(ax.get_xticklabels(),fontsize=fontsize)
# plt.xlim(0,200)
plt.xlim([lower_lim,len(truth)])
# plt.savefig("{}.png".format(num),dpi=200,bbox_inches="tight")
if save_fig:
plt.savefig('sig_fig_{}{}.png'.format(shot.number,extra_filename),bbox_inches='tight')
#np.savez('sig_{}{}.npz'.format(shot.number,extra_filename),shot=shot,T_min_warn=self.T_min_warn,T_max_warn=self.T_max_warn,prediction=prediction,truth=truth,use_signals=use_signals,P_thresh=P_thresh_opt)
#plt.show()
else:
print("Shot hasn't been processed")
def _normalize(response):
callback(
Normalizer(self, data_type, response,
**self._normalizer_options).normalize())
from normalize import Normalizer from recognize import LetterRecognizer from model import LetterClassifier # creating the recognizer # it uses `tesserocr` to provide us with very rough letter boxes # because those boxes are pretty rough, we have to normalize them first # by rough prediction of letter box I mean a box with 2 or more letters in it(or maybe none at all) rec = LetterRecognizer(image_path='./res/table_real.jpg') letters_raw = rec.find_letters() # creating normalizer(cuts all the letters into the small images and pads them to the same size) normalizer = Normalizer(letter_sequence=letters_raw) letters_norm = normalizer.normalized_letters() # creating SGD classifier model classifier = LetterClassifier(gamma=1e-4) classifier.load(dump_filename='model.dmp') # resulting in labels labels = classifier.predict(letters_norm)
self.convert_cont_spaces,
self.strip
]
_text = text
for func in funcs:
_text = func(_text)
return _text
if __name__ == '__main__':
import sys
from normalize import Normalizer
fd = open(sys.argv[1]) if len(sys.argv) >= 2 else sys.stdin
ps = JaWikiPreprocess()
norm = Normalizer()
for _line in (_.strip() for _ in fd):
for line in _line.split("。"):
try:
conv = ps.execute(line)
if conv:
print(norm.normalize(conv + "。"))
except KeyboardInterrupt:
exit()
except:
traceback.print_exc()
# for file in jawiki-latest*.txt; do python
# ~/Projects/cabocha/jawiki_preprocess.py $file >preprocess/$file.pre.txt;
# done
def build_dataset(slide_dir,
output_dir,
projects,
background=0.2,
size=255,
reject_rate=0.1,
ignore_repeat=False):
proceed = None
train_path = os.path.join(output_dir, "train.h5")
val_path = os.path.join(output_dir, "val.h5")
test_path = os.path.join(output_dir, "test.h5")
if (os.path.isfile(train_path) and os.path.isfile(val_path)
and os.path.isfile(test_path)):
while not (proceed == "C" or proceed == "A" or proceed == "R"
or proceed == "Q"):
print(
"""A dataset already exists in this directory. Do you want to \n
- Continue to build the datset [C] \n
- Reset the dataset [R] \n
- Quit [Q]
""")
proceed = input().upper()
if proceed == "R":
os.remove(train_path)
os.remove(val_path)
os.remove(test_path)
if proceed == "C":
train_data = load_set_data(train_path)
val_data = load_set_data(val_path)
test_data = load_set_data(test_path)
train_h5 = h5py.File(train_path, 'a')
val_h5 = h5py.File(val_path, 'a')
test_h5 = h5py.File(test_path, 'a')
elif proceed == "R" or proceed == None:
if projects is None:
raise ValueError("Missing list of projects to download.")
data = get_projects_info(projects)
train_h5 = h5py.File(train_path, 'a')
val_h5 = h5py.File(val_path, 'a')
test_h5 = h5py.File(test_path, 'a')
all_cases = list(data['case to images'].keys())
shuffle(all_cases)
#split to train and val+test
train_len = int(0.8 * len(all_cases))
train_set = all_cases[:train_len]
all_cases = all_cases[train_len:]
#split val+test into val and test
val_len = int(0.5 * len(all_cases))
val_set = all_cases[:val_len]
test_set = all_cases[val_len:]
train_data = split_to_sets(train_set, data, train_path)
val_data = split_to_sets(val_set, data, val_path)
test_data = split_to_sets(test_set, data, test_path)
if proceed != "Q":
dataset = [(list(train_data["image to sample"].keys()), train_h5),
(list(val_data["image to sample"].keys()), val_h5),
(list(test_data["image to sample"].keys()), test_h5)]
# train_images = ["TCGA-44-7671-01A-01-BS1.914604a2-de9c-404d-9fa5-23fbd0b76da3.svs"]
# val_images = ["TCGA-FF-8041-01A-01-TS1.b8b69ce3-a325-4864-a5b0-43c450347bc9.svs"]
# test_images = ["TCGA-G8-6326-01A-01-TS1.e0eb24da-6293-4ecb-8345-b70149c84d1e.svs"]
# # # train_images = []
# val_images = []
# test_images = []
# dataset = [
# (train_images, train_h5),
# (val_images, val_h5),
# (test_images, test_h5)
# ]
normalizer = Normalizer()
for images, h5_file in dataset:
image_h5_file = h5_file.require_group("images")
for filename in images:
if proceed != "C" or ".".join(
filename.split(".")[:-1]) not in image_h5_file:
download_image(filename, slide_dir)
Tile(slide_loc=os.path.join(slide_dir, filename),
set_hdf5_file=image_h5_file,
normalizer=normalizer,
background=background,
size=size,
reject_rate=reject_rate,
ignore_repeat=ignore_repeat)
h5_file.close()
normalizer.normalize_dir(output_dir)
def run_pipeline():
#get training data
training_data = pd.read_csv('worldbank-data/WDI_Data.csv')
training_data.set_index(['Country Name', 'Indicator Name'], inplace=True)
#convert to panel
panel = training_data.to_panel()
panel.drop(['Indicator Code', 'Country Code'], axis=0, inplace=True)
panel = panel.swapaxes(0, 1)
indicators_to_use = [
'Agriculture, value added (% of GDP)',
'Industry, value added (% of GDP)',
'Services, etc., value added (% of GDP)',
'Domestic credit provided by financial sector (% of GDP)',
'GDP growth (annual %)', 'GDP (current US$)', 'Expense (% of GDP)',
'Inflation, consumer prices (annual %)',
'Inflation, GDP deflator (annual %)',
'Total debt service (% of exports of goods, services and primary income)',
'Current account balance (BoP, current US$)',
'External balance on goods and services (% of GDP)',
'Health expenditure, total (% of GDP)', 'Tax revenue (% of GDP)',
'Gross capital formation (% of GDP)', 'Gross savings (% of GDP)',
'Net investment in nonfinancial assets (% of GDP)',
'Bank capital to assets ratio (%)',
'Bank nonperforming loans to total gross loans (%)',
'Broad money (% of GDP)',
'Commercial bank branches (per 100,000 adults)',
'Deposit interest rate (%)', 'Real interest rate (%)',
'Risk premium on lending (lending rate minus treasury bill rate, %)',
'Total reserves (includes gold, current US$)',
'Unemployment, total (% of total labor force) (modeled ILO estimate)',
'Interest rate spread (lending rate minus deposit rate, %)'
]
print len(indicators_to_use), 'indicators used'
panel = panel[:, :, indicators_to_use]
target_variables = [
'Agriculture, value added (% of GDP)',
'Industry, value added (% of GDP)',
'Services, etc., value added (% of GDP)', 'GDP growth (annual %)',
'Inflation, GDP deflator (annual %)',
'Gross capital formation (% of GDP)', 'Gross savings (% of GDP)',
'Bank capital to assets ratio (%)',
'Bank nonperforming loans to total gross loans (%)',
'Deposit interest rate (%)', 'Real interest rate (%)',
'Risk premium on lending (lending rate minus treasury bill rate, %)',
'Unemployment, total (% of total labor force) (modeled ILO estimate)',
'Interest rate spread (lending rate minus deposit rate, %)'
]
#drop useless countries such as samoa, lesoto and so on.
useful_countries = []
for country in panel.axes[0]:
if find_null_percentage(panel[country, :, :]) < 0.7:
useful_countries.append(country)
panel = panel.ix[useful_countries, :, :]
normalizer = Normalizer(panel)
normalized_panel = normalizer.normalize(panel)
# #visualize normalization:
# for indicator in normalized_panel.axes[2]:
# plot_hist(indicator, [panel, normalized_panel])
# select train data
years_to_validate = 1
years_to_predict = 10
years_train = generate_year_list(stop=2016 - years_to_validate)
years_val = generate_year_list(start=2016 - years_to_validate + 1)
years_predict = generate_year_list(start=2017,
stop=2016 + years_to_predict)
train_panel = normalized_panel[:, years_train, :].copy()
# fill missing values:
# either banal mean or median filling
# or sampling with a generative bidirectional LSTM - see https://arxiv.org/abs/1306.1091
generative_model = dense_generative_model(train_panel,
hidden_layers=[120],
epochs=100)
sampled_filled_values = iterative_fill(generative_model,
train_panel,
normalizer,
iterations=50,
burn_in=10)
train_panel.update(sampled_filled_values, overwrite=False)
# or
# train_panel.fillna(0, inplace=True)
# or
# train_panel = iterative_fill_bLSTM(train_panel)
# or
# filled_panel = fill_missing_bLSTM(train_panel, epochs=100)
# train_panel.update(filled_panel, overwrite=False)
# or
# interpolate(train_panel)
# create 1-step-ahead model
epochs = 200
hl = [100, 100]
print "ARCHITECTURE:", hl
print 'EPOCHS:', epochs
X_train = train_panel[:, years_train, :][:, :-1, :]
y_train = train_panel[:, years_train, :][:, 1:, :]
model = dense_gradient_model(X_train,
y_train,
hidden_layers=hl,
d=0.2,
patience=50,
epochs=epochs)
# finally, predict
for start, year in enumerate(years_val + years_predict):
predictions = model.predict(train_panel[:,
start + 1:, :].values)[:,
-1, :]
train_panel = train_panel.swapaxes(0, 1)
new_year_df = pd.DataFrame(data=predictions,
index=train_panel.axes[1],
columns=y_train.axes[2])
train_panel[year] = new_year_df
train_panel = train_panel.swapaxes(0, 1)
print "score:", rmse(
normalized_panel[:, years_val, target_variables].values,
train_panel[:, years_val, target_variables].values)
#revert to original scale and distributions
train_panel = normalizer.renormalize(train_panel)
#convert to dataframe, and write relevant information to file
target_countries = ['Bulgaria', 'Cyprus', 'Albania']
train_panel = train_panel.swapaxes(0, 1)
df = train_panel[:, target_countries,
target_variables].to_frame(filter_observations=False)
df.to_csv('Predictions.csv')
def __init__(self, params):
print(datetime.now().strftime(params.time_format), 'Starting..')
# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # verbose off(info, warning)
random.seed(params.seed)
np.random.seed(params.seed)
tf.set_random_seed(params.seed)
print("A GPU is{} available".format(
"" if tf.test.is_gpu_available() else " NOT"))
stm_dict = dict()
stm_dict['params'] = params
FLAGS.model_dir = './biobert_ner/pretrainedBERT/'
FLAGS.bert_config_file = './biobert_ner/conf/bert_config.json'
FLAGS.vocab_file = './biobert_ner/conf/vocab.txt'
FLAGS.init_checkpoint = \
'./biobert_ner/pretrainedBERT/pubmed_pmc_470k/biobert_model.ckpt'
FLAGS.ip = params.ip
FLAGS.port = params.port
FLAGS.gnormplus_home = params.gnormplus_home
FLAGS.gnormplus_host = params.gnormplus_host
FLAGS.gnormplus_port = params.gnormplus_port
FLAGS.tmvar2_home = params.tmvar2_home
FLAGS.tmvar2_host = params.tmvar2_host
FLAGS.tmvar2_port = params.tmvar2_port
# import pprint
# pprint.PrettyPrinter().pprint(FLAGS.__flags)
stm_dict['biobert'] = BioBERT(FLAGS)
stm_dict['gnormplus_home'] = params.gnormplus_home
stm_dict['gnormplus_host'] = params.gnormplus_host
stm_dict['gnormplus_port'] = params.gnormplus_port
stm_dict['tmvar2_home'] = params.tmvar2_home
stm_dict['tmvar2_host'] = params.tmvar2_host
stm_dict['tmvar2_port'] = params.tmvar2_port
stm_dict['max_word_len'] = params.max_word_len
stm_dict['ner_model'] = params.ner_model
stm_dict['n_pmid_limit'] = params.n_pmid_limit
stm_dict['time_format'] = params.time_format
stm_dict['available_formats'] = params.available_formats
if not os.path.exists('./output'):
os.mkdir('output')
else:
# delete prev. version outputs
delete_files('./output')
delete_files(os.path.join(params.gnormplus_home, 'input'))
delete_files(os.path.join(params.tmvar2_home, 'input'))
print(datetime.now().strftime(params.time_format),
'Starting server at http://{}:{}'.format(params.ip, params.port))
# https://stackoverflow.com/a/18445168
GetHandler.stm_dict = stm_dict
GetHandler.normalizer = Normalizer()
# https://docs.python.org/3.6/library/socketserver.html#asynchronous-mixins
# https://stackoverflow.com/a/14089457
server = ThreadedHTTPServer((params.ip, params.port), GetHandler)
server.serve_forever()
custom_path = None
if only_predict:
custom_path = sys.argv[1]
print("predicting using path {}".format(custom_path))
#####################################################
# PREPROCESSING #
#####################################################
# TODO(KGF): check tuple unpack
(shot_list_train, shot_list_validate, shot_list_test) = guarantee_preprocessed(conf)
#####################################################
# NORMALIZATION #
#####################################################
nn = Normalizer(conf)
nn.train()
loader = Loader(conf, nn)
print("...done")
print(
"Training on {} shots, testing on {} shots".format(
len(shot_list_train), len(shot_list_test)
)
)
#####################################################
# TRAINING #
#####################################################
# train(conf,shot_list_train,loader)
if not only_predict:
def plot_shot(
self,
shot,
save_fig=True,
normalize=True,
truth=None,
prediction=None,
P_thresh_opt=None,
prediction_type="",
extra_filename="",
):
if self.normalizer is None and normalize:
if self.conf is not None:
self.saved_conf["paths"]["normalizer_path"] = self.conf[
"paths"]["normalizer_path"]
nn = Normalizer(self.saved_conf)
nn.train()
self.normalizer = nn
self.normalizer.set_inference_mode(True)
if shot.previously_saved(self.shots_dir):
shot.restore(self.shots_dir)
if shot.signals_dict is not None: # make sure shot was saved with data
# t_disrupt = shot.t_disrupt
# is_disruptive = shot.is_disruptive
if normalize:
self.normalizer.apply(shot)
use_signals = self.saved_conf["paths"]["use_signals"]
fontsize = 18
lower_lim = 0 # len(pred)
plt.close()
colors = ["b", "green", "red", "c", "m", "orange", "k", "y"]
# lss = ["-", "--"]
f, axarr = plt.subplots(
4 + 1, 1, sharex=True,
figsize=(18, 15)) # ,squeeze=False)#, squeeze=False)
plt.title(prediction_type)
assert np.all(shot.ttd.flatten() == truth.flatten())
xx = range(len(prediction))
j = 0 # list(reversed(range(len(pred))))
j1 = 0
p0 = 0
for k, p in enumerate(prediction):
if p > P_thresh_opt:
p0 = k
break
for i, sig in enumerate(use_signals):
num_channels = sig.num_channels
sig_arr = shot.signals_dict[sig]
# legend = []
if num_channels == 1:
j = i // 7
ax = axarr[j]
# if j == 0:
ax.plot(
xx,
sig_arr[:, 0],
linewidth=2,
color=colors[i % 7],
label=sig.description,
) # ,linestyle=lss[j],color=colors[j])
if np.min(sig_arr[:, 0]) < -100000:
ax.set_ylim([-6, 6])
ax.set_yticks([-5, 0, 5])
else:
ax.set_ylim([-2, 11])
ax.set_yticks([0, 5, 10])
# ax.set_ylabel(labels[sig],size=fontsize)
ax.legend()
else:
j = -2 - j1
j1 += 1
ax = axarr[j]
ax.imshow(
sig_arr[:, :].T,
aspect="auto",
label=sig.description,
cmap="inferno",
)
ax.set_ylim([0, num_channels])
ax.text(
lower_lim + 200,
45,
sig.description,
bbox={
"facecolor": "white",
"pad": 10
},
fontsize=fontsize,
)
ax.set_yticks([0, num_channels / 2])
ax.set_yticklabels(["0", "0.5"])
ax.set_ylabel("$\\rho$", size=fontsize)
ax.legend(
loc="center left",
labelspacing=0.1,
bbox_to_anchor=(1, 0.5),
fontsize=fontsize,
frameon=False,
)
ax.axvline(len(truth) - self.T_min_warn, color="r")
ax.axvline(p0, linestyle="--", color="darkgrey")
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), fontsize=fontsize)
f.subplots_adjust(hspace=0)
# print(sig)
# print('min: {}, max: {}'.format(np.min(sig_arr), np.max(sig_arr)))
ax = axarr[-1]
# ax.semilogy((-truth+0.0001),label='ground truth')
# ax.plot(-prediction+0.0001,'g',label='neural net prediction')
# ax.axhline(-P_thresh_opt,color='k',label='trigger threshold')
# nn = np.min(pred)
# ax.plot(xx,truth,'g',label='target',linewidth=2)
# ax.axhline(0.4,linestyle="--",color='k',label='threshold')
ax.plot(
xx,
prediction,
"b",
label="RNN output--Disruption score",
linewidth=2,
zorder=1,
)
ax.axhline(P_thresh_opt,
linestyle="--",
color="k",
label="threshold",
zorder=2)
ax.axvline(p0, linestyle="--", color="darkgrey")
ax.set_ylim([min(prediction), max(prediction)])
ax.set_yticks([0, 1])
if p0 > 0:
ax.scatter(xx[k],
p,
s=300,
marker="*",
color="r",
zorder=3)
ax.axvline(len(truth) - self.T_min_warn,
color="r",
linewidth=0.5) # ,label='min warning time')
ax.set_xlabel("T [ms]", size=fontsize)
# ax.axvline(2400)
ax.legend(
loc="center left",
labelspacing=0.1,
bbox_to_anchor=(1, 0.5),
fontsize=fontsize + 2,
frameon=False,
)
plt.setp(ax.get_yticklabels(), fontsize=fontsize)
plt.setp(ax.get_xticklabels(), fontsize=fontsize)
# plt.xlim(0,200)
plt.xlim([lower_lim, len(truth)])
# plt.savefig("{}.png".format(num),dpi=200,bbox_inches="tight")
if save_fig:
plt.savefig(
"sig_fig_{}{}.png".format(shot.number, extra_filename),
bbox_inches="tight",
)
np.savez(
"sig_{}{}.npz".format(shot.number, extra_filename),
shot=shot,
T_min_warn=self.T_min_warn,
T_max_warn=self.T_max_warn,
prediction=prediction,
truth=truth,
use_signals=use_signals,
P_thresh=P_thresh_opt,
)
# plt.show()
else:
print("Shot hasn't been processed")
def plot_shot(
self,
shot,
save_fig=True,
normalize=True,
truth=None,
prediction=None,
P_thresh_opt=None,
prediction_type="",
extra_filename="",
):
if self.normalizer is None and normalize:
if self.conf is not None:
self.saved_conf["paths"]["normalizer_path"] = self.conf[
"paths"]["normalizer_path"]
nn = Normalizer(self.saved_conf)
nn.train()
self.normalizer = nn
self.normalizer.set_inference_mode(True)
if shot.previously_saved(self.shots_dir):
shot.restore(self.shots_dir)
if shot.signals_dict is not None:
# make sure shot was saved with data
# t_disrupt = shot.t_disrupt
# is_disruptive = shot.is_disruptive
if normalize:
self.normalizer.apply(shot)
use_signals = self.saved_conf["paths"]["use_signals"]
fontsize = 15
lower_lim = 0 # len(pred)
plt.close()
# colors = ["b", "k"]
# lss = ["-", "--"]
f, axarr = plt.subplots(len(use_signals) + 1,
1,
sharex=True,
figsize=(10, 15))
plt.title(prediction_type)
assert np.all(shot.ttd.flatten() == truth.flatten())
xx = range(len(prediction)) # list(reversed(range(len(pred))))
for i, sig in enumerate(use_signals):
ax = axarr[i]
num_channels = sig.num_channels
sig_arr = shot.signals_dict[sig]
if num_channels == 1:
ax.plot(xx, sig_arr[:, 0], linewidth=2)
ax.plot([], linestyle="none", label=sig.description)
if np.min(sig_arr[:, 0]) < 0:
ax.set_ylim([-6, 6])
ax.set_yticks([-5, 0, 5])
ax.plot([], linestyle="none", label=sig.description)
if np.min(sig_arr[:, 0]) < 0:
ax.set_ylim([-6, 6])
ax.set_yticks([-5, 0, 5])
else:
ax.set_ylim([0, 8])
ax.set_yticks([0, 5])
else:
ax.imshow(
sig_arr[:, :].T,
aspect="auto",
label=sig.description,
cmap="inferno",
)
ax.set_ylim([0, num_channels])
ax.text(
lower_lim + 200,
45,
sig.description,
bbox={
"facecolor": "white",
"pad": 10
},
fontsize=fontsize - 5,
)
ax.set_yticks([0, num_channels / 2])
ax.set_yticklabels(["0", "0.5"])
ax.set_ylabel("$\\rho$", size=fontsize)
ax.legend(loc="best",
labelspacing=0.1,
fontsize=fontsize,
frameon=False)
ax.axvline(len(truth) - self.T_min_warn,
color="r",
linewidth=0.5)
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), fontsize=fontsize)
f.subplots_adjust(hspace=0)
ax = axarr[-1]
# ax.semilogy((-truth+0.0001),label='ground truth')
# ax.plot(-prediction+0.0001,'g',label='neural net prediction')
# ax.axhline(-P_thresh_opt,color='k',label='trigger threshold')
# nn = np.min(pred)
ax.plot(xx, truth, "g", label="target", linewidth=2)
# ax.axhline(0.4,linestyle="--",color='k',label='threshold')
ax.plot(xx, prediction, "b", label="RNN output", linewidth=2)
ax.axhline(P_thresh_opt,
linestyle="--",
color="k",
label="threshold")
ax.set_ylim([-2, 2])
ax.set_yticks([-1, 0, 1])
# if len(truth)-T_max_warn >= 0:
# ax.axvline(len(truth)-T_max_warn,color='r')#,label='max
# warning time')
# ,label='min warning time')
ax.axvline(len(truth) - self.T_min_warn,
color="r",
linewidth=0.5)
ax.set_xlabel("T [ms]", size=fontsize)
# ax.axvline(2400)
ax.legend(
loc=(0.5, 0.7),
fontsize=fontsize - 5,
labelspacing=0.1,
frameon=False,
)
plt.setp(ax.get_yticklabels(), fontsize=fontsize)
plt.setp(ax.get_xticklabels(), fontsize=fontsize)
# plt.xlim(0,200)
plt.xlim([lower_lim, len(truth)])
# plt.savefig("{}.png".format(num),dpi=200,bbox_inches="tight")
if save_fig:
plt.savefig("sig_fig_{}{}.png".format(
shot.number, extra_filename)),
# bbox_inches='tight')
np.savez(
"sig_{}{}.npz".format(shot.number, extra_filename),
shot=shot,
T_min_warn=self.T_min_warn,
T_max_warn=self.T_max_warn,
prediction=prediction,
truth=truth,
use_signals=use_signals,
P_thresh=P_thresh_opt,
)
# plt.show()
else:
print("Shot hasn't been processed")
