def compile_file(f, name, c):
base, ext = os.path.splitext(name)
print 'read...'
r = lisp_reader.reader(f)
exp = r.read_all()
if c.verbose:
print '--- read ---'
pp(exp)
print 'transform...'
t = transform.transformer(c)
exp2 = t.go(exp)
if c.verbose:
print '--- transform ---'
pp(exp2)
w = nodes.walker(c)
exp3 = w.go(exp2)
print 'rename...'
# alpha conversion
c.var_dict = nodes.rename_variables(exp3, c)
# find strongly connected components
print 'call graph...'
c.dep_graph = graph.build_dependency_graph(exp3)
c.scc_graph, c.scc_map = graph.strongly(c.dep_graph)
a = analyze.analyzer(c)
exp4 = a.analyze(exp3)
if c.verbose:
print '--- analyzer ---'
exp4.pprint()
ic = byte_cps(c, verbose=c.verbose)
exp5 = ic.go(exp4)
if c.verbose:
print '--- cps ---'
cps.pretty_print(exp5)
fo = open('%s.byc' % base, 'wb')
num_regs = cps.the_register_allocator.max_reg
b = compiler(fo, name, num_regs, c)
b.go(exp5)
fo.close()
def compile_file (f, name, c):
base, ext = os.path.splitext (name)
print 'read...'
r = lisp_reader.reader (f)
exp = r.read_all()
if c.verbose:
print '--- read ---'
pp (exp)
print 'transform...'
t = transform.transformer (c)
exp2 = t.go (exp)
if c.verbose:
print '--- transform ---'
pp (exp2)
w = nodes.walker (c)
exp3 = w.go (exp2)
print 'rename...'
# alpha conversion
c.var_dict = nodes.rename_variables (exp3, c)
# find strongly connected components
print 'call graph...'
c.dep_graph = graph.build_dependency_graph (exp3)
c.scc_graph, c.scc_map = graph.strongly (c.dep_graph)
a = analyze.analyzer (c)
exp4 = a.analyze (exp3)
if c.verbose:
print '--- analyzer ---'
exp4.pprint()
ic = byte_cps (c, verbose=c.verbose)
exp5 = ic.go (exp4)
if c.verbose:
print '--- cps ---'
cps.pretty_print (exp5)
fo = open ('%s.byc' % base, 'wb')
num_regs = cps.the_register_allocator.max_reg
b = compiler (fo, name, num_regs, c)
b.go (exp5)
fo.close()
# save the data
pickle.dump(data,open(args.output_file_name,"w"))
if args.do_analysis:
# get and configure the model
import models
model = getattr(models,model_name)(config=model_config)
# iterate over rule data and analyze point-by-point
saved_data_list = []
data = pickle.load(open(args.output_file_name))
for rule, rule_data in data.items():
logr.info(u"analyzing rule: {}".format(rule))
plotable_data = analyzer(rule_data,model,logr)
# remove spaces in rule name
rule_name = rule.replace(" ","-")[0:100]
if args.do_plot:
return_val = plotter(plotable_data,rule_name)
# the plotter returns -1 if the counts data don't exist
if return_val == -1:
continue
# save data
if plotable_data != []:
saved_data_list.append((rule_name,plotable_data))
def max_last_field_getter(tup):
""" a function that acts on tuples like:
def analyze(self):
# a = analyzer('BOW', 800, 600)
a = analyzer('SIFT', 800, 600)
a.createRawP()
a.processRaw()
self.delete()
parser = argparse.ArgumentParser()
parser.add_argument("-i",dest="input_file_name",default="output.pkl")
parser.add_argument("-c",dest="config_file_name",default=None,help="get configuration from this file")
parser.add_argument("-t",dest="plot_title",default=None)
args = parser.parse_args()
plot_config = {}
if args.config_file_name is not None:
config = ConfigParser.SafeConfigParser()
config.read(args.config_file_name)
model_name = config.get("analyze","model_name")
model_config = dict(config.items(model_name + "_model"))
if config.has_section("plot"):
plot_config = dict(config.items("plot"))
else:
plot_config["plot_title"] = "output"
plot_config["plot_dir"] = "."
else:
model_config = {"alpha":0.99,"mode":"lc"}
model_name = "Poisson"
plot_config["plot_title"] = "output"
plot_config["plot_dir"] = "."
if args.plot_title is not None:
plot_config["plot_title"] = args.plot_title
model = getattr(models,model_name)(config=model_config)
generator = pickle.load(open(args.input_file_name))
plotable_data = analyzer(generator,model,logr)
plot(plotable_data,plot_config)
dest="config_file_name",
default=None,
help="get configuration from this file")
parser.add_argument("-t", dest="plot_title", default=None)
args = parser.parse_args()
plot_config = {}
if args.config_file_name is not None:
config = ConfigParser.SafeConfigParser()
config.read(args.config_file_name)
model_name = config.get("analyze", "model_name")
model_config = dict(config.items(model_name + "_model"))
if config.has_section("plot"):
plot_config = dict(config.items("plot"))
else:
plot_config["plot_title"] = "output"
plot_config["plot_dir"] = "."
else:
model_config = {"alpha": 0.99, "mode": "lc"}
model_name = "Poisson"
plot_config["plot_title"] = "output"
plot_config["plot_dir"] = "."
if args.plot_title is not None:
plot_config["plot_title"] = args.plot_title
model = getattr(models, model_name)(config=model_config)
generator = pickle.load(open(args.input_file_name))
plotable_data = analyzer(generator, model, logr)
plot(plotable_data, plot_config)
def my_handler(event, context):
# checks each message posted in the group to see if it contains
# the name of the bot. if it does, it will then do a few more
# string comparisons to know which function to call
# check that it's not posted by itself to avoid infinite loops
if "botpost" not in event['source_guid']:
# if event['sender_id'] != "45530059":
# putting the input text to lowercase for better matches
input_text = event['text'].lower()
# handle attachment URLs for images
# the value will default to 'null' if a URL attachment is not found
# true JSON path to what we want: [event][attachments][url]
if event['attachments']:
for url in event['attachments']:
input_attachment = url.get('url', 'null')
else:
input_attachment = 'null'
# vision & photoboi combo handler
if ("@photo" in input_text) and ("@analyze" in input_text):
# gets the image URL
url_val = photoboi.photoboi()
# passes the value to the vision API & gets back JSON
json_content = analyze.visionAPI(url_val)
# parses the JSON into a usable format
list_a, list_b, list_c, list_d, list_e, list_f = analyze.parse_vision_JSON(json_content)
# formats the message text into a block of text
msg_val = analyze.vision_list_handler(list_a, list_b, list_c, list_d, list_e, list_f)
# concatenate the pic URL and the vision text
url_plus_vision_text = url_val + "\n\n" + msg_val
post_text(url_plus_vision_text)
# photoboi handler
elif "@photo" in input_text:
# gets the image URL and posts it
pic_url = photoboi.photoboi()
post_text(pic_url)
# analyze handler
elif "@analyze" in input_text:
# send both vals bc the pic could be either place
url_val, success = analyze.analyzer(input_attachment, input_text)
# if it found a valid URL, proceed
if success == True:
json_content = analyze.visionAPI(url_val)
list_a, list_b, list_c, list_d, list_e, list_f = analyze.parse_vision_JSON(json_content)
msg_val = analyze.vision_list_handler(list_a, list_b, list_c, list_d, list_e, list_f)
post_text(msg_val)
else:
post_text(url_val)
# videoboi handler
elif "@video" in input_text:
# gets the video URL and posts it
vid_url = videoboi.videoboi()
post_text(vid_url)
# odds handler
elif "@odds" in input_text:
# generate random number and post it
rand_num = random.randint(0, 100)
post_text(str(rand_num) + "%")
elif "@crypto" in input_text:
crypto_price = crypto.get_crypto(input_text)
post_text(crypto_price)
elif "@news" in input_text:
news = news.get_news(input_text)
post_text(news)
elif "@help" in input_text:
intro = "I recognize the following:\n"
options = """@photo will grab a random photo from the history
\n@video will grab a random vid from the history
\n@analyze (attach pic or paste URL)
\n@odds
\n@crypto (+ help for more info) along with the coin name
\n@news
\n$[stock_name]
\n@help"""
msg = intro + options
post_text(msg)
elif "$" in input_text:
# create a local file with the PNG chart
local_img_path = stonks.get_stock(input_text)
# send the file to the groupme image upload service
img_url = post_image(local_img_path)
# post the message to the chat and print the status code
print(post_img_attachment(img_url))
elif "@pandemic" in input_text:
# initiate boot sequence
print("## INPUT DATA")
print(event)
print(booter.boot_sequence(event))
def analyze(self):
# a = analyzer('BOW', 800, 600)
a = analyzer('SIFT', 800, 600)
a.createRawP()
a.processRaw()
self.delete()
# save the data
pickle.dump(data, open(args.output_file_name, "w"))
if args.do_analysis:
# get and configure the model
import models
model = getattr(models, model_name)(config=model_config)
# iterate over rule data and analyze point-by-point
saved_data_list = []
data = pickle.load(open(args.output_file_name))
for rule, rule_data in data.items():
logr.info(u"analyzing rule: {}".format(rule))
plotable_data = analyzer(rule_data, model, logr)
# remove spaces in rule name
rule_name = rule.replace(" ", "-")[0:100]
if args.do_plot:
return_val = plotter(plotable_data, rule_name)
# the plotter returns -1 if the counts data don't exist
if return_val == -1:
continue
# save data
if plotable_data != []:
saved_data_list.append((rule_name, plotable_data))
def max_last_field_getter(tup):
""" a function that acts on tuples like:
