content stringlengths 5 1.05M |
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from piquery.piq_hash64 import download_to_hash
from piquery.piq_error import DownloadError, ImageFormatError
from piquery.piq_config import config as db_cfg
from piquery.piq_response import Response
class PIQueryHash64:
def __init__(self, db):
self.db = db
def queryRepeat(self, url):
try:
img_hash = download_to_hash(url)
sql = 'select cid, aid from {} where `dhash`="{}" limit 1'.format(db_cfg['table_hash'], img_hash)
db_row = self.db.read(sql)
except DownloadError as err:
return Response.json(errorcode=501, errormsg=repr(err))
except ImageFormatError as err:
return Response.json(errorcode=502, errormsg=repr(err))
except:
return Response.json(errorcode=500, errormsg='server inner exception!')
if db_row:
return Response.json(repeat=True, repeat_confidence=1.0, cid=db_row[0], id=db_row[1])
return Response.json(repeat=False)
class AddHashCommand:
def __init__(self, db, cid, _id, url):
self.db = db
self.cid = cid
self.id = _id
self.url = url
def _exist(self):
return self.db.read('select * from {} where `cid`="{}" and `aid`="{}"'.format(db_cfg['table_hash'], self.cid, self.id))
def execute(self):
if self._exist():
return True
img_hash = img_hash = download_to_hash(self.url)
sql = 'insert into {} (`url`, `cid`, `aid`, `dhash`) values ("{}", "{}", "{}", "{}")'.format(db_cfg['table_hash'], self.url, self.cid, self.id, img_hash)
return self.db.write(sql)
class DelHashCommand:
def __init__(self, db, cid, _id):
self.db = db
self.cid = cid
self.id = _id
def _exist(self):
return self.db.read('select * from {} where `cid`="{}" and `aid` in ({})'.format(db_cfg['table_hash'], self.cid, self.id))
def execute(self):
if not self._exist():
return True
sql = 'delete from {} where cid="{}" and `aid` in ({})'.format(db_cfg['table_hash'], self.cid, self.id)
return self.db.write(sql)
|
import datetime
from core.state import State, StateTime
N_TESTS = 100000
state_avg = 0
state_time_avg = 0
for i in range(N_TESTS):
if i % (N_TESTS / 10) == 0:
print(i / N_TESTS * 100, "%")
t1 = datetime.datetime.now()
s1 = State()
t2 = datetime.datetime.now()
st = StateTime(s1, 0.0)
t3 = datetime.datetime.now()
state_avg += (t2 - t1).microseconds
state_time_avg += (t3 - t2).microseconds
state_avg /= N_TESTS
state_time_avg /= N_TESTS
print("Average time to create State: ", state_avg, " \u03BCs")
print("Average time to create StateTime: ", state_time_avg, " \u03BCs") |
__author__ = 'flier'
|
#!/usr/bin/env python
# coding=utf-8
import dominate
from dominate.tags import *
from newspaper import Article
from textblob import TextBlob
from qutescript import userscript
polarity_map = {
8: 'green',
5: 'olive',
2: '#333',
0: '#777',
-2: 'orange',
-5: 'red',
-8: 'brown',
}
def get_polarity_color(polarity):
for thresh in reversed(sorted(polarity_map.keys())):
if (polarity * 10) >= thresh:
return polarity_map[thresh]
else:
return '#777'
def generate_html(paragraphs, title_text):
doc = dominate.document(title='Summary: {}'.format(title_text))
with doc.head:
style("""\
body {
background-color: #F9F8F1;
color: #2C232A;
font-family: sans-serif;
font-size: 1.2em;
}
""")
with doc:
div(id='header').add(h1(title_text))
with div():
attr(cls='body')
for para in paragraphs:
tb = TextBlob(para)
with p():
for sentence in tb.sentences:
span(sentence, style="color: {}".format(get_polarity_color(sentence.polarity)))
return doc
@userscript
def sentiment_markup(request):
article = Article(request.url)
# article.download(request.html, request.title)
article.download()
article.parse()
html = generate_html(article.text.split('\n\n'), article.title).render()
request.send_html(html)
if __name__ == '__main__':
sentiment_markup()
|
#!/usr/bin/env python
import argparse
from HAPpy import genome_fork as genome
import re, os, subprocess, tempfile, shlex, sys
def orf_finder(sequence,startphase,stopphase,strand,expected_aa_len,length_variance,
search_coords = None, is_start = False, is_stop = False,
hmm_profile = None, evalue='0.05', genewise_on_fail = True):
"""Finds all putative exons matching a given expected length and intron phase profile and \
containing an open reading frame"""
search_seq = sequence
if search_coords:
search_seq = sequence[search_coords[0]:search_coords[1]]
if strand == '+':
search_seq = genome.Sequence(search_seq)
elif strand == '-':
search_seq = genome.Sequence(search_seq).reverse_compliment()
match_len = expected_aa_len * 3 + ((3 - startphase) % 3) + stopphase
phase_matches = []
startstop = ['AG','G[TC]']
if is_start:
startstop[0] = 'ATG'
match_len = match_len - 3
if is_stop:
startstop[1] = "T(AG|GA|AA)"
match_len = match_len - 3
for variance in range(length_variance + 1):
for direction in (1,-1):
if variance == 0 and direction == -1: # prevents double returns of 0 variance
continue
for match in re.finditer(startstop[0] + '.{' + str(match_len + variance * 3 * direction) +
'}' + startstop[1], search_seq):
phase_matches.append([match.start(), match.start() + match_len + variance * 3 *
direction + len(startstop[0]) + len(startstop[1])])
exon_coords = []
for match_coords in phase_matches:
start = match_coords[0] + len(startstop[0])
stop = match_coords[1] - len(startstop[1])
if stop - (start + ((3 - startphase) % 3)) > 2:
if not "*" in genome.Sequence(search_seq[start + ((3 - startphase) % 3) : stop ]).translate():
#NB: returns 1-based coords consistent with gff and blast coords
exon_coords.append([start + 1,stop])
if is_start:
exon_coords[-1][0] = exon_coords[-1][0] - 3
if is_stop:
exon_coords[-1][1] = exon_coords[-1][1] + 3
if strand == '-':
exon_coords[-1] = [len(search_seq) - exon_coords[-1][1] + 1,len(search_seq) - exon_coords[-1][0] + 1]
if search_coords:
exon_coords[-1] = [exon_coords[-1][0] + search_coords[0],exon_coords[-1][1] + search_coords[0]]
if hmm_profile and len(exon_coords) > 0:
exon_coords = hmmsearch(hmm_profile,exon_coords,sequence,strand,startphase,evalue=evalue)
if exon_coords == [] and genewise_on_fail and search_coords[1] - search_coords[0] < 10000:
if not search_coords:
search_coords = [0,None]
exon_coords = genewisesearch(sequence,startphase,stopphase,strand,
hmm_profile, search_coords = search_coords,seqname = None)
elif exon_coords != []:
exon_coords = [ exon_coords + [False] ]
return exon_coords
def hmmsearch(hmm_profile,exon_coords,sequence,strand,startphase, evalue= "0.05"):
orf_file = tempfile.NamedTemporaryFile('w')
for coords_index in range(len(exon_coords)):
nuc_seq = genome.Sequence(sequence[exon_coords[coords_index][0] - 1:exon_coords[coords_index][1]])
if strand == '-':
nuc_seq = nuc_seq.reverse_compliment()
pep_seq = genome.Sequence(nuc_seq[((3 - startphase) % 3):]).translate()
orf_file.write(">coords" + str(coords_index) + '\n' +
pep_seq + '\n' )
orf_file.flush()
hmmout = subprocess.check_output(shlex.split('hmmsearch --max -E ' + str(evalue) + ' ' + hmm_profile + ' ' +
orf_file.name)).decode('utf8').split('\n')
found_hit = False
for line in hmmout:
if line[:2] == ">>":
found_hit = True
return exon_coords[int(line[9:].replace('\r',''))]
break
if not found_hit:
return []
def genewisesearch(sequence,startphase,stopphase,strand,hmm_profile,
search_coords = [0,None],seqname = None, log_file = open(os.devnull, 'w')):
"""searches an hmm profile against a sequence (optionally within a designated sub region) to \
find boundaries of an exon interupted by stop codons or frame shifts"""
os.environ['WISECONFIGDIR'] = os.path.dirname(genome.__file__) + '/cfgFiles'
seqfile = tempfile.NamedTemporaryFile('w')
seqfile.write(">temp_" + str(search_coords[0]) + "-" + str(search_coords[1]) + "\n" + sequence[search_coords[0]:search_coords[1]] + "\n")
seqfile.flush()
modelfile = tempfile.NamedTemporaryFile('w')
subprocess.run(shlex.split('hmmconvert -2 ' + hmm_profile),stdout=modelfile)
modelfile.flush()
try:
gwout = subprocess.check_output(shlex.split('genewisedb -sum -gff -hmmer ' \
+ modelfile.name + ' ' + seqfile.name),stderr = log_file).decode('utf8').split('\n')
except subprocess.CalledProcessError:
log_file.write('warning: genewise failed on one gene, continuing\n')
return []
#sys.stderr.write("\n".join(gwout) + "\n")
tcoords = [None,None]
qcoords = []
keeplines = 0
for line in gwout:
if line != "":
if line[0] == "/" and not tcoords[1]:
tcoords[0] = None
if len(line) > 4:
if line[:4] == 'Bits' and not tcoords[0]:
tcoords[0] = 'primed'
elif tcoords[0] and not tcoords[1]:
fields = line.split()
start,stop = int(fields[5]), int(fields[6])
if (strand == '+' and start < stop ) or (strand == "-" and start > stop):
tcoords = [start,stop]
keeplines = 1 + int(fields[7])
elif tcoords[1] and tcoords[0] and "\tcds\t" in line and keeplines > 0:
fields = line.split('\t')
if fields[6] == strand:
coords = [int(fields[3]) + search_coords[0],int(fields[4]) + search_coords[0]]
if qcoords != []:
if fields[6] == '-':
while coords[1] > qcoords[-1][0]:
coords[1] += 3
if coords[1] + 3 >= coords[0]:
break
elif fields[6] == '+':
while coords[0] < qcoords[-1][1]:
coords[0] += 3
if coords[0] + 3 >= coords[1]:
break
qcoords.append(sorted(coords))
#sys.stderr.write(str(sorted(coords))+ "\n")
keeplines -= 1
elif tcoords[1] and tcoords[0] and keeplines == 0:
break
qcoords.sort()
if qcoords != []:
#sys.stderr.write(str(qcoords) + '\n' + str((3 - startphase) % 3) + '\n' + str(stopphase) + "\n" + strand + "\n")
if strand == '+':
qcoords[0][0] = qcoords[0][0] - (3 - startphase) % 3
qcoords[-1][-1] = qcoords[-1][-1] + stopphase
elif strand == '-':
qcoords[0][0] = qcoords[0][0] - stopphase
qcoords[-1][-1] = qcoords[-1][-1] + (3 - startphase) % 3
if len(qcoords) > 1:
log_file.write('anotherone bites the dust\t')
log_file.write(str(seqname) + '\t' + str([k + ['P'] for k in qcoords]) + '\n')
return [k + ['P'] for k in qcoords]
def main():
parser = argparse.ArgumentParser(description='Finds orfs between splice sites (or start and stop sites) which match a given \
starting and ending intron phase')
parser.add_argument('--sequence', help = 'sequence, raw or in fasta format')
parser.add_argument('--startphase', type = int, help = 'phase of preceding intron')
parser.add_argument('--stopphase', type = int, help = 'phase of following intron')
parser.add_argument('--expected_aa_length', type = int, help = 'expected length (in amino acids) of exon product')
parser.add_argument('--length_variance', type = int, help = 'permitted variance in exon length (in amino acids)')
parser.add_argument('--strand', help = 'strand of expected feature')
parser.add_argument('--search_coords', default = None, help = 'subset of sequence within which to search')
parser.add_argument('--is_start', default = False, type = bool, help = 'Is this exon the first exon- i.e. should it start with atg instead of a splice site')
parser.add_argument('--is_stop', default = False, type = bool, help = 'Is this exon the last exon- i.e. should it start with a stop codon instead of a splice site')
parser.add_argument('--hmm_profile', default = None, help = "If provided, FindOrfs will search orfs against the provided\
hmm profile and return the best hit")
args = parser.parse_args()
sequence = ""
seqcounter = 0
for line in open(args.sequence):
if not line[0] == ">":
sequence = sequence + line.replace('\r','').replace('\n','')
else:
seqcounter += 1
if seqcounter > 1:
print("multiple seqs in fasta- not yet supported")
break
exon_coords = orf_finder(sequence,args.startphase, args.stopphase, args.strand,
args.expected_aa_length, args.length_variance, args.search_coords,
args.is_start, args.is_stop, hmm_profile = args.hmm_profile)
for coords in exon_coords:
print(coords)
if __name__ == "__main__":
main()
|
# Preppin' Data 2021 Week 16
import pandas as pd
import numpy as np
# remove warnings
pd.set_option('mode.chained_assignment', None)
# Input the files
fixtures = pd.read_csv('unprepped_data\\PD 2021 Wk 16 Input - PL Fixtures.csv')
# Filter out matches which haven't been played yet
played_matches = fixtures[fixtures['Result'].notnull()]
# Split out goals from result field
played_matches[['Home Team Goals','Away Team Goals']] = played_matches['Result'].str.split(' - ', expand=True)
played_matches[['Home Team Goals','Away Team Goals']] = played_matches[['Home Team Goals','Away Team Goals']].astype(int)
# Assign points from match result for home and away teams:
# - Win - 3 Points
# - Draw - 1 Point
# - Lose - 0 Points
played_matches['Home Team Points'] = np.select(
[
played_matches['Home Team Goals'] == played_matches['Away Team Goals'],
played_matches['Home Team Goals'] > played_matches['Away Team Goals'],
played_matches['Home Team Goals'] < played_matches['Away Team Goals']
],
[
1,
3,
0
],
default='Unknown'
)
played_matches['Away Team Points'] = np.select(
[
played_matches['Away Team Goals'] == played_matches['Home Team Goals'],
played_matches['Away Team Goals'] > played_matches['Home Team Goals'],
played_matches['Away Team Goals'] < played_matches['Home Team Goals']
],
[
1,
3,
0
],
default='Unknown'
)
# calculate goal difference for home and away teams
played_matches['Home Team Goals Difference'] = played_matches['Home Team Goals'] - played_matches['Away Team Goals']
played_matches['Away Team Goals Difference'] = played_matches['Away Team Goals'] - played_matches['Home Team Goals']
# split home and away teams into seperate data frames to stack later
home_teams = played_matches[['Round Number','Date','Location','Home Team','Home Team Goals','Home Team Points','Home Team Goals Difference']]
away_teams = played_matches[['Round Number','Date','Location','Away Team','Away Team Goals','Away Team Points','Away Team Goals Difference']]
columns = ['Round Number','Date','Location','Team','Team Goals','Team Points','Team Goals Difference']
home_teams.columns = columns
away_teams.columns = columns
# stack home and away data frames
teams_df = pd.concat([home_teams,away_teams])
# convert points to int form str
teams_df['Team Points'] = teams_df['Team Points'].astype(int)
# create 3 data frames for the aggregations and then merge together
points = teams_df.groupby(['Team'],as_index=False)['Team Points'].agg({'Total Points': 'sum'})
diff = teams_df.groupby(['Team'],as_index=False)['Team Goals Difference'].agg({'Goal Difference': 'sum'})
matches = teams_df.groupby(['Team'],as_index=False)['Team Points'].agg({'Total Games Played': 'count'})
output_1 = pd.merge(points,diff, on='Team', how = 'inner')
output_1 = pd.merge(output_1,matches, on='Team', how = 'inner')
# Determine team positons by points then goal difference
output_1 = output_1.sort_values(by=['Total Points','Goal Difference'], ascending=[False,False]).reset_index()
output_1 = output_1.reset_index(drop=True)
output_1['Position'] = output_1.index +1
# Clean up data frame for output
del output_1['index']
output_1 = output_1[['Position','Team','Total Points','Goal Difference','Total Games Played']]
# Writing data to csv
output_1.to_csv('prepped_data\\PD 2021 Wk 16 Output - Current League Table.csv', index=False)
# Assuming that the 'Big 6' didn't play any games this season, recalculate the league table.
# After removing the 6 clubs, how has the position changed for the remaining clubs?
big_six = ['Man City','Man Utd','Arsenal','Chelsea','Liverpool','Spurs']
# Rerun earlier process filtering out the big six
played_matches_new = played_matches.loc[~played_matches['Home Team'].isin(big_six)]
played_matches_new = played_matches_new.loc[~played_matches_new['Away Team'].isin(big_six)]
# split home and away teams into seperate data frames to stack later
home_teams = played_matches_new[['Round Number','Date','Location','Home Team','Home Team Goals','Home Team Points','Home Team Goals Difference']]
away_teams = played_matches_new[['Round Number','Date','Location','Away Team','Away Team Goals','Away Team Points','Away Team Goals Difference']]
columns = ['Round Number','Date','Location','Team','Team Goals','Team Points','Team Goals Difference']
home_teams.columns = columns
away_teams.columns = columns
# stack home and away data frames
new_league_df = pd.concat([home_teams,away_teams])
new_league_df['Team Points'] = new_league_df['Team Points'].astype(int)
# create 3 data frames for the aggregations and then merge together
points = new_league_df.groupby(['Team'],as_index=False)['Team Points'].agg({'Total Points': 'sum'})
diff = new_league_df.groupby(['Team'],as_index=False)['Team Goals Difference'].agg({'Goal Difference': 'sum'})
matches = new_league_df.groupby(['Team'],as_index=False)['Team Points'].agg({'Total Games Played': 'count'})
output_2 = pd.merge(points,diff, on='Team', how = 'inner')
output_2 = pd.merge(output_2,matches, on='Team', how = 'inner')
# Determine team positons by points then goal difference
output_2 = output_2.sort_values(by=['Total Points','Goal Difference'], ascending=[False,False]).reset_index()
output_2 = output_2.reset_index(drop=True)
output_2['Position'] = output_2.index +1
# Clean up data frame for output
del output_2['index']
output_2 = output_2[['Position','Team','Total Points','Goal Difference','Total Games Played']]
# Add previous rank to calculate change in position
previous_rank = output_1[['Position','Team']]
previous_rank.columns = ['Previous Position','Team']
output_2 = pd.merge(output_2,previous_rank, on='Team', how = 'inner')
output_2['Position Change'] = output_2['Previous Position'] - output_2['Position']
output_2 = output_2[['Position Change','Position','Team','Total Points','Goal Difference','Total Games Played']]
# Writing data to csv
output_2.to_csv('prepped_data\\PD 2021 Wk 16 Output - New League Table.csv', index=False)
print("data prepped!")
|
matrix1 = [[1, 2, 3], [4, 6, 3], [2, 5, 5]]
matrix2 = [[1, 4, 2], [6, 2, 2], [6, 2, 7]]
resultMatrix = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
if len(matrix1) == len(matrix2) :
for i in range(len(matrix1)) :
for j in range(len(matrix2)) :
resultMatrix[i][j] = matrix1[i][j] + matrix2[i][j]
print('Sum of ', matrix1, ' and ', matrix2, ' is: ', resultMatrix) |
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from airflow import DAG
from airflow.contrib.operators import gcs_to_bq, kubernetes_pod_operator
default_args = {
"owner": "Google",
"depends_on_past": False,
"start_date": "2021-03-01",
}
with DAG(
dag_id="google_political_ads.geo_spend",
default_args=default_args,
max_active_runs=1,
schedule_interval="@daily",
catchup=False,
default_view="graph",
) as dag:
# Run CSV transform within kubernetes pod
geo_spend_transform_csv = kubernetes_pod_operator.KubernetesPodOperator(
task_id="geo_spend_transform_csv",
startup_timeout_seconds=600,
name="geo_spend",
namespace="default",
affinity={
"nodeAffinity": {
"requiredDuringSchedulingIgnoredDuringExecution": {
"nodeSelectorTerms": [
{
"matchExpressions": [
{
"key": "cloud.google.com/gke-nodepool",
"operator": "In",
"values": ["pool-e2-standard-4"],
}
]
}
]
}
}
},
image_pull_policy="Always",
image="{{ var.json.google_political_ads.container_registry.run_csv_transform_kub }}",
env_vars={
"SOURCE_URL": "https://storage.googleapis.com/transparencyreport/google-political-ads-transparency-bundle.zip",
"SOURCE_FILE": "files/data.zip",
"FILE_NAME": "google-political-ads-transparency-bundle/google-political-ads-geo-spend.csv",
"TARGET_FILE": "files/data_output.csv",
"TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}",
"TARGET_GCS_PATH": "data/google_political_ads/geo_spend/data_output.csv",
"PIPELINE_NAME": "geo_spend",
"CSV_HEADERS": '["country","country_subdivision_primary","country_subdivision_secondary","spend_usd","spend_eur","spend_inr","spend_bgn","spend_hrk","spend_czk","spend_dkk","spend_huf","spend_pln","spend_ron","spend_sek","spend_gbp","spend_nzd"]',
"RENAME_MAPPINGS": '{"Country": "country","Country_Subdivision_Primary": "country_subdivision_primary","Country_Subdivision_Secondary": "country_subdivision_secondary","Spend_USD": "spend_usd","Spend_EUR": "spend_eur","Spend_INR": "spend_inr","Spend_BGN": "spend_bgn","Spend_HRK": "spend_hrk","Spend_CZK": "spend_czk","Spend_DKK": "spend_dkk","Spend_HUF": "spend_huf","Spend_PLN": "spend_pln","Spend_RON": "spend_ron","Spend_SEK": "spend_sek","Spend_GBP": "spend_gbp","Spend_NZD": "spend_nzd"}',
},
resources={"request_memory": "2G", "request_cpu": "1"},
)
# Task to load CSV data to a BigQuery table
load_geo_spend_to_bq = gcs_to_bq.GoogleCloudStorageToBigQueryOperator(
task_id="load_geo_spend_to_bq",
bucket="{{ var.value.composer_bucket }}",
source_objects=["data/google_political_ads/geo_spend/data_output.csv"],
source_format="CSV",
destination_project_dataset_table="google_political_ads.geo_spend",
skip_leading_rows=1,
write_disposition="WRITE_TRUNCATE",
schema_fields=[
{
"name": "country",
"type": "string",
"description": 'The country where election ads were served specified in the ISO 3166-1 alpha-2 standard code. For example "US" for United States.',
"mode": "nullable",
},
{
"name": "country_subdivision_primary",
"type": "string",
"description": 'The primary subdivision of the country where election ads were served specified by the ISO 3166-2 standard code. For example "US-CA" for California state in United States',
"mode": "nullable",
},
{
"name": "country_subdivision_secondary",
"type": "string",
"description": "The name of the secondary subdivision. For example The name of a US congressional district.",
"mode": "nullable",
},
{
"name": "spend_usd",
"type": "integer",
"description": "Total amount in USD spent on election ads in this region.",
"mode": "nullable",
},
{
"name": "spend_eur",
"type": "integer",
"description": "Total amount in EUR spent on election ads in this region.",
"mode": "nullable",
},
{
"name": "spend_inr",
"type": "integer",
"description": "Total amount in INR spent on election ads in this region.",
"mode": "nullable",
},
{
"name": "spend_bgn",
"type": "integer",
"description": "Total amount in BGN spent on election ads in this region.",
"mode": "nullable",
},
{
"name": "spend_hrk",
"type": "integer",
"description": "Total amount in HRK spent on election ads in this region.",
"mode": "nullable",
},
{
"name": "spend_czk",
"type": "integer",
"description": "Total amount in CZK spent on election ads in this region.",
"mode": "nullable",
},
{
"name": "spend_dkk",
"type": "integer",
"description": "Total amount in DKK spent on election ads in this region.",
"mode": "nullable",
},
{
"name": "spend_huf",
"type": "integer",
"description": "Total amount in HUF spent on election ads in this region.",
"mode": "nullable",
},
{
"name": "spend_pln",
"type": "integer",
"description": "Total amount in PLN spent on election ads in this region.",
"mode": "nullable",
},
{
"name": "spend_ron",
"type": "integer",
"description": "Total amount in RON spent on election ads in this region.",
"mode": "nullable",
},
{
"name": "spend_sek",
"type": "integer",
"description": "Total amount in SEK spent on election ads in this region.",
"mode": "nullable",
},
{
"name": "spend_gbp",
"type": "integer",
"description": "Total amount in GBP spent on election ads in this region.",
"mode": "nullable",
},
{
"name": "spend_nzd",
"type": "integer",
"description": "Total amount in NZD spent on election ads in this region.",
"mode": "nullable",
},
],
)
geo_spend_transform_csv >> load_geo_spend_to_bq
|
import pytest
from fbotics.models.quick_reply import QuickReply
from schematics.exceptions import DataError
def test_validation_when_content_type_is_text_and_title_does_not_exist(client):
"""
GIVEN a QuickReply object with a text content type and not title
WHEN validating the object
THEN is throws a validation error
"""
qr = QuickReply({"content_type": "text"})
with pytest.raises(DataError):
qr.validate()
def test_validation_when_content_type_is_text_and_image_url_is_set_but_payload_is_not_set(
client,
):
"""
GIVEN a QuickReply object with a text content type and an image url, but without a payload
WHEN validating the object
THEN is throws a validation error
"""
qr = QuickReply({"content_type": "text", "image_url": "xxx"})
with pytest.raises(DataError):
qr.validate()
def test_validation_when_content_type_is_text_and_title_is_empty_and_image_url_is_not_set(
client,
):
"""
GIVEN a QuickReply object with a text content type and an empty title, but without an image_url
WHEN validating the object
THEN is throws a validation error
"""
qr = QuickReply({"content_type": "text", "title": ""})
with pytest.raises(DataError):
qr.validate()
|
MeshLoop.tangent = None
|
import json
import pytest
import torch.distributed as dist
from tango.common.logging import initialize_logging, teardown_logging
from tango.common.testing import TangoTestCase
class TestTrainStep(TangoTestCase):
def setup_method(self):
super().setup_method()
initialize_logging(enable_cli_logs=True)
def teardown_method(self):
super().teardown_method()
if dist.is_initialized():
dist.destroy_process_group()
teardown_logging()
@pytest.mark.parametrize("with_validation", [True, False])
def test_basic_train(self, with_validation: bool):
result_dir = self.run(
self.FIXTURES_ROOT / "integrations" / "torch" / "train.jsonnet",
include_package=[
"test_fixtures.integrations.common",
"test_fixtures.integrations.torch",
],
overrides=""
if with_validation
else json.dumps(
{"steps.train.validation_split": None, "steps.train.validate_every": None}
),
)
assert (result_dir / "train" / "data.pt").is_file()
assert (result_dir / "train" / "work" / "weights.pt").is_file()
assert (
result_dir / "train" / "work" / "checkpoint_state_latest" / "worker0_model.pt"
).is_file()
assert (
result_dir / "train" / "work" / "checkpoint_state_best" / "worker0_optimizer.pt"
).is_file()
assert (
result_dir / "train" / "work" / "checkpoint_state_best" / "worker0_trainer.pt"
).is_file()
def test_basic_train_with_epochs(self):
result_dir = self.run(
self.FIXTURES_ROOT / "integrations" / "torch" / "train.jsonnet",
include_package=[
"test_fixtures.integrations.common",
"test_fixtures.integrations.torch",
],
overrides=json.dumps(
{
"steps.train.train_steps": None,
"steps.train.train_epochs": 2,
"steps.train.validate_every": None,
}
),
)
assert (result_dir / "train" / "data.pt").is_file()
def test_basic_train_with_streaming_data(self):
result_dir = self.run(
self.FIXTURES_ROOT / "integrations" / "torch" / "train.jsonnet",
include_package=[
"test_fixtures.integrations.common",
"test_fixtures.integrations.torch",
],
)
assert (result_dir / "train" / "data.pt").is_file()
def test_train_distributed(self):
result_dir = self.run(
self.FIXTURES_ROOT / "integrations" / "torch" / "train_dist.jsonnet",
include_package=[
"test_fixtures.integrations.common",
"test_fixtures.integrations.torch",
],
)
assert (result_dir / "train" / "data.pt").is_file()
assert (result_dir / "train" / "work" / "weights.pt").is_file()
assert (
result_dir / "train" / "work" / "checkpoint_state_latest" / "worker0_model.pt"
).is_file()
assert (
result_dir / "train" / "work" / "checkpoint_state_best" / "worker0_model.pt"
).is_file()
assert (
result_dir / "train" / "work" / "checkpoint_state_latest" / "worker1_model.pt"
).is_file()
assert (
result_dir / "train" / "work" / "checkpoint_state_best" / "worker1_model.pt"
).is_file()
|
from ._quantum import Quantum
from ._position import Position
|
# Copyright 2021 The FastEstimator Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import os
import tempfile
import numpy as np
import pandas as pd
import tensorflow as tf
import wget
from scipy import stats
from tensorflow.keras import Model, layers
import fastestimator as fe
from fastestimator.dataset.data import cifar10
from fastestimator.op.numpyop.univariate import Normalize
from fastestimator.search import GridSearch
from fastestimator.util import to_number
from fastestimator.util.wget_util import bar_custom, callback_progress
wget.callback_progress = callback_progress
# Predefined operation set
OPS = {
'none':
lambda inputs,
n_filters,
stride: _zero(inputs, n_filters),
'avg_pool_3x3':
lambda inputs,
n_filters,
stride: _pooling(inputs, n_filters, stride),
'nor_conv_3x3':
lambda inputs,
n_filters,
stride: _relu_conv_bn_block(inputs, n_filters, (3, 3), stride, "same", 1),
'nor_conv_1x1':
lambda inputs,
n_filters,
stride: _relu_conv_bn_block(inputs, n_filters, (1, 1), stride, "valid", 1),
'skip_connect':
lambda inputs,
n_filters,
stride: _identity(inputs)
if stride == 1 and inputs.shape[-1] == n_filters else _factorize_reduce(inputs, n_filters, stride),
}
def _resnet_basic_block(inputs, n_filters, stride):
assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
x = _relu_conv_bn_block(inputs, n_filters, kernel_size=3, stride=stride, padding="same", dilation=1)
x = _relu_conv_bn_block(x, n_filters, kernel_size=3, stride=1, padding="same", dilation=1)
if stride == 2:
residual = layers.AveragePooling2D(pool_size=2, strides=stride, padding="valid")(inputs)
residual = layers.Conv2D(n_filters, 1, 1, padding="valid", use_bias=False)(residual)
elif inputs.shape[-1] != n_filters:
residual = _relu_conv_bn_block(inputs, n_filters, kernel_size=1, stride=1, padding="valid", dilation=1)
else:
residual = inputs
return residual + x
def _relu_conv_bn_block(inputs, n_filters, kernel_size, stride, padding, dilation):
x = layers.ReLU()(inputs)
x = layers.Conv2D(n_filters, kernel_size, stride, padding=padding, dilation_rate=dilation, use_bias=False)(x)
x = layers.BatchNormalization(momentum=0.9)(x)
return x
def _pooling(inputs, n_filters, stride):
if inputs.shape[-1] != n_filters:
inputs = _relu_conv_bn_block(inputs, n_filters, kernel_size=1, stride=1, padding="valid", dilation=1)
x = layers.AveragePooling2D(pool_size=3, strides=stride, padding="same")(inputs)
return x
def _identity(inputs):
return inputs
def _zero(inputs, n_filters):
inp_shape = inputs.shape
if inp_shape[-1] == n_filters:
return 0. * inputs
else:
inp_shape[-1] = n_filters
return tf.zeros(inp_shape, inputs.dtype)
def _factorize_reduce(inputs, n_filters, stride):
if stride == 2:
filters_list = [n_filters // 2, n_filters - n_filters // 2]
x = layers.ReLU()(inputs)
y = tf.pad(inputs, [0, 0, 1, 1], mode="CONSTANT")
x = layers.Conv2D(filters_list[0], kernel_size=1, stride=stride, padding="valid", use_bias=False)(x)
y = layers.Conv2D(filters_list[1], kernel_size=1, stride=stride, padding="valid",
use_bias=False)(y[:, 1:, 1:, :])
out = tf.cat([x, y], dim=1)
elif stride == 1:
out = layers.Conv2D(n_filters, kernel_size=1, stride=stride, padding="valid", use_bias=False)(inputs)
else:
raise ValueError('Invalid stride : {:}'.format(stride))
out = layers.BatchNormalization(momentum=0.9)(out)
return out
def str2structure(xstr):
"""Process the architecture string from NAS-Bench-201. Referenced from https://github.com/D-X-Y/AutoDL-Projects.
"""
assert isinstance(xstr, str), 'must take string (not {:}) as input'.format(type(xstr))
nodestrs = xstr.split('+')
genotypes = []
for node_str in nodestrs:
inputs = list(filter(lambda x: x != '', node_str.split('|')))
for xinput in inputs:
assert len(xinput.split('~')) == 2, 'invalid input length : {:}'.format(xinput)
inputs = (xi.split('~') for xi in inputs)
input_infos = tuple((op, int(IDX)) for (op, IDX) in inputs)
genotypes.append(input_infos)
return genotypes
def _infer_cell(inputs, genotype, n_filters, stride):
x_in = [inputs]
for i in range(len(genotype)):
node_info = genotype[i]
if len(node_info) == 1:
op_name, op_in = node_info[0]
x = OPS[op_name](x_in[op_in], n_filters, stride) if op_in == 0 else OPS[op_name](x_in[op_in], n_filters, 1)
else:
x = layers.Add()([
OPS[op_name](x_in[op_in], n_filters, stride) if op_in == 0 else OPS[op_name](x_in[op_in], n_filters, 1)
for (op_name, op_in) in node_info
])
x_in.append(x)
return x
def nasbench_network(input_shape, genotype, C=16, N=5, num_classes=10):
layer_channels = [C] * N + [C * 2] + [C * 2] * N + [C * 4] + [C * 4] * N
layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
inputs = layers.Input(shape=input_shape)
x = layers.Conv2D(C, kernel_size=3, padding="same", use_bias=False)(inputs)
x = layers.BatchNormalization(momentum=0.9)(x)
for (C_curr, reduction) in zip(layer_channels, layer_reductions):
if reduction:
x = _resnet_basic_block(x, n_filters=C_curr, stride=2)
else:
x = _infer_cell(x, genotype=genotype, n_filters=C_curr, stride=1)
x = layers.BatchNormalization(momentum=0.9)(x)
x = layers.ReLU()(x)
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dense(num_classes, activation="softmax")(x)
model = Model(inputs, x)
return model
def get_pipeline_data(batch_size=128):
train_data, _ = cifar10.load_data()
pipeline = fe.Pipeline(
train_data=train_data,
batch_size=batch_size,
ops=[
Normalize(inputs="x", outputs="x", mean=(0.4914, 0.4822, 0.4465), std=(0.2471, 0.2435, 0.2616)),
])
result = pipeline.get_results()
return result
def score_fn(search_idx, uid, batch_data, config_info):
config = config_info.loc[uid, :]
nasbench201_model = nasbench_network((32, 32, 3),
str2structure(config["architecture"]),
config["C"],
config["N"],
10)
feature_list = [layer.output for layer in nasbench201_model.layers if "re_lu" in layer.name]
model = fe.build(model_fn=lambda: Model(nasbench201_model.input, feature_list), optimizer_fn=None)
# Only a single forward pass through the network is required
relu_result = fe.backend.feed_forward(model, batch_data["x"], training=False)
matrix = np.zeros((relu_result[0].shape[0], relu_result[0].shape[0]))
for sample in relu_result:
sample = to_number(sample)
sample = sample.reshape((sample.shape[0], -1))
x = (sample > 0.).astype(float)
x_t = np.transpose(x)
mat = x @ x_t
mat2 = (1. - x) @ (1. - x_t)
matrix = matrix + mat + mat2
_, score = np.linalg.slogdet(matrix)
return score
def fastestimator_run(batch_size=128, num_archs=1000, save_dir=tempfile.mkdtemp()):
download_link = "https://github.com/fastestimator-util/fastestimator-misc/raw/master/resource/nasbench201_info.csv"
uid_list = np.random.choice(15625, size=num_archs, replace=False) # Select random set of networks
wget.download(download_link, save_dir, bar=bar_custom)
config_info = pd.read_csv(os.path.join(save_dir, "nasbench201_info.csv"))
batch_data = get_pipeline_data(batch_size)
search = GridSearch(
score_fn=lambda search_idx,
uid: score_fn(search_idx, uid, batch_data=batch_data, config_info=config_info),
params={"uid": uid_list},
best_mode="max")
search.fit()
best_results = search.get_best_results()
score_list = [result[1] for result in search.get_search_results()]
acc_list = [config_info.loc[i, :]["accuracy"] for i in uid_list]
tau, _ = stats.kendalltau(acc_list, score_list)
print("Kendall's Tau correlation coefficient: ", tau)
print("Maximum accuracy among all the networks tested: ", np.max(acc_list))
print("Params for best network: {}, best score: {} and corresponding accuracy: {}".format(
best_results[0], best_results[1], config_info.loc[best_results[0]["uid"], :]["accuracy"]))
print(
"The best network is the top - {} network among the selected networks, based on trained performance (accuracy)".
format(len(acc_list) - list(np.sort(acc_list)).index(config_info.loc[best_results[0]["uid"], :]["accuracy"])))
if __name__ == "__main__":
fastestimator_run()
|
#
# -*- coding: utf-8 -*-#
# Copyright (C) 2016 AllSeeingEyeTolledEweSew <allseeingeyetolledewesew@protonmail.com>
#
# Basic plugin template created by:
# Copyright (C) 2008 Martijn Voncken <mvoncken@gmail.com>
# Copyright (C) 2007-2009 Andrew Resch <andrewresch@gmail.com>
# Copyright (C) 2009 Damien Churchill <damoxc@gmail.com>
# Copyright (C) 2010 Pedro Algarvio <pedro@algarvio.me>
#
# This file is part of YATFS and is licensed under GNU General Public License 3.0, or later, with
# the additional special exception to link portions of this program with the OpenSSL library.
# See LICENSE for more details.
#
import base64
import logging
from deluge import component
from deluge._libtorrent import lt
from deluge.core.rpcserver import export
from deluge.event import DelugeEvent
from deluge.plugins.pluginbase import CorePluginBase
log = logging.getLogger(__name__)
class MetadataReceivedEvent(DelugeEvent):
def __init__(self, torrent_id):
self._args = [torrent_id]
class BulkMetaTrackerErrorEvent(DelugeEvent):
def __init__(self, torrent_id, tracker_url, error_message, times_in_row,
status_code, error):
self._args = [
torrent_id, tracker_url, error_message, times_in_row,
status_code, error]
class Core(CorePluginBase):
def enable(self):
self.core = component.get("Core")
self.session = self.core.session
self.torrents = self.core.torrentmanager.torrents
self.pluginmanager = component.get("CorePluginManager")
self.eventmanager = component.get("EventManager")
self.alertmanager = component.get("AlertManager")
self.alertmanager.register_handler(
"metadata_received_alert", self.on_metadata_received)
self.alertmanager.register_handler(
"tracker_error_alert", self.on_tracker_error)
self.pluginmanager.register_status_field(
"bulkmetarpc.has_metadata", self.get_has_metadata)
self.pluginmanager.register_status_field(
"bulkmetarpc.upload_mode", self.get_upload_mode)
def disable(self):
self.alertmanager.deregister_handler(self.on_metadata_received)
self.alertmanager.deregister_handler(self.on_tracker_error)
self.pluginmanager.deregister_status_field("bulkmetarpc.has_metadata")
self.pluginmanager.deregister_status_field("bulkmetarpc.upload_mode")
def update(self):
pass
@export
def get_metadata(self, torrent_id):
torrent = self.torrents[torrent_id]
if torrent.handle.has_metadata():
ti = torrent.handle.get_torrent_info()
return ti.metadata()
@export
def set_upload_mode(self, torrent_id, upload_mode):
torrent = self.torrents[torrent_id]
return torrent.handle.set_upload_mode(upload_mode)
def get_has_metadata(self, torrent_id):
torrent = self.torrents[torrent_id]
return torrent.handle.has_metadata()
def get_upload_mode(self, torrent_id):
torrent = self.torrents[torrent_id]
return torrent.handle.status().upload_mode
def on_metadata_received(self, alert):
torrent_id = str(alert.handle.info_hash())
self.eventmanager.emit(MetadataReceivedEvent(torrent_id))
def on_tracker_error(self, alert):
try:
torrent_id = str(alert.handle.info_hash())
tracker_url = alert.tracker_url()
error_message = alert.error_message()
times_in_row = alert.times_in_row
status_code = alert.status_code
e = alert.error
error = {"message": e.message(), "value": e.value()}
self.eventmanager.emit(BulkMetaTrackerErrorEvent(
torrent_id, tracker_url, error_message, times_in_row,
status_code, error))
except:
raise
|
# Copyright (C) 2015 Nippon Telegraph and Telephone Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
import sys
import time
import unittest
from fabric.api import local
import nose
from lib.noseplugin import OptionParser, parser_option
from lib import base
from lib.base import (
Bridge,
BGP_FSM_ESTABLISHED,
)
from lib.gobgp import GoBGPContainer
from lib.quagga import QuaggaBGPContainer
class GoBGPTestBase(unittest.TestCase):
@classmethod
def setUpClass(cls):
gobgp_ctn_image_name = parser_option.gobgp_image
base.TEST_PREFIX = parser_option.test_prefix
# preparing the container for ipv4
g1_v4 = GoBGPContainer(name='g1_v4', asn=65000, router_id='192.168.0.1',
ctn_image_name=gobgp_ctn_image_name,
log_level=parser_option.gobgp_log_level,
zebra=True)
q1_v4 = QuaggaBGPContainer(name='q1_v4', asn=65001, router_id='192.168.0.2', zebra=True)
o1_v4 = QuaggaBGPContainer(name='o1_v4', asn=65002, router_id='192.168.0.3')
o2_v4 = QuaggaBGPContainer(name='o2_v4', asn=65002, router_id='192.168.0.4')
# preparing the container for ipv6
g1_v6 = GoBGPContainer(name='g1_v6', asn=65000, router_id='192.168.0.1',
ctn_image_name=gobgp_ctn_image_name,
log_level=parser_option.gobgp_log_level,
zebra=True)
q1_v6 = QuaggaBGPContainer(name='q1_v6', asn=65001, router_id='192.168.0.2', zebra=True)
o1_v6 = QuaggaBGPContainer(name='o1_v6', asn=65002, router_id='192.168.0.3')
o2_v6 = QuaggaBGPContainer(name='o2_v6', asn=65002, router_id='192.168.0.4')
# preparing the bridge for ipv4
br01_v4 = Bridge(name='br01_v4', subnet='192.168.10.0/24')
br02_v4 = Bridge(name='br02_v4', subnet='192.168.20.0/24')
br03_v4 = Bridge(name='br03_v4', subnet='192.168.30.0/24')
# preparing the bridge for ipv6
br01_v6 = Bridge(name='br01_v6', subnet='2001:10::/32')
br02_v6 = Bridge(name='br02_v6', subnet='2001:20::/32')
br03_v6 = Bridge(name='br03_v6', subnet='2001:30::/32')
cls.ctns = {'ipv4': [g1_v4, q1_v4, o1_v4, o2_v4],
'ipv6': [g1_v6, q1_v6, o1_v6, o2_v6]}
cls.gobgps = {'ipv4': g1_v4, 'ipv6': g1_v6}
cls.quaggas = {'ipv4': q1_v4, 'ipv6': q1_v6}
cls.others = {'ipv4': [o1_v4, o2_v4], 'ipv6': [o1_v6, o2_v6]}
cls.bridges = {
'br01_v4': br01_v4,
'br02_v4': br02_v4,
'br03_v4': br03_v4,
'br01_v6': br01_v6,
'br02_v6': br02_v6,
'br03_v6': br03_v6,
}
"""
No.1 start up ipv4 containers and check state
each neighbor is established in ipv4 environment
"""
def test_01_check_neighbor_established(self):
g1 = self.gobgps['ipv4']
q1 = self.quaggas['ipv4']
o1 = self.others['ipv4'][0]
o2 = self.others['ipv4'][1]
# start up containers of ipv4 environment
initial_wait_time = max(ctn.run() for ctn in self.ctns['ipv4'])
time.sleep(initial_wait_time)
# make ipv4 bridge and set ip to each container
[self.bridges['br01_v4'].addif(ctn) for ctn in [o1, g1]]
[self.bridges['br02_v4'].addif(ctn) for ctn in [g1, q1]]
[self.bridges['br03_v4'].addif(ctn) for ctn in [q1, o2]]
g1.add_peer(q1, bridge=self.bridges['br02_v4'].name)
q1.add_peer(g1, bridge=self.bridges['br02_v4'].name)
g1.wait_for(expected_state=BGP_FSM_ESTABLISHED, peer=q1)
"""
No.2 check whether the ping is reachable in container
that have previously beyond the gobpg in ipv4 environment
"""
def test_02_check_reachablily_beyond_gobgp_from_quagga(self):
g1 = self.gobgps['ipv4']
q1 = self.quaggas['ipv4']
o1 = self.others['ipv4'][0]
next_hop = None
for info in g1.ip_addrs:
if 'br01_v4' in info[2]:
next_hop = info[1].split('/')[0]
self.assertFalse(next_hop is None)
o1.add_static_route(self.bridges['br02_v4'].subnet, next_hop)
addr = [e[1] for e in o1.ip_addrs if 'br01_v4' in e[2]]
self.assertTrue(len(addr) == 1)
q1.get_reachablily(addr[0])
"""
No.3 check whether the ping is reachable in container
that have previously beyond the quagga in ipv4 environment
"""
def test_03_check_reachablily_beyond_quagga_from_gobgp(self):
g1 = self.gobgps['ipv4']
q1 = self.quaggas['ipv4']
o2 = self.others['ipv4'][1]
next_hop = q1.ip_addrs[2][1].split('/')[0]
o2.add_static_route(self.bridges['br02_v4'].subnet, next_hop)
addr = [e[1] for e in o2.ip_addrs if 'br03_v4' in e[2]]
self.assertTrue(len(addr) == 1)
g1.get_reachablily(addr[0])
"""
No.4 start up ipv4 containers and check state
each neighbor is established in ipv6 environment
"""
def test_04_check_neighbor_established_v6(self):
g1 = self.gobgps['ipv6']
q1 = self.quaggas['ipv6']
o1 = self.others['ipv6'][0]
o2 = self.others['ipv6'][1]
# start up containers of ipv6 environment
initial_wait_time = max(ctn.run() for ctn in self.ctns['ipv6'])
time.sleep(initial_wait_time)
# make ipv6 bridge and set ip to each container
[self.bridges['br01_v6'].addif(ctn) for ctn in [o1, g1]]
[self.bridges['br02_v6'].addif(ctn) for ctn in [g1, q1]]
[self.bridges['br03_v6'].addif(ctn) for ctn in [q1, o2]]
g1.add_peer(q1, bridge=self.bridges['br02_v6'].name)
q1.add_peer(g1, bridge=self.bridges['br02_v6'].name)
g1.wait_for(expected_state=BGP_FSM_ESTABLISHED, peer=q1)
"""
No.5 check whether the ping is reachable in container
that have previously beyond the gobpg in ipv6 environment
"""
def test_05_check_reachablily_beyond_gobgp_from_quagga(self):
g1 = self.gobgps['ipv6']
q1 = self.quaggas['ipv6']
o1 = self.others['ipv6'][0]
next_hop = g1.ip_addrs[1][1].split('/')[0]
g1.set_ipv6_forward()
o1.add_static_route(self.bridges['br02_v6'].subnet, next_hop)
addr = [e[1] for e in o1.ip_addrs if 'br01_v6' in e[2]]
self.assertTrue(len(addr) == 1)
q1.get_reachablily(addr[0])
"""
No.6 check whether the ping is reachable in container
that have previously beyond the quagga in ipv6 environment
"""
def test_06_check_reachablily_beyond_quagga_from_gobgp(self):
g1 = self.gobgps['ipv6']
q1 = self.quaggas['ipv6']
o2 = self.others['ipv6'][1]
next_hop = q1.ip_addrs[2][1].split('/')[0]
q1.set_ipv6_forward()
o2.add_static_route(self.bridges['br02_v6'].subnet, next_hop)
addr = [e[1] for e in o2.ip_addrs if 'br03_v6' in e[2]]
self.assertTrue(len(addr) == 1)
g1.get_reachablily(addr[0])
def test_07_mpath_test_setup(self):
g1 = GoBGPContainer(name='g1', asn=65000, router_id='192.168.0.1',
ctn_image_name=parser_option.gobgp_image,
log_level=parser_option.gobgp_log_level,
config_format=parser_option.config_format,
zebra=True)
g2 = GoBGPContainer(name='g2', asn=65001, router_id='192.168.0.2',
ctn_image_name=parser_option.gobgp_image)
g3 = GoBGPContainer(name='g3', asn=65001, router_id='192.168.0.3',
ctn_image_name=parser_option.gobgp_image)
g4 = GoBGPContainer(name='g4', asn=65000, router_id='192.168.0.4',
ctn_image_name=parser_option.gobgp_image)
g5 = GoBGPContainer(name='g5', asn=65000, router_id='192.168.0.5',
ctn_image_name=parser_option.gobgp_image)
ctns = [g1, g2, g3, g4, g5]
for ctn in ctns:
self.ctns[ctn.name] = ctn
initial_wait_time = max(ctn.run() for ctn in ctns)
time.sleep(initial_wait_time)
# advertise same prefix
g2.add_route('10.0.10.0/24')
g3.add_route('10.0.10.0/24')
g4.add_route('10.0.10.0/24')
g5.add_route('10.0.10.0/24')
for g in [g2, g3, g4, g5]:
g1.add_peer(g)
g.add_peer(g1)
def test_08_mpath_test_check_neighbor_established(self):
g1 = self.ctns['g1']
g2 = self.ctns['g2']
g3 = self.ctns['g3']
g4 = self.ctns['g4']
g5 = self.ctns['g5']
for g in [g2, g3, g4, g5]:
g1.wait_for(expected_state=BGP_FSM_ESTABLISHED, peer=g)
def test_09_mpath_test_check_mpath_injected(self):
g1 = self.ctns['g1']
g2 = self.ctns['g2']
g3 = self.ctns['g3']
g4 = self.ctns['g4']
g5 = self.ctns['g5']
def nexthops():
n = []
for line in g1.local('ip route show 10.0.10.0/24', capture=True).split('\n'):
line = line.strip()
if 'via' in line:
n.append(line.split(' ')[2].strip())
return n
def validate_nexthops(peers):
interval = 1
count = 0
timeout = 30
while True:
valid = False
nhs = nexthops()
if len(nhs) == len(peers):
valid = True
for peer in peers:
if g1.peers[peer]['neigh_addr'].split('/')[0] not in nhs:
valid = False
break
if valid:
return
time.sleep(interval)
count += interval
if count >= timeout:
raise Exception(nhs)
validate_nexthops([g4, g5])
g4.local('gobgp g ri del 10.0.10.0/24')
validate_nexthops([g5])
g4.local('gobgp g ri add 10.0.10.0/24 local-pref 200')
validate_nexthops([g4])
g4.local('gobgp g ri del 10.0.10.0/24')
g5.local('gobgp g ri del 10.0.10.0/24')
validate_nexthops([g2, g3])
g3.local('gobgp g ri del 10.0.10.0/24')
validate_nexthops([g2])
g3.local('gobgp g ri add 10.0.10.0/24 med 10')
validate_nexthops([g2])
g2.local('gobgp g ri add 10.0.10.0/24 med 20')
validate_nexthops([g3])
if __name__ == '__main__':
output = local("which docker 2>&1 > /dev/null ; echo $?", capture=True)
if int(output) is not 0:
print "docker not found"
sys.exit(1)
nose.main(argv=sys.argv, addplugins=[OptionParser()],
defaultTest=sys.argv[0])
|
# -*- coding: utf-8 -*-
from __future__ import absolute_import
import os
import shutil
from contextlib import contextmanager
from os.path import (lexists, expanduser, isfile, islink, ismount,
realpath)
from stat import S_ISDIR, S_ISLNK, S_ISREG
import psutil
import send2trash
import xxhash
from ..init import is_os64
try:
import directio
except ImportError:
directio = None
try:
from os import scandir
except ImportError:
from scandir import scandir
_xxhash_xxh = xxhash.xxh64 if is_os64 else xxhash.xxh32
def fullpath(path):
return realpath(expanduser(path))
def fsdecode(path):
try:
upath = unicode(path)
except NameError:
upath = os.fsdecode(path)
return upath
def _stat(path):
try:
mode = os.lstat(path).st_mode
except AttributeError:
mode = os.stat(path).st_mode
link = False
else:
link = S_ISLNK(mode)
return mode, link
def splitpaths(iterable, followlinks=False):
dirs = []
files = []
links = []
nodes = []
unexs = []
for path in iterable:
try:
mode, symlink = _stat(path)
except OSError:
unexs.append(path)
else:
if S_ISDIR(mode):
if symlink and not followlinks:
continue
dirs.append(path)
elif S_ISREG(mode):
(links if symlink else files).append(path)
else:
nodes.append(path)
return dirs, files, links, nodes, unexs
def _scaniter(iterable, onerror):
while True:
try:
try:
yield next(iterable)
except StopIteration:
break
except (IOError, OSError) as exc:
if onerror is not None:
onerror(exc)
return
def _scandir(path, onerror, followlinks):
dirs = []
files = []
links = []
try:
scandir_it = scandir(path)
except (IOError, OSError) as exc:
if onerror is not None:
onerror(exc)
return
try:
for entry in _scaniter(scandir_it, onerror):
if entry.is_file(follow_symlinks=False):
files.append(entry)
elif entry.is_dir(followlinks):
dirs.append(entry)
elif entry.is_file():
links.append(entry)
return dirs, files, links
finally:
try:
scandir_it.close()
except AttributeError:
pass
def _walk(seen, path, onerror, followlinks):
if path in seen:
return
dirs, files, links = _scandir(path, onerror, followlinks)
yield dirs, files, links
seen.add(path)
#: Recurse into sub-directories
for entry in dirs:
dirpath = entry.path
if dirpath in seen:
continue
for dirs, files, links in _walk(seen, dirpath, onerror, followlinks):
yield dirs, files, links
def walk(dirname, onerror=lambda exc: None, followlinks=False, scout=None):
if not scout:
scout = set()
path = fullpath(dirname)
return _walk(scout, path, onerror, followlinks)
def mountpoint(path):
dirname = os.path.dirname
head = dirname(fullpath(path))
while not ismount(head):
head = dirname(head)
return head
def blkdevice(path):
partitions = psutil.disk_partitions()
mount = mountpoint(path)
if os.name == 'nt':
mount = mount.upper()
device = next(dp.device for dp in partitions if dp.mountpoin == mount)
block = device.rsplit('/', 1)[-1]
return block
def _readflags(sequential, direct):
flags = os.O_RDONLY
try:
flags |= os.O_BINARY
if sequential is not None:
flags |= os.O_SEQUENTIAL if sequential else os.O_RANDOM
except AttributeError:
pass
try:
if direct:
flags |= os.O_DIRECT
read = directio.read
else:
raise AttributeError
except AttributeError:
read = os.read
return read, flags
def _read(fd, fn, sequential, direct):
try:
if direct:
if sequential is not None:
fadv_sequential = os.POSIX_FADV_SEQUENTIAL
fadv_random = os.POSIX_FADV_RANDOM
advice = fadv_sequential if sequential else fadv_random
os.posix_fadvise(fd, 0, 0, advice)
def read(buf):
data = fn(fd, buf)
os.posix_fadvise(fd, read.offset, buf, os.POSIX_FADV_DONTNEED)
read.offset += buf
return data
# NOTE: `nonlocal` statement is not available in Python 2.
read.offset = 0
else:
raise AttributeError
except AttributeError:
def read(buf):
return fn(fd, buf)
return read, fd
@contextmanager
def readopen(filename, sequential=None, direct=False):
read, flags = _readflags(sequential, direct)
fd = os.open(filename, flags)
try:
yield _read(fd, read, sequential, direct)
finally:
os.close(fd)
def signature(filename):
with readopen(filename) as (read, _):
data = read(261)
return _xxhash_xxh(data).hexdigest()
def _chunksum(fd, read, size, bufsizes, whence):
buf0, buf1 = bufsizes
offset, how = whence
x = _xxhash_xxh()
update = x.update
if offset:
os.lseek(fd, offset, how)
left = size
data = read(buf0)
while left and data:
update(data)
left -= buf0
data = read(buf0)
if buf1:
data = read(buf1)
update(data)
return x.hexdigest()
def sidesum(filename, chksize, bufsize, offset=0):
if bufsize < chksize:
bufsizes = (bufsize, chksize % bufsize)
else:
bufsizes = (chksize, 0)
offset = abs(offset)
with readopen(filename, sequential=False, direct=True) as (read, fd):
whence = (offset, os.SEEK_SET)
header = _chunksum(fd, read, chksize, bufsizes, whence)
whence = (-chksize - offset, os.SEEK_END)
footer = _chunksum(fd, read, chksize, bufsizes, whence)
return header, footer
def checksum(filename, bufsize):
x = _xxhash_xxh()
update = x.update
with readopen(filename, sequential=True, direct=True) as (read, _):
data = read(bufsize)
while data:
update(data)
data = read(bufsize)
return x.hexdigest()
def remove(path, trash=False, ignore_errors=False):
if ignore_errors and not lexists(path):
return None
if islink(path):
os.unlink(path)
elif trash:
send2trash.send2trash(path)
elif isfile(path):
os.remove(path)
else:
shutil.rmtree(path, ignore_errors)
|
#!/usr/bin/env python
# Authors: Chongzhi Zang, Weiqun Peng
#
# Disclaimer
#
# This software is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# Comments and/or additions are welcome (send e-mail to:
# wpeng@gwu.edu).
#
# Version 1.1 6/9/2010
import re, os, sys, shutil
from math import *
from string import *
from optparse import OptionParser
import operator
import GenomeData
import SeparateByChrom
import Utility
def remove_redundant_1chrom_single_strand_sorted(infile, outfile, cutoff):
'''infile can only contain reads from one chromosome and only one kind of strands (+/-). file must be pre-sorted by column2, then by column3.'''
f = open(infile,'r')
o = open(outfile, 'w')
current_start = 0
current_end = 0
current_count = 1
total = 0
retained = 0
for line in f:
if not re.match("#", line):
total += 1
line = line.strip()
sline = line.split()
start = atoi(sline[1])
end = atoi(sline[2])
if start != current_start:
o.write('\t'.join(sline)+'\n')
retained += 1
current_start = start
current_end = end
current_count = 1
elif end != current_end:
o.write('\t'.join(sline)+'\n')
retained += 1
current_start = start
current_end = end
current_count = 1
else:
current_count += 1
assert current_start == start
assert current_end == end
if current_count <= cutoff:
o.write('\t'.join(sline)+'\n')
retained += 1
f.close()
o.close()
return (total, retained)
def strand_broken_remove(chrom, cutoff):
'''infile can only contain reads from one chromosome'''
infile = chrom + ".bed1";
outfile = chrom + ".bed2"
try:
if os.system('grep [[:space:]]+ %s | sort -g -k 2,3 > plus.bed1' % (infile)):
raise
except: sys.stderr.write("+ reads do not exist in " + str(infile) + "\n");
(p_total, p_retained) = remove_redundant_1chrom_single_strand_sorted('plus.bed1', 'plus_removed.bed1', cutoff)
try:
if os.system('grep [[:space:]]- %s | sort -g -k 2,3 > minus.bed1' % (infile)):
raise
except: sys.stderr.write("- reads do not exist in " + str(infile) + "\n");
(m_total, m_retained) = remove_redundant_1chrom_single_strand_sorted('minus.bed1', 'minus_removed.bed1', cutoff)
print chrom, "\tPlus reads:",p_total, "\tRetained plus reads:", p_retained, ";\tMinus reads:", m_total, "\tRetained minus reads:", m_retained;
os.system('cat plus_removed.bed1 minus_removed.bed1 > %s' % (outfile))
os.system('rm plus*.bed1')
os.system('rm minus*.bed1')
def main(argv):
parser = OptionParser()
parser.add_option("-s", "--species", action="store", type="string",
dest="species", help="species under consideration", metavar="<str>")
parser.add_option("-b", "--raw_bed_file", action="store", type="string",
dest="bed_file", help="raw bed file", metavar="<file>")
parser.add_option("-t", "--threshold", action="store", type="int",
dest="threshold", help="threshold for copy number", metavar="<int>")
parser.add_option("-o", "--output_file_name", action="store", type="string",
dest="out_file", help="output file name", metavar="<file>")
(opt, args) = parser.parse_args(argv)
if len(argv) < 8:
parser.print_help()
sys.exit(1)
if opt.species in GenomeData.species_chroms.keys():
chroms = GenomeData.species_chroms[opt.species];
else:
print "This species is not recognized, exiting";
sys.exit(1);
SeparateByChrom.separateByChrom(chroms, opt.bed_file, '.bed1')
for chrom in chroms:
if (Utility.fileExists(chrom + ".bed1")):
strand_broken_remove(chrom, opt.threshold)
SeparateByChrom.combineAllGraphFiles(chroms, '.bed2', opt.out_file)
SeparateByChrom.cleanup(chroms, '.bed1')
SeparateByChrom.cleanup(chroms, '.bed2')
if __name__ == "__main__":
main(sys.argv)
|
def notes(self, node):
pass
def properties(self, node):
pass
def property(self, node):
pass
def tag(self, node):
pass
def value(self, node):
pass
|
import seaborn as sns
import numpy as np
from pprint import pprint
from script.util.JobPool import JobPool
from script.util.PlotTools import PlotTools
import pandas
DF = pandas.DataFrame
def test_plot_tools_pair_plot():
iris = sns.load_dataset("iris")
plt_tools = PlotTools()
plt_tools.pair_plot(iris)
def test_plot_tool_to_2d_square():
plt_tools = PlotTools()
rand_x_1d = np.random.normal(3, 5, [15])
pprint(plt_tools.to_2d_square(rand_x_1d))
pprint(plt_tools.to_2d_square(rand_x_1d).shape)
rand_x_1d = np.random.normal(3, 5, [16])
pprint(plt_tools.to_2d_square(rand_x_1d))
pprint(plt_tools.to_2d_square(rand_x_1d).shape)
rand_x_1d = np.random.normal(3, 5, [17])
pprint(plt_tools.to_2d_square(rand_x_1d))
pprint(plt_tools.to_2d_square(rand_x_1d).shape)
def test_plot_tools_cluster_map():
rand_2d = np.random.normal(0, 1, [20, 20])
plt_tools = PlotTools()
plt_tools.cluster_map(rand_2d)
def test_plot_tools_heatmap():
rand_2d = np.random.normal(0, 1, [20, 20])
plt_tools = PlotTools()
plt_tools.heatmap(rand_2d)
rand_x_1d = np.random.normal(3, 5, [17])
plt_tools.heatmap(rand_x_1d)
def test_plot_tools_violine():
tips = sns.load_dataset("tips")
plt_tools = PlotTools()
plt_tools.violin_plot(tips, "day", "total_bill", with_swarmplot=True, hue="sex")
def test_plt_joint_2d():
tips = sns.load_dataset("tips")
plt_tools = PlotTools()
plt_tools.joint_2d(tips, "total_bill", "tip")
def test_plt_tool_count():
titanic = sns.load_dataset("titanic")
plt_tools = PlotTools()
plt_tools.count(titanic, column='class', hue='who')
def test_plt_line():
xs = [np.array([i * float(k) for i in range(32)]) for k in range(-10, 10)]
plt_tools = PlotTools()
plt_tools.line(xs)
def test_plt_scatter_2d():
xys = [(np.random.uniform(10, -10, [100]), np.random.normal(k, 1, [100])) for k in range(1, 20)]
plt_tools = PlotTools()
plt_tools.scatter_2d(xys)
def test_plt_dist():
rand_x_1d = np.random.normal(3, 5, [20])
plt_tools = PlotTools()
plt_tools.dist(rand_x_1d)
def test_plot_tool_timeit():
rand_x_1d = np.random.normal(3, 5, [100])
tool = PlotTools()
for i in range(10):
tool.dist(rand_x_1d)
def test_plot_tool_async_timeit():
rand_x_1d = np.random.normal(3, 5, [100])
tool = PlotTools()
pool = JobPool(processes=3)
childs = []
for i in range(10):
child = pool.apply_async(tool.dist, args=[rand_x_1d])
childs += [child]
for child in childs:
child.get()
def test_plot_font():
df = DF({'한글': [0, 1, 2, 3], 'english': [4, 5, 6, 7]})
plot = PlotTools(save=True, show=False)
corr = df.corr()
plot.heatmap(corr, title='heatmap_labeled, 한글')
|
from random import choice
class RandomWalk():
def __init__(self, num_points=5000):
"""Initalize attributes of a walk."""
self.num_points = num_points
# All walks start at (0, 0).
self.x_values = [0]
self.y_values = [0]
def get_step(self):
direction = choice([1, -1])
distance = choice([0, 1, 2, 3, 4, 5, 6, 7, 8])
return direction * distance
def fill_walk(self):
"""Calculate all the points in the walk."""
# Keep taking steps until walk reaches the desired length.
while len(self.x_values) < self.num_points:
# Decide which direction to go and how far to go in that direction
x_step = self.get_step()
y_step = self.get_step()
# Reject moves that go nowhere.
if x_step == 0 and y_step == 0:
continue
# Calculate the next steps
next_x = self.x_values[-1] + x_step
next_y = self.y_values[-1] + y_step
self.x_values.append(next_x)
self.y_values.append(next_y)
|
import pygame
from world_map.camp import Camp
from fuzy_prob.prob import Prob
from fuzy_prob import prob
class Wmap():
preprint = None
__map_x_size = 10
__map_y_size = 10
__is_generated = False
__matrix = [] #world mape matriz
def __init__(self):
pass
def init(self):
print(self.preprint + 'wmap from world_map...')
def quit(self):
print(self.preprint + 'wmap from world_map...')
def def_map_xy(self, mapx, mapy):
# argument type check
if(self.__is_generated):
raise Exception('map already generated')
elif(type(mapx) != int or type(mapy) != int):
raise TypeError('arguments just be int, but the are: ' + str(type(mapx)) + str(type(mapy)) )
elif(mapx <= 0 or mapy <= 0):
raise Exception('map size should be positive')
self.__map_x_size = mapx
self.__map_y_size = mapy
def generate_map(self):
if(self.__is_generated):
raise Exception('map is already generated')
self.__is_generated = True
print('generating world map...', end='')
for ix in range(self.__map_x_size):
line = []
for iy in range(self.__map_y_size):
coin = prob.flit_a_coin()
line.append(coin)
self.__matrix.append(line)
print(' done', end='\n\n')
world_map = Wmap();
if __name__ == '__main__':
Wmap.generate_map()
|
"""
This module is starting point for task 4.1 from Coding Campus 2018 Python course
(Dungeon Game)
"""
import logging
import threading
from .dungeon_map import DungeonMap
from .player import Player
import dungeon_game.utils as utils
import dungeon_game.log as log
from dungeon_game.decorators import log_decorator, debug_log_decorator
from dungeon_game.exceptions import InputError, MapInitError
from dungeon_game.enemy import ThreadEnemyWrapper
import dungeon_game.config as config
SAVE_PATH = "save.dat"
logger = logging.getLogger(log.LOGGER_NAME)
active_map = None
active_player = None
thread_enemy = None
def validator_map_size(string):
"""
Validator for map size input
Raises InputError with error description if string is not valid
:param string: String to check
:return: Bool, if success
"""
result = False
if string.isdigit():
size = int(string)
if 5 <= size <= 100:
result = True
else:
raise InputError("Unacceptable map size! Try again")
else:
raise InputError("Input is not integer! Try again")
return result
def validator_movement_string(string):
"""
Validator for player move direction input
Raises InputError with error description if string is not valid
:param string: String to check
:return: Bool, if success
"""
result = False
if string.lower() == "u":
result = True
elif string.lower() == "l":
result = True
elif string.lower() == "r":
result = True
elif string.lower() == "d":
result = True
else:
raise InputError("Incorrect movement direction!\nPlayer can move up [U], left [L], right [R] or down [D]\n")
return result
def validator_response(string):
"""
Validator for yes/no response input
Raises InputError with error description if string is not valid
:param string: String to check
:return: Bool, if success
"""
result = False
if string.lower() == "n" or string.lower() == "y":
result = True
else:
raise InputError("Incorrect answer!")
return result
def validator_command(string):
"""
Validator for command input
Raises InputError with error description if string is not valid
:param string: String to check
:return: Bool, if success
"""
result = False
if string.lower() == "move" or string.lower() == "save":
result = True
else:
raise InputError("Incorrect command!")
return result
@log_decorator
@debug_log_decorator
def play_game():
"""
Plays single game of Dungeon Game
:return: None
"""
while True:
player_hitpoints = active_player.get_hitpoints()
is_trap_nearby, is_treasure_nearby = active_map.check_nearby_tiles(active_player.position)
print("\n\n")
if active_player.is_dead():
logger.info("Player is killed")
print("You grow weak and fall to the cold floor, dropping your bag beside you. You feel you can't go anymore further.")
print("Dungeon consumes you!")
break
if is_trap_nearby:
print("Your senses detect a trap in nearby tile. Watch your step!")
if is_treasure_nearby:
print("Radiant glow gives away treasure's location in nearby tile. Target is close!")
logger.info(f"Player position {active_player.position}")
print(f"Your position is ({active_player.position[1]}, {active_player.position[0]})")
print(f"Your health is {player_hitpoints} HP")
print(f"Your bag has space for {config.PLAYER_BAG_SIZE - active_player.bag } more chests.\n")
command = utils.get_input(validator_command, "Input command [Save] or [Move]: ")
if command.lower() == "save":
if utils.save_game(SAVE_PATH, active_map, active_player):
print("Game saved!")
else:
print("Save failed!")
continue
while True:
move_direction = utils.get_input(validator_movement_string, "Input direction to move in: ")
if active_player.move(move_direction.lower(), active_map):
logger.info(f"Player moves {active_player.position[1]}, {active_player.position[0]}")
break
else:
print("You can't move there! Try again\n")
is_trap, is_treasure = active_map.check_current_tile(active_player.position)
if is_trap:
logger.info(f"Player is damaged by 1 hitpoint. Hitpoints left: {player_hitpoints - 1}")
print("You spring the trap and it firmly clenches around your leg.")
active_player.decrease_hitpoints()
if active_player.is_dead():
logger.info("Player is killed")
print("You loose too much blood trying to remove it and give in to darkness.")
print("Dungeon consumes you!")
break
else:
print("You manage to remove the trap suffering minor wounds.")
active_map.game_map[active_player.position[0]][active_player.position[1]] = DungeonMap.SYMBOL_PLAYER
if is_treasure:
logger.info(f"Player found the treasure. Current bag size: {active_player.bag + 1}")
print("Shimmering glow and warmth leads you to open chest filled with gold. You fill your bag with contents of the chest.")
active_player.bag += 1
if active_player.is_bag_full():
logger.info("Player filled the bag")
print("Feeling you can't carry anymore, you backtrack to dungeon exit with the bag full of gold.")
print("You escaped the dungeon!")
break
else:
print("Your bag feels a little heavier, but there is still space for more. You continue your journey.")
active_map.game_map[active_player.position[0]][active_player.position[1]] = DungeonMap.SYMBOL_PLAYER
active_player.mark_last_pos(active_map)
logger.info("Game over")
@log_decorator
@debug_log_decorator
def init_game():
"""
Initializes game map and player position
:return: None
"""
global active_map
global active_player
while True:
map_size = utils.get_input(validator_map_size, "Input map size [5 - 100]: ")
try:
active_map = DungeonMap(int(map_size))
except MapInitError as exc:
print(f"Failed to create map! Error: f{str(exc)}")
else:
break
active_player = Player(active_map)
@log_decorator
@debug_log_decorator
def start_game():
"""
Start game dialog
:return: None
"""
global active_map
global active_player
global thread_enemy
active_map = DungeonMap(0, False)
active_player = Player(active_map)
response = utils.get_input(validator_response, "Do you wish to load last saved game? [Y\\N]: ")
if response.lower() == 'y':
if not utils.load_game(SAVE_PATH, active_map, active_player):
print("Failed to load saved game! Starting new game")
init_game()
else:
logger.info("Starting new game.")
init_game()
thread_enemy = ThreadEnemyWrapper(active_map, active_player)
thread_enemy.start()
def run():
"""
Runs game
:return: None
"""
print("Welcome to Dungeon Game!")
log.init_logger()
while True:
start_game()
play_game()
print("\nGAME OVER\n")
active_map.print_map()
thread_enemy.join()
response = utils.get_input(validator_response, "Try again? [Y\\N]: ")
if response.lower() == 'n':
break
else:
logger.info("User is restarting the game")
logger.info("User quit the game")
print("\nQuitting game...")
|
import indexTricks as iT
import numpy
from pylens import *
from imageSim import profiles,convolve,SBModels, models
import distances as D
import cPickle
import indexTricks as iT
import numpy,pylab
#from imageSim import profiles,convolve,models
import pylab as plt
from Surveys import Survey
from StochasticObserving import SO
from SignaltoNoise import S2N
class FastLensSim(SO, S2N):
def __init__(self, surveyname, fractionofseeing = 1):
#setting surveyname to be the surveyname and declaring the parameters to be the same
### Read in survey
self.surveyName = surveyname
survey=Survey(surveyname) #This stores typical survey in Surveys.Survey
self.survey = Survey(surveyname)
self.pixelsize = self.survey.pixelsize #let self.pixelsixe be equal to the pixelsize of survey
self.side = self.survey.side
self.readnoise = self.survey.readnoise
self.nexposures = self.survey.nexposures
self.f_sky = self.survey.f_sky
self.bands = self.survey.bands
self.strategy = self.survey.strategy
self.strategyx = self.survey.strategyx
self.exposuretimes = {}
self.zeropoints = {}
self.stochasticobservingdata = {}
self.gains = {}
self.seeing = {}
self.psfscale = {}
self.psf = {}
self.psfFFT = {}
self.ET = {}
self.SB = {}
#for i in range of the length of bands in survey, set the parameters to self.parameter
for i in range(len(survey.bands)):
self.exposuretimes[survey.bands[i]] = survey.exposuretimes[i]
self.zeropoints[survey.bands[i]] = survey.zeropoints[i]
self.gains[survey.bands[i]] = survey.gains[i]
self.stochasticobservingdata[survey.bands[i]] = survey.stochasticobservingdata[i]
self.zeroexposuretime = survey.zeroexposuretime
###do some setup
self.xl = (self.side - 1.)/2.
self.yl = (self.side - 1.)/2.
self.x, self.y = iT.coords((self.side, self.side)) #creating an array of the sides coordinates
self.r2 = (self.x - self.xl) ** 2 + (self.y - self.yl) ** 2 # r squared (r2) = distance between (x,y) and (xl, yl)
self.pixelunits = False
self.Reset() #resets all parameters as seen in definition below
#_____________________________________________________________________________________________________________________-
def Reset(self):
self.sourcenumbers = []
#Some objects that need pre-defining as dictionaries
self.magnification = {}
self.image = {}
self.sigma = {}
self.residual = {}
self.zeroMagCounts = {}
self.xSum = {}
self.ySum = {}
self.ms = {}
self.qs = {}
self.ps = {}
self.rs = {}
self.ns = {}
self.bl = {}
self.src = {}
self.galModel = {}
self.sourceModel = {}
self.model = {}
self.totallensedsrcmag={}
self.fakeLens={}
self.fakeResidual={}
self.fakeResidual[0]={}
self.SN={}
self.SNRF={}
self.convolvedsrc={}
self.convolvedGal={}
#______________________________________________________________________________________________________________
def trytoconvert(self,par,p):
try:
return par/p
except NameError:
print "Warning one of the parameters is not defined"
#_______________________________________________________________________________________________________________
def setLensPars(self, m, r, q, n = 4, pixelunits = False, reset = True, xb = 0, xp = 0, jiggle = 0):
if reset: self.Reset() #if reset is true, reset all parameters
self.rl={} #define rl variable as a dictionary
if pixelunits == False: #if there is no pixelunits
# loop to fill rl array with bands and pixelsizes
for band in r.keys(): #if band is within the dictionary r keys, then...
self.rl[band] = self.trytoconvert(r[band], self.pixelsize) # the rl band array is set to be the rband captured by the trytoconvert method
self.ml = m # defining an array of band, pixelsize where ml is nodel?
self.ql = q
self.deltaxl = (numpy.random.rand() - 0.5) * 2 * jiggle #delta xl coordinate = 0, when jiggle = 0
self.deltayl = (numpy.random.rand() - 0.5) * 2 * jiggle #delta yl coordinate, when jiggle = 0
if jiggle != 0:
self.deltap = 0.0 + (numpy.random.rand() - 0.5) * 180 # if jiggle is not 0, then delta p is numpy.random.rand()-0.5)*180
n = (numpy.random.rand() + 1) * 4
else:
self.deltap = 0.0
self.nl = n
# defining gal
self.gal = SBModels.Sersic('gal',{'x':self.xl + self.deltaxl,
'y':self.yl + self.deltayl,
'q':self.ql,
'pa':90+self.deltap,
're':self.rl[band],
'n':self.nl})
#defining xb and xp as themselves in this class
self.xb = xb
self.xp = xp
#_________________________________________________________________________________________________________
def setSourcePars( self, b, m, x, y, q, p, r, n = 1, pixelunits = False, sourcenumber = 1):
if pixelunits == False: # if pixelunits doesnt exist
x = self.trytoconvert(x, self.pixelsize) # x is the result of trytoconvert function where par is x, and p is the pixelsize
y = self.trytoconvert(y, self.pixelsize) # y is the result of trytoconvert function where par is y, and p is the pixelsize
r = self.trytoconvert(r, self.pixelsize) # r is the result of trytoconvert function where par is r, and p is the pixelsize
b = self.trytoconvert(b, self.pixelsize) # b is the result of trytoconvert function where par is b, and p is the pixelsize
self.xSum[sourcenumber] = x + self.xl + self.deltaxl + 0.000001 # xsum with a sourcenumber of 1 is the sum of all x
self.ySum[sourcenumber] = y + self.yl + self.deltayl + 0.000001 # ysum with a sourcenumber of 1 is the sum of all y
self.ms[sourcenumber] = m
self.qs[sourcenumber] = q
self.ps[sourcenumber] = p
self.rs[sourcenumber] = r
self.ns[sourcenumber] = n
self.bl[sourcenumber] = b
# The identified sourcenumber in the source dictionary is stated.
# where x = xSum, y = ySum, q = qs, pa = ps, re =rs, n=ns where sourcenumber is the key
self.src[sourcenumber] = SBModels.Sersic('src%i'%sourcenumber, {'x':self.xSum[sourcenumber],
'y':self.ySum[sourcenumber],
'q':self.qs[sourcenumber],
'pa':self.ps[sourcenumber],
're':self.rs[sourcenumber],
'n':self.ns[sourcenumber]})
# sourcenumbers array increases with source number
self.sourcenumbers.append(sourcenumber)
# sourceModel, totallensedssrcmag, fakeResidual, SN, SNRF, convolvedsrc using the
# input of sourcenumber is now a dictonary
self.sourceModel[sourcenumber] = {}
self.totallensedsrcmag[sourcenumber] = {}
self.fakeResidual[sourcenumber] = {}
self.SN[sourcenumber] = {}
self.SNRF[sourcenumber] = {}
self.convolvedsrc[sourcenumber] = {}
#______________________________________________________________________________________________________________________
def lensASource(self,sourcenumber,bands):
# src is created as a sourcenumber
src = self.src[sourcenumber]
# lens is a powerlaw instance of massmodel which includes x, y, q, pa, b, eta
lens = massmodel.PowerLaw('lens',{},{'x':self.xl+self.deltaxl,
'y':self.yl+self.deltayl,
'q':self.ql,
'pa':90+self.deltap,
'b':self.bl[sourcenumber],
'eta':1})
# es is a ExtShear instance of massmodel. Not sure what ExtShear is
es = massmodel.ExtShear('lens',{},{'x':self.xl+self.deltaxl,
'y':self.yl+self.deltayl,
'pa':self.xp,
'b':self.xb})
# list lenses created including lens and es
lenses = [lens, es]
a = 51
# makes array of ox, and oy which shows the shape of the array as a float
ox , oy = iT.coords((a,a))
# ps is a float which is calculated by taking the rs value and multiplying it by (10.0/a)
ps = (self.rs[sourcenumber] * (10.0 / a))
ox = (ox - (a - 1) / 2.0) * ps + (self.xSum[sourcenumber]) # ox are arrays in which values are calculates as ox-(a-1)/2 + the xSum
oy = (oy - (a - 1) / 2.0) * ps + (self.ySum[sourcenumber]) # oy are arrays in which values are calculates as oy-(a-1)/2 + the ySum
unlensedSourceModel = (src.pixeval( ox, oy, csub = 5) * ( ps ** 2)).sum() #sum of pixelvalues of souces
sourceNorm = unlensedSourceModel.sum() # sum of unlensedSourceModel
unlensedSourceModel /= sourceNorm # unlensedSourceModel = unlensedSourceModel / sourceNorm
#creating a model for the lenses
sourceModel = pylens.lens_images(lenses, src, [self.x,self.y], getPix = True, csub = 5)[0]
sourceModel[sourceModel < 0] = 0 # if the sourceModel is < 0,set it to be 0
sourceModel /= sourceNorm #sourceModel = sourceModel / sourceNorm
#creating a magnification for each source
self.magnification[sourcenumber] = (numpy.sum(numpy.ravel(sourceModel)) / numpy.sum(numpy.ravel(unlensedSourceModel)))
sm = {}
#for each band that is seen in the bands list:
# unlensedTotalSourceFlux is calculated as flux of the a band with regards to the sourcenumber,
# and that same band at the zeropoint
# important to note: flux = 10 ** (0.4 *( m2 - m1)), where m2 is the zeropoints
# or fluw = 10 ** (-(m1 - m2) / 2.5)
for band in bands:
unlensedtotalsrcflux = 10 **(-(self.ms[sourcenumber][band] - self.zeropoints[band]) / 2.5)
sm[band] = sourceModel * unlensedtotalsrcflux
if sm[band].max() > 0:
# if the maximum value in sm[band] > 0, then the magnitude calculated
# magnitude = m1/m2 ((m1/m2) from calculations of flux in comments above)
self.totallensedsrcmag[sourcenumber][band] = -2.5 * numpy.log10(sm[band].sum()) + self.zeropoints[band]
else:
# else magnitude = 99
self.totallensedsrcmag[sourcenumber][band] = 99
return sm
#_____________________________________________________________________________________________________________________
# defining a function to Evaluate a Galaxy
def EvaluateGalaxy(self, light, mag, bands):
model = {} # model is defined
lightMag = light.pixeval(self.x, self.y, csub = 5) # the magnitude of light in the pixels are calculated in lightMag array
lightMag [lightMag < 0] = 0 # set the lightMag to if it is less than 0
lightMag /= lightMag.sum() # lightMag = lightMag / sum of the lightMag
# for each band that is seen in the bands list:
# calculat the flux in each band regarding the magnitude
# a model array calculated
for band in bands:
flux = 10 ** (-(mag[band] - self.zeropoints[band]) /2.5)
model[band] = lightMag * flux
return model
#______________________________________________________________________________________________________________________
def MakeModel(self, bands):
#did you know that self.gal is actually fixed for all bands currently?
self.galModel = self.EvaluateGalaxy(self.gal,self.ml,bands)
for sourcenumber in self.sourcenumbers:
self.sourceModel[sourcenumber] = self.lensASource(sourcenumber,bands)
# sourceModel is set to be the lensASource function including sourcenumber and bands
for band in bands:
self.model[band] = self.galModel[band] * 1
for sourcenumber in self.sourcenumbers:
self.model[band] += self.sourceModel[sourcenumber][band]
#_________________________________________________________________________________________________________________________
def ObserveLens(self, noisy = True, bands = []):
if bands == []:
bands = self.bands # if bands exists, then bands is set to be the bands
for band in bands:
if self.seeing[band] == 0:
# convolvedGalaxy and the psfForwardFourierTransform is the convolution of the galaxyModel and the psf from convolve.py and edgeCheck is True
convolvedGalaxy, self.psfFFT[band] = convolve.convolve(self.galModel[band], self.psf[band], True)
convolvedGalaxy[convolvedGalaxy <0] = 0
self.convolvedGal[band] = convolvedGalaxy
convolvedmodel = convolvedGalaxy * 1
convolvedsrc = {}
for sourcenumber in self.sourcenumbers:
convolvedsrc[sourcenumber] = convolve.convolve(self.sourceModel[sourcenumber][band], self.psfFFT[band], False)[0]
convolvedsrc[sourcenumber][convolvedsrc[sourcenumber]<0]=0
self.convolvedsrc[sourcenumber][band]=convolvedsrc[sourcenumber]
convolvedmodel+=convolvedsrc[sourcenumber]
self.zeroMagCounts[band]=(10**(-(0-self.zeropoints[band])/2.5))
exposurecorrection=((self.ET[band]*1./self.zeroexposuretime))*self.gains[band]
convolvedmodel*=exposurecorrection
#skybackground per second per square arcsecond
background=(10**(-(self.SB[band]-self.zeropoints[band])/2.5))*(self.pixelsize**2)
tot_bg=background*exposurecorrection
sigma=((convolvedmodel+tot_bg)+self.nexposures*(self.readnoise**0.5)**2)**.5
fakeLens=convolvedmodel*1.
if noisy:fakeLens+=(numpy.random.randn(self.side,self.side)*(sigma))
#convert back to ADU/second:
fakeLens/=exposurecorrection
sigma/=exposurecorrection
self.image[band]=fakeLens*1
self.fakeLens[band]=fakeLens*1
self.sigma[band]=sigma*1
self.fakeResidual[0][band]=fakeLens-convolvedGalaxy
for sourcenumber in self.sourcenumbers:
self.SN[sourcenumber][band]=self.SNfunc(\
convolvedsrc[sourcenumber],sigma)
self.fakeResidual[sourcenumber][band]=\
fakeLens-convolvedmodel+convolvedsrc[sourcenumber]
#===========================================================================
def loadModel(self,ideallens):
if ideallens is not None:
self.galModel,self.sourceModel,self.model,self.magnification,self.totallensedsrcmag=ideallens
self.image=self.model
#===========================================================================
def loadConvolvedModel(self,ideallens):
self.galModel,self.sourceModel,self.model,self.magnification,self.totallensedsrcmag=ideallens
self.image=self.model
#===========================================================================
def makeLens(self, stochastic=True, save=False, noisy=True, stochasticmode="MP", SOdraw=[], bands=[], musthaveallbands=False, MakeModel=True):
if stochastic==True:
self.stochasticObserving(mode=stochasticmode, SOdraw=SOdraw, musthaveallbands=musthaveallbands)
if self.seeingtest=="Fail":
return None
if bands==[]:
bands=self.bands
if MakeModel:
self.MakeModel(bands)
if self.strategy=="resolve" and stochastic==True:
self.stochasticObserving(mode=stochasticmode,SOdraw=SOdraw) #have to rerun stochastic observing now we know the magnification
self.ObserveLens(noisy=noisy)
return [self.galModel, self.sourceModel, self.model, self.magnification, self.totallensedsrcmag]
#===========================================================================
def makeColorLens(self,bands=["g_SDSS","r_SDSS","i_SDSS"],recolourize=True):
if self.surveyName=="Euclid" and bands==["g_SDSS","r_SDSS","i_SDSS"]:
bands=["VIS","VIS","VIS"]
import colorImage
goodbands=[]
for band in bands:
try:
self.image[band]
goodbands.append(band)
except KeyError:
pass
bands=goodbands
if len(bands)==1:
bands=[bands[0],bands[0],bands[0]]
if len(bands)==2:
bands=[bands[0],"dummy",bands[1]]
self.ml["dummy"]=(self.ml[bands[0]]+self.ml[bands[2]])/2
self.image["dummy"]=(self.image[bands[0]]+self.image[bands[2]])/2
if recolourize:
self.color = colorImage.ColorImage()
self.color.bMinusr=(self.ml[bands[0]]-self.ml[bands[2]])/4.
self.color.bMinusg=(self.ml[bands[0]]-self.ml[bands[1]])/4.
self.color.nonlin=4.
self.colorimage = self.color.createModel(\
self.image[bands[0]],self.image[bands[1]],self.image[bands[2]])
else:
self.colorimage = self.color.colorize(\
self.image[bands[0]],self.image[bands[1]],self.image[bands[2]])
return self.colorimage
#===========================================================================
def display(self,band="g_SDSS",bands=["g_SDSS","r_SDSS","i_SDSS"]):
if self.surveyName=="Euclid":bands=["VIS","VIS","VIS"]
import pylab as plt
plt.ion()
plt.figure(1)
plt.imshow(self.makeColorLens(bands=bands),interpolation="none")
plt.figure(2)
import colorImage
self.color = colorImage.ColorImage()#sigma-clipped single band residual
plt.imshow(self.color.createModel(self.fakeResidual[0][band],self.fakeResidual[0][band],self.fakeResidual[0][band])[:,:,0],interpolation="none")
plt.figure(3)
plt.imshow(self.fakeResidual[0][band],interpolation="none")
try:
self.fakeResidual[1]["RF"]
plt.figure(4)
plt.imshow(self.fakeResidual[1]["RF"],interpolation="none")
except KeyError: pass
plt.draw()
raw_input()
plt.ioff()
#===========================================================================
def Rank(self,mode,band="g_SDSS",bands=["g_SDSS","r_SDSS","i_SDSS"]):
import pylab as plt
plt.ion()
rank="d"
while rank not in ["0","1","2","3","4","-1","-2","-3"]:
if mode=="colour":
plt.imshow(self.makeColorLens(bands=bands),interpolation="none")
plt.draw()
if mode=="rf":
plt.imshow(self.fakeResidual[0]["RF"],interpolation="none")
plt.draw()
if mode=="best":
plt.imshow(self.fakeResidual[0][band],interpolation="none")
plt.draw()
rank=raw_input()
if rank=="":rank="0"
plt.ioff()
return rank
#===========================================================================
|
# -*- coding: utf-8 -*-
"""
Azure Resource Manager (ARM) Container Instance Group Execution Module
.. versionadded:: 3.0.0
:maintainer: <devops@eitr.tech>
:configuration: This module requires Azure Resource Manager credentials to be passed as keyword arguments
to every function or via acct in order to work properly.
Required provider parameters:
if using username and password:
* ``subscription_id``
* ``username``
* ``password``
if using a service principal:
* ``subscription_id``
* ``tenant``
* ``client_id``
* ``secret``
Optional provider parameters:
**cloud_environment**: Used to point the cloud driver to different API endpoints, such as Azure GovCloud.
Possible values:
* ``AZURE_PUBLIC_CLOUD`` (default)
* ``AZURE_CHINA_CLOUD``
* ``AZURE_US_GOV_CLOUD``
* ``AZURE_GERMAN_CLOUD``
"""
# Python libs
import logging
# Azure libs
HAS_LIBS = False
try:
import azure.mgmt.containerinstance # pylint: disable=unused-import
from msrestazure.azure_exceptions import CloudError
from msrest.exceptions import SerializationError
HAS_LIBS = True
except ImportError:
pass
__func_alias__ = {"list_": "list"}
log = logging.getLogger(__name__)
def __virtual__(hub):
"""
Only load when Azure SDK imports successfully.
"""
return HAS_LIBS
async def create_or_update(
hub,
ctx,
name,
resource_group,
containers,
os_type,
restart_policy="OnFailure",
identity=None,
image_registry_credentials=None,
ip_address=None,
volumes=None,
diagnostics=None,
network_profile=None,
dns_config=None,
sku=None,
encryption_properties=None,
init_containers=None,
tags=None,
**kwargs,
):
"""
.. versionadded:: 3.0.0
Create or update container groups with specified configurations. This is an EXTREMELY complex module. I wouldn't
recommend attempting to use this on the command line...
Consult the `SDK documentation <https://docs.microsoft.com/en-us/python/api/azure-mgmt-containerinstance/azure.mgmt.containerinstance.models.containergroup?view=azure-python>`__
for more information about the objects passed to the parameters in this module.
:param name: The name of the container group.
:param resource_group: The name of the resource group to which the container group belongs.
:param containers: A list of the containers within the container group. The following are possible parameters for
the containers:
- **name**: Required. The user-provided name of the container instance.
- **image**: Required. The name of the image used to create the container instance.
- **resources**:
- **requests**:
- **memory_in_gb**: Required. The memory request in GB of this container instance.
- **cpu**: Required. The CPU request of this container instance.
- **gpu**: The GPU request of this container instance.
- **limits**:
- **memory_in_gb**: The memory limit in GB of this container instance.
- **cpu**: The CPU limit of this container instance.
- **gpu**: The GPU limit of this container instance.
- **command**: A list of commands to execute within the container instance in exec form.
- **ports**: A list of the dictionaries of exposed ports on the container instance
(i.e., ``{"protocol": "TCP", "port": 80}``).
- **environment_variables**: A list of environment variables to set in the container instance.
- **name**: Required if environment_variables is used. The name of the environment variable.
- **value**: The value of the environment variable.
- **secure_value**: The value of the secure environment variable.
- **volume_mounts**: A list of volume mounts available to the container instance.
- **name**: Required if volume_mounts is used. The name of the volume mount.
- **mount_path**: Required if volume_mounts is used. The path within the container where the volume should
be mounted. Must not contain colon (:).
- **read_only**: Boolean flag indicating whether the volume mount is read-only.
- **liveness_probe**:
- **exec_property**:
- **command**: The commands to execute within the container.
- **http_get**:
- **path**: The path to probe.
- **port**: Required if http_get is used. The port number to probe.
- **scheme**: The scheme. Possible values include: 'http', 'https'.
- **initial_delay_seconds**: The initial delay seconds.
- **period_seconds**: The period seconds.
- **failure_threshold**: The failure threshold.
- **success_threshold**: The success threshold.
- **timeout_seconds**: The timeout seconds.
- **readiness_probe**:
- **exec_property**:
- **command**: The commands to execute within the container.
- **http_get**:
- **path**: The path to probe.
- **port**: Required if http_get is used. The port number to probe.
- **scheme**: The scheme. Possible values include: 'http', 'https'
- **initial_delay_seconds**: The initial delay seconds.
- **period_seconds**: The period seconds.
- **failure_threshold**: The failure threshold.
- **success_threshold**: The success threshold.
- **timeout_seconds**: The timeout seconds.
:param os_type: The operating system type required by the containers in the container group. Possible values
include: 'Windows', 'Linux'.
:param restart_policy: Restart policy for all containers within the container group. Possible values are:
- ``Always``: Always restart.
- ``OnFailure``: Restart on failure.
- ``Never``: Never restart.
:param identity: A dictionary defining a ContainerGroupIdentity object which represents the identity for the
container group.
:param image_registry_credentials: A list of dictionaries defining ImageRegistryCredential objects for the image
registry credentials.
:param ip_address: A dictionary defining an IpAddress object which represents the IP address for the container
group. Possible keys are:
- ``ports``: The list of ports exposed on the container group. Required if ip_address is used.
- ``type``: Specifies if the IP is exposed to the public internet or private VNET. Required if ip_address is
used. Possible values include: 'Public', 'Private'.
- ``ip``: The IP exposed to the public internet.
- ``dns_name_label``: The Dns name label for the IP.
:param volumes: The list of dictionaries representing Volume objects that can be mounted by containers in this
container group.
:param diagnostics: A dictionary defining a ContainerGroupDiagnostics object which represents the diagnostic
information for the container group.
:param network_profile: A dictionary defining a ContainerGroupNetworkProfile object which represents the network
profile information for the container group.
:param dns_config: A dictionary defining a DnsConfiguration object which represents the DNS config information for
the container group.
:param sku: The SKU for a container group. Possible values include: 'Standard', 'Dedicated'
:param encryption_properties: A dictionary defining an EncryptionProperties object which represents the encryption
properties for the container group.
:param init_containers: A list of dictionaries defining InitContainerDefinition objects which represent the init
containers for the container group.
:param tags: The tags of the resource.
"""
result = {}
if "location" not in kwargs:
rg_props = await hub.exec.azurerm.resource.group.get(
ctx, resource_group, **kwargs
)
if "error" in rg_props:
log.error("Unable to determine location from resource group specified.")
return {
"error": "Unable to determine location from resource group specified."
}
kwargs["location"] = rg_props["location"]
conconn = await hub.exec.azurerm.utils.get_client(
ctx, "containerinstance", **kwargs
)
try:
grpmodel = await hub.exec.azurerm.utils.create_object_model(
"containerinstance",
"ContainerGroup",
containers=containers,
os_type=os_type,
restart_policy=restart_policy,
identity=identity,
image_registry_credentials=image_registry_credentials,
ip_address=ip_address,
volumes=volumes,
diagnostics=diagnostics,
network_profile=network_profile,
dns_config=dns_config,
sku=sku,
encryption_properties=encryption_properties,
init_containers=init_containers,
tags=tags,
**kwargs,
)
except TypeError as exc:
result = {
"error": "The object model could not be built. ({0})".format(str(exc))
}
return result
try:
grp = conconn.container_groups.create_or_update(
container_group_name=name,
resource_group_name=resource_group,
container_group=grpmodel,
)
grp.wait()
result = grp.result().as_dict()
except (CloudError, SerializationError) as exc:
await hub.exec.azurerm.utils.log_cloud_error(
"containerinstance", str(exc), **kwargs
)
result = {"error": str(exc)}
return result
async def update(
hub, ctx, name, resource_group, tags=None, **kwargs,
):
"""
.. versionadded:: 3.0.0
Updates container group tags with specified values.
:param name: The name of the container group.
:param resource_group: The name of the resource group to which the container group belongs.
:param tags: The tags of the resource.
CLI Example:
.. code-block:: bash
azurerm.containerinstance.group.update containergroup resourcegroup tags='{"owner": "me"}'
"""
result = {}
conconn = await hub.exec.azurerm.utils.get_client(
ctx, "containerinstance", **kwargs
)
try:
grp = conconn.container_groups.update(
container_group_name=name, resource_group_name=resource_group, tags=tags,
)
result = grp.as_dict()
except (CloudError, SerializationError) as exc:
await hub.exec.azurerm.utils.log_cloud_error(
"containerinstance", str(exc), **kwargs
)
result = {"error": str(exc)}
return result
async def get(hub, ctx, name, resource_group, **kwargs):
"""
.. versionadded:: 3.0.0
Gets the properties of the specified container group in the specified subscription and resource group. The operation
returns the properties of each container group including containers, image registry credentials, restart policy, IP
address type, OS type, state, and volumes.
:param name: The name of the container group.
:param resource_group: The name of the resource group to which the container group belongs.
CLI Example:
.. code-block:: bash
azurerm.containerinstance.group.get containergroup resourcegroup
"""
result = {}
conconn = await hub.exec.azurerm.utils.get_client(
ctx, "containerinstance", **kwargs
)
try:
ret = conconn.container_groups.get(
container_group_name=name, resource_group_name=resource_group
)
result = ret.as_dict()
except CloudError as exc:
await hub.exec.azurerm.utils.log_cloud_error(
"containerinstance", str(exc), **kwargs
)
result = {"error": str(exc)}
return result
async def list_(hub, ctx, resource_group=None, **kwargs):
"""
.. versionadded:: 3.0.0
Get a list of container groups in the specified subscription. This operation returns properties of each container
group including containers, image container credentials, restart policy, IP address type, OS type, state, and
volumes.
:param resource_group: The name of the resource group to limit the results.
CLI Example:
.. code-block:: bash
azurerm.containerinstance.group.list
"""
result = {}
conconn = await hub.exec.azurerm.utils.get_client(
ctx, "containerinstance", **kwargs
)
try:
if resource_group:
groups = await hub.exec.azurerm.utils.paged_object_to_list(
conconn.container_groups.list_by_resource_group(
resource_group_name=resource_group
)
)
else:
groups = await hub.exec.azurerm.utils.paged_object_to_list(
conconn.container_groups.list()
)
for group in groups:
result[group["name"]] = group
except (CloudError, Exception) as exc:
await hub.exec.azurerm.utils.log_cloud_error(
"containerinstance", str(exc), **kwargs
)
result = {"error": str(exc)}
return result
async def delete(hub, ctx, name, resource_group, **kwargs):
"""
.. versionadded:: 3.0.0
Delete the specified container group in the specified subscription and resource group. The operation does not delete
other resources provided by the user, such as volumes.
:param name: The name of the container group.
:param resource_group: The name of the resource group to which the container group belongs.
CLI Example:
.. code-block:: bash
azurerm.containerinstance.group.delete containergroup resourcegroup
"""
result = False
conconn = await hub.exec.azurerm.utils.get_client(
ctx, "containerinstance", **kwargs
)
try:
ret = conconn.container_groups.delete(
container_group_name=name, resource_group_name=resource_group
)
ret.wait()
result = True
except CloudError as exc:
await hub.exec.azurerm.utils.log_cloud_error(
"containerinstance", str(exc), **kwargs
)
result = {"error": str(exc)}
return result
async def restart(hub, ctx, name, resource_group, **kwargs):
"""
.. versionadded:: 3.0.0
Restarts all containers in a container group in place. If container image has updates, new image will be downloaded.
:param name: The name of the container group.
:param resource_group: The name of the resource group to which the container group belongs.
CLI Example:
.. code-block:: bash
azurerm.containerinstance.group.restart containergroup resourcegroup
"""
result = {}
conconn = await hub.exec.azurerm.utils.get_client(
ctx, "containerinstance", **kwargs
)
try:
ret = conconn.container_groups.restart(
container_name=name, resource_group_name=resource_group
)
result = ret.as_dict()
except CloudError as exc:
await hub.exec.azurerm.utils.log_cloud_error(
"containerinstance", str(exc), **kwargs
)
result = {"error": str(exc)}
return result
async def start(hub, ctx, name, resource_group, **kwargs):
"""
.. versionadded:: 3.0.0
Starts all containers in a container group. Compute resources will be allocated and billing will start.
:param name: The name of the container group.
:param resource_group: The name of the resource group to which the container group belongs.
CLI Example:
.. code-block:: bash
azurerm.containerinstance.group.start containergroup resourcegroup
"""
result = {}
conconn = await hub.exec.azurerm.utils.get_client(
ctx, "containerinstance", **kwargs
)
try:
ret = conconn.container_groups.start(
container_name=name, resource_group_name=resource_group
)
result = ret.as_dict()
except CloudError as exc:
await hub.exec.azurerm.utils.log_cloud_error(
"containerinstance", str(exc), **kwargs
)
result = {"error": str(exc)}
return result
async def stop(hub, ctx, name, resource_group, **kwargs):
"""
.. versionadded:: 3.0.0
Stops all containers in a container group. Compute resources will be deallocated and billing will stop.
:param name: The name of the container group.
:param resource_group: The name of the resource group to which the container group belongs.
CLI Example:
.. code-block:: bash
azurerm.containerinstance.group.stop containergroup resourcegroup
"""
result = {}
conconn = await hub.exec.azurerm.utils.get_client(
ctx, "containerinstance", **kwargs
)
try:
ret = conconn.container_groups.stop(
container_name=name, resource_group_name=resource_group
)
result = ret.as_dict()
except CloudError as exc:
await hub.exec.azurerm.utils.log_cloud_error(
"containerinstance", str(exc), **kwargs
)
result = {"error": str(exc)}
return result
|
from alexandria.settings.commands import remove_activation
from alexandria.settings.extensions import db
from datetime import datetime,timedelta
def test_remove_activation_expired(client):
from alexandria.modules.security.models import User,AccountRegistration
days_to_expire6,days_to_expire8 = timedelta(days=6),timedelta(days=8)
user1 = User(username='test_activation',joined_date=datetime.now()- days_to_expire6)
user2 = User(username='test_activation2',joined_date=datetime.now()- days_to_expire8)
activation1 = AccountRegistration(user=user1)
activation2 = AccountRegistration(user=user2)
db.session.add_all([activation1,activation2])
remove_activation() # do commmit to the datebase
assert db.session.query(User.query.filter(User.username == 'test_activation').exists()).scalar()
assert not db.session.query(User.query.filter(User.username == 'test_activation2').exists()).scalar()
|
symbols = []
exports = [{'type': 'function', 'name': 'AssocCreateForClasses', 'address': '0x7ffb39c36d10'}, {'type': 'function', 'name': 'AssocGetDetailsOfPropKey', 'address': '0x7ffb3a093050'}, {'type': 'function', 'name': 'AssocShouldProcessUseAppToAppLaunching', 'address': '0x7ffb39eb2a20'}, {'type': 'function', 'name': 'CCachedShellItem_CreateInstance', 'address': '0x7ffb39c951e0'}, {'type': 'function', 'name': 'CCollectionFactory_CreateInstance', 'address': '0x7ffb39d7b5a0'}, {'type': 'function', 'name': 'CDesktopFolder_CreateInstanceWithBindContext', 'address': '0x7ffb39cb5f40'}, {'type': 'function', 'name': 'CFSFolder_AdjustForSlowColumn', 'address': '0x7ffb39fe9940'}, {'type': 'function', 'name': 'CFSFolder_CreateFolder', 'address': '0x7ffb39cbcb90'}, {'type': 'function', 'name': 'CFSFolder_IsCommonItem', 'address': '0x7ffb39d7e600'}, {'type': 'function', 'name': 'CFileOperationRecorder_CreateInstance', 'address': '0x7ffb3a003020'}, {'type': 'function', 'name': 'CFreeThreadedItemContainer_CreateInstance', 'address': '0x7ffb39cea1c0'}, {'type': 'function', 'name': 'CMruLongList_CreateInstance', 'address': '0x7ffb3a0047f0'}, {'type': 'function', 'name': 'CPrivateProfileCache_Save', 'address': '0x7ffb39d82fc0'}, {'type': 'function', 'name': 'CRegFolder_CreateAndInit', 'address': '0x7ffb39d7e3a0'}, {'type': 'function', 'name': 'CRegFolder_CreateInstance', 'address': '0x7ffb39d7d470'}, {'type': 'function', 'name': 'CShellItemArrayAsCollection_CreateInstance', 'address': '0x7ffb39cb3150'}, {'type': 'function', 'name': 'CShellItemArrayAsVirtualizedObjectArray_CreateInstance', 'address': '0x7ffb39fd0990'}, {'type': 'function', 'name': 'CShellItemArrayWithCommonParent_CreateInstance', 'address': '0x7ffb39c284d0'}, {'type': 'function', 'name': 'CShellItemArray_CreateInstance', 'address': '0x7ffb39d3d620'}, {'type': 'function', 'name': 'CShellItem_CreateInstance', 'address': '0x7ffb39cde360'}, {'type': 'function', 'name': 'CStorageItem_GetValidatedStorageItemObject', 'address': '0x7ffb39ead9a0'}, {'type': 'function', 'name': 'CTaskAddDoc_Create', 'address': '0x7ffb39bdd0e0'}, {'type': 'function', 'name': 'CViewSettings_CreateInstance', 'address': '0x7ffb39d70320'}, {'type': 'function', 'name': 'CopyDefaultLibrariesFromGroupPolicy', 'address': '0x7ffb39fa1f50'}, {'type': 'function', 'name': 'CreateExtrinsicPropertyStore', 'address': '0x7ffb3a013ea0'}, {'type': 'function', 'name': 'CreateItemArrayFromItemStore', 'address': '0x7ffb39fd0a10'}, {'type': 'function', 'name': 'CreateItemArrayFromObjectArray', 'address': '0x7ffb39c45450'}, {'type': 'function', 'name': 'CreateLocalizationDesktopIni', 'address': '0x7ffb39f9c700'}, {'type': 'function', 'name': 'CreateSortColumnArray', 'address': '0x7ffb39d39490'}, {'type': 'function', 'name': 'CreateStorageItemFromPath_FullTrustCaller', 'address': '0x7ffb39e763a0'}, {'type': 'function', 'name': 'CreateStorageItemFromPath_FullTrustCaller_ForPackage', 'address': '0x7ffb39e763c0'}, {'type': 'function', 'name': 'CreateStorageItemFromPath_PartialTrustCaller', 'address': '0x7ffb39eadaa0'}, {'type': 'function', 'name': 'CreateStorageItemFromShellItem', 'address': '0x7ffb39c66850'}, {'type': 'function', 'name': 'CreateStorageItemFromShellItem_FullTrustCaller_ForPackage', 'address': '0x7ffb39c58d70'}, {'type': 'function', 'name': 'CreateVolatilePropertyStore', 'address': '0x7ffb39d84d80'}, {'type': 'function', 'name': 'CustomStatePropertyDescription_CreateWithItemPropertyStore', 'address': '0x7ffb3a015490'}, {'type': 'function', 'name': 'CustomStatePropertyDescription_CreateWithStateIdentifier', 'address': '0x7ffb3a0155c0'}, {'type': 'function', 'name': 'DataAccessCaches_InvalidateForLibrary', 'address': '0x7ffb39c251f0'}, {'type': 'function', 'name': 'DeserializeTextToLink', 'address': '0x7ffb39c55c90'}, {'type': 'function', 'name': 'DetermineFolderDestinationParentAppID', 'address': '0x7ffb39bdf5c0'}, {'type': 'function', 'name': 'DllCanUnloadNow', 'address': '0x7ffb39d59b70'}, {'type': 'function', 'name': 'DllGetActivationFactory', 'address': '0x7ffb39d52aa0'}, {'type': 'function', 'name': 'DllGetClassObject', 'address': '0x7ffb39cd1f00'}, {'type': 'function', 'name': 'DllMain', 'address': '0x7ffb39d91ff0'}, {'type': 'function', 'name': 'DllRegisterServer', 'address': '0x7ffb39d7f570'}, {'type': 'function', 'name': 'DllUnregisterServer', 'address': '0x7ffb39d7f570'}, {'type': 'function', 'name': 'DragQueryFileW', 'address': '0x7ffb3a098b70'}, {'type': 'function', 'name': 'EnumShellItemsFromEnumFullIdList', 'address': '0x7ffb39fd0ac0'}, {'type': 'function', 'name': 'GetCachedFileUpdateInformation', 'address': '0x7ffb39eadc90'}, {'type': 'function', 'name': 'GetCommandProviderForFolderType', 'address': '0x7ffb3a022450'}, {'type': 'function', 'name': 'GetFileUndoText', 'address': '0x7ffb3a003200'}, {'type': 'function', 'name': 'GetFindDataForPath', 'address': '0x7ffb39cdd740'}, {'type': 'function', 'name': 'GetFindDataFromFileInformationByHandle', 'address': '0x7ffb39d20930'}, {'type': 'function', 'name': 'GetInfoForFileInUse', 'address': '0x7ffb39eb3da0'}, {'type': 'function', 'name': 'GetRegDataDrivenCommand', 'address': '0x7ffb39c66820'}, {'type': 'function', 'name': 'GetRegDataDrivenCommandWithAssociation', 'address': '0x7ffb39c7c420'}, {'type': 'function', 'name': 'GetSelectionStateFromItemArray', 'address': '0x7ffb3a0224f0'}, {'type': 'function', 'name': 'GetSystemPersistedStorageItemList', 'address': '0x7ffb39c37d80'}, {'type': 'function', 'name': 'GetSystemPersistedStorageItemListForUser', 'address': '0x7ffb39eb3e30'}, {'type': 'function', 'name': 'GetThreadFlags', 'address': '0x7ffb39d11fe0'}, {'type': 'function', 'name': 'GetUserChoiceForUrl', 'address': '0x7ffb39bf4030'}, {'type': 'function', 'name': 'Global_WindowsStorage_MaxIcons', 'address': '0x7ffb39d7e5f0'}, {'type': 'function', 'name': 'Global_WindowsStorage_Untyped_FileClassSRWLock', 'address': '0x7ffb39d7bf80'}, {'type': 'function', 'name': 'Global_WindowsStorage_Untyped_MountPoint', 'address': '0x7ffb39d60eb0'}, {'type': 'function', 'name': 'Global_WindowsStorage_Untyped_pFileClassCacheTable', 'address': '0x7ffb39d7bf30'}, {'type': 'function', 'name': 'Global_WindowsStorage_Untyped_pFileHanderMap', 'address': '0x7ffb39d7e170'}, {'type': 'function', 'name': 'Global_WindowsStorage_Untyped_rgshil', 'address': '0x7ffb39d5d420'}, {'type': 'function', 'name': 'Global_WindowsStorage_afNotRedirected', 'address': '0x7ffb39d72150'}, {'type': 'function', 'name': 'Global_WindowsStorage_ccIcon', 'address': '0x7ffb39d792e0'}, {'type': 'function', 'name': 'Global_WindowsStorage_csIconCache', 'address': '0x7ffb39d5faf0'}, {'type': 'function', 'name': 'Global_WindowsStorage_csSCN', 'address': '0x7ffb39d87e10'}, {'type': 'function', 'name': 'Global_WindowsStorage_dwThreadBindCtx', 'address': '0x7ffb39d7b030'}, {'type': 'function', 'name': 'Global_WindowsStorage_dwThreadInitializing', 'address': '0x7ffb39d7a7b0'}, {'type': 'function', 'name': 'Global_WindowsStorage_esServerMode', 'address': '0x7ffb39d7c440'}, {'type': 'function', 'name': 'Global_WindowsStorage_fEndInitialized', 'address': '0x7ffb39d6dd70'}, {'type': 'function', 'name': 'Global_WindowsStorage_fIconCacheHasBeenSuccessfullyCreated', 'address': '0x7ffb39c67620'}, {'type': 'function', 'name': 'Global_WindowsStorage_fIconCacheIsValid', 'address': '0x7ffb39d72550'}, {'type': 'function', 'name': 'Global_WindowsStorage_fNeedsInitBroadcast', 'address': '0x7ffb39c67610'}, {'type': 'function', 'name': 'Global_WindowsStorage_hwndSCN', 'address': '0x7ffb39d73ad0'}, {'type': 'function', 'name': 'Global_WindowsStorage_iLastSysIcon', 'address': '0x7ffb39d7e9a0'}, {'type': 'function', 'name': 'Global_WindowsStorage_iLastSystemColorDepth', 'address': '0x7ffb39d7e900'}, {'type': 'function', 'name': 'Global_WindowsStorage_iUseLinkPrefix', 'address': '0x7ffb39eb4640'}, {'type': 'function', 'name': 'Global_WindowsStorage_lProcessClassCount', 'address': '0x7ffb39c67320'}, {'type': 'function', 'name': 'Global_WindowsStorage_lrFlags', 'address': '0x7ffb39d78f10'}, {'type': 'function', 'name': 'Global_WindowsStorage_nImageManagerVersion', 'address': '0x7ffb39eb4650'}, {'type': 'function', 'name': 'Global_WindowsStorage_tlsChangeClientProxy', 'address': '0x7ffb39d76730'}, {'type': 'function', 'name': 'Global_WindowsStorage_tlsIconCache', 'address': '0x7ffb39d7ad80'}, {'type': 'function', 'name': 'Global_WindowsStorage_tlsThreadFlags', 'address': '0x7ffb39eb4660'}, {'type': 'function', 'name': 'Global_WindowsStorage_ulNextID', 'address': '0x7ffb39d7c4f0'}, {'type': 'function', 'name': 'GrantPathAccess_FullTrustCaller_ForPackage', 'address': '0x7ffb39eb3f90'}, {'type': 'function', 'name': 'GrantWorkingDirectoryAccess_FullTrustCaller_ForPackage', 'address': '0x7ffb39eb4060'}, {'type': 'function', 'name': 'HideExtension', 'address': '0x7ffb39c0f970'}, {'type': 'function', 'name': 'ILAppendID', 'address': '0x7ffb3a098d40'}, {'type': 'function', 'name': 'ILClone', 'address': '0x7ffb39cbd9e0'}, {'type': 'function', 'name': 'ILCloneFirst', 'address': '0x7ffb39cef630'}, {'type': 'function', 'name': 'ILCombine', 'address': '0x7ffb39cbdb00'}, {'type': 'function', 'name': 'ILFindChild', 'address': '0x7ffb3a098e20'}, {'type': 'function', 'name': 'ILFindLastID', 'address': '0x7ffb39cc5160'}, {'type': 'function', 'name': 'ILFree', 'address': '0x7ffb39cef310'}, {'type': 'function', 'name': 'ILGetNext', 'address': '0x7ffb39d61a10'}, {'type': 'function', 'name': 'ILGetSize', 'address': '0x7ffb39cbdce0'}, {'type': 'function', 'name': 'ILIsEqual', 'address': '0x7ffb39cb64e0'}, {'type': 'function', 'name': 'ILIsParent', 'address': '0x7ffb39cf31c0'}, {'type': 'function', 'name': 'ILLoadFromStreamEx', 'address': '0x7ffb39cf1df0'}, {'type': 'function', 'name': 'ILRemoveLastID', 'address': '0x7ffb39cf0f40'}, {'type': 'function', 'name': 'ILSaveToStream', 'address': '0x7ffb39d61910'}, {'type': 'function', 'name': 'IsLFNDriveW', 'address': '0x7ffb39d7f530'}, {'type': 'function', 'name': 'IsLibraryCreatedByPolicy', 'address': '0x7ffb39fa2080'}, {'type': 'function', 'name': 'IsLibraryPolicyEnabled', 'address': '0x7ffb39c4bcc0'}, {'type': 'function', 'name': 'IsNameListedUnderKey', 'address': '0x7ffb3a02c8d0'}, {'type': 'function', 'name': 'IsUserAnAdmin', 'address': '0x7ffb3a099d70'}, {'type': 'function', 'name': 'NeverProvidedByJunction', 'address': '0x7ffb39c11830'}, {'type': 'function', 'name': 'PathCleanupSpec', 'address': '0x7ffb39d566a0'}, {'type': 'function', 'name': 'PathContainedByManifestedKnownFolder_FullTrustCaller_ForPackage', 'address': '0x7ffb39eafcc0'}, {'type': 'function', 'name': 'PathIsExe', 'address': '0x7ffb3a099340'}, {'type': 'function', 'name': 'PathMakeUniqueName', 'address': '0x7ffb3a099620'}, {'type': 'function', 'name': 'PathYetAnotherMakeUniqueName', 'address': '0x7ffb39c23210'}, {'type': 'function', 'name': 'QueryStorageAccess_FullTrustCaller_ForPackage', 'address': '0x7ffb39eb4130'}, {'type': 'function', 'name': 'QueryStorageAccess_FullTrustCaller_ForToken', 'address': '0x7ffb39eb4330'}, {'type': 'function', 'name': 'RebaseOnDriveLetter', 'address': '0x7ffb39bfbd00'}, {'type': 'function', 'name': 'RebaseOnVolumeID', 'address': '0x7ffb39be09c0'}, {'type': 'function', 'name': 'RegistryVerbs_GetHandlerMultiSelectModel', 'address': '0x7ffb39d6cd50'}, {'type': 'function', 'name': 'SHBindToFolderIDListParent', 'address': '0x7ffb39d6e960'}, {'type': 'function', 'name': 'SHBindToFolderIDListParentEx', 'address': '0x7ffb39cb8980'}, {'type': 'function', 'name': 'SHBindToObject', 'address': '0x7ffb39cf0f80'}, {'type': 'function', 'name': 'SHBindToParent', 'address': '0x7ffb39cf0e80'}, {'type': 'function', 'name': 'SHCLSIDFromString', 'address': '0x7ffb39c7e210'}, {'type': 'function', 'name': 'SHChangeNotification_Lock', 'address': '0x7ffb39c97d00'}, {'type': 'function', 'name': 'SHChangeNotification_Unlock', 'address': '0x7ffb39c97ae0'}, {'type': 'function', 'name': 'SHChangeNotify', 'address': '0x7ffb39caaf50'}, {'type': 'function', 'name': 'SHChangeNotifyDeregister', 'address': '0x7ffb39d83580'}, {'type': 'function', 'name': 'SHChangeNotifyRegister', 'address': '0x7ffb39d660e0'}, {'type': 'function', 'name': 'SHCoCreateInstanceWorker', 'address': '0x7ffb39cb7e00'}, {'type': 'function', 'name': 'SHCreateAssociationRegistration', 'address': '0x7ffb3a0b0c40'}, {'type': 'function', 'name': 'SHCreateDataObject', 'address': '0x7ffb39c5f9b0'}, {'type': 'function', 'name': 'SHCreateDefaultExtractIcon', 'address': '0x7ffb39d174a0'}, {'type': 'function', 'name': 'SHCreateDirectory', 'address': '0x7ffb3a02ce50'}, {'type': 'function', 'name': 'SHCreateDirectoryExA', 'address': '0x7ffb3a02ce70'}, {'type': 'function', 'name': 'SHCreateDirectoryExW', 'address': '0x7ffb39d7d1b0'}, {'type': 'function', 'name': 'SHCreateItemFromIDList', 'address': '0x7ffb39cddb70'}, {'type': 'function', 'name': 'SHCreateItemFromParsingName', 'address': '0x7ffb39cf6f10'}, {'type': 'function', 'name': 'SHCreateItemInKnownFolder', 'address': '0x7ffb3a09e5a0'}, {'type': 'function', 'name': 'SHCreateItemWithParent', 'address': '0x7ffb39c90110'}, {'type': 'function', 'name': 'SHCreateItemWithParentAndChildId', 'address': '0x7ffb39ceccd0'}, {'type': 'function', 'name': 'SHCreateShellItemArray', 'address': '0x7ffb39c83c40'}, {'type': 'function', 'name': 'SHCreateShellItemArrayFromDataObject', 'address': '0x7ffb39c83f40'}, {'type': 'function', 'name': 'SHCreateShellItemArrayFromIDLists', 'address': '0x7ffb39c843c0'}, {'type': 'function', 'name': 'SHCreateShellItemArrayFromShellItem', 'address': '0x7ffb39c83b30'}, {'type': 'function', 'name': 'SHCreateShellItemArrayWithFolderParent', 'address': '0x7ffb39fd0bf0'}, {'type': 'function', 'name': 'SHCreateStdEnumFmtEtc', 'address': '0x7ffb39c35f60'}, {'type': 'function', 'name': 'SHFileOperationWithAdditionalFlags', 'address': '0x7ffb3a003a50'}, {'type': 'function', 'name': 'SHFindFiles', 'address': '0x7ffb3a096360'}, {'type': 'function', 'name': 'SHFlushSFCache', 'address': '0x7ffb3a0b3db0'}, {'type': 'function', 'name': 'SHGetDesktopFolder', 'address': '0x7ffb39d7cdb0'}, {'type': 'function', 'name': 'SHGetFileInfoW', 'address': '0x7ffb39c8f910'}, {'type': 'function', 'name': 'SHGetFolderLocation', 'address': '0x7ffb39d5d460'}, {'type': 'function', 'name': 'SHGetFolderPathA', 'address': '0x7ffb39d837f0'}, {'type': 'function', 'name': 'SHGetFolderPathAndSubDirA', 'address': '0x7ffb3a02ca70'}, {'type': 'function', 'name': 'SHGetFolderPathAndSubDirW', 'address': '0x7ffb39d63160'}, {'type': 'function', 'name': 'SHGetFolderPathEx', 'address': '0x7ffb39d02920'}, {'type': 'function', 'name': 'SHGetFolderPathW', 'address': '0x7ffb39cd9740'}, {'type': 'function', 'name': 'SHGetIDListFromObject', 'address': '0x7ffb39cdc9d0'}, {'type': 'function', 'name': 'SHGetInstanceExplorer', 'address': '0x7ffb39c67780'}, {'type': 'function', 'name': 'SHGetItemFromObject', 'address': '0x7ffb39d33680'}, {'type': 'function', 'name': 'SHGetKnownFolderIDList', 'address': '0x7ffb39d654a0'}, {'type': 'function', 'name': 'SHGetKnownFolderIDList_Internal', 'address': '0x7ffb39cd47c0'}, {'type': 'function', 'name': 'SHGetKnownFolderItem', 'address': '0x7ffb39d50a30'}, {'type': 'function', 'name': 'SHGetKnownFolderPath', 'address': '0x7ffb39cd3c80'}, {'type': 'function', 'name': 'SHGetNameFromIDList', 'address': '0x7ffb39d62650'}, {'type': 'function', 'name': 'SHGetPathFromIDListEx', 'address': '0x7ffb39d015b0'}, {'type': 'function', 'name': 'SHGetPathFromIDListW', 'address': '0x7ffb3a096540'}, {'type': 'function', 'name': 'SHGetSetSettings', 'address': '0x7ffb39d14110'}, {'type': 'function', 'name': 'SHGetSpecialFolderLocation', 'address': '0x7ffb39d5d430'}, {'type': 'function', 'name': 'SHGetSpecialFolderPathA', 'address': '0x7ffb3a02cb50'}, {'type': 'function', 'name': 'SHGetSpecialFolderPathW', 'address': '0x7ffb39d736f0'}, {'type': 'function', 'name': 'SHGetStockIconInfo', 'address': '0x7ffb39bed830'}, {'type': 'function', 'name': 'SHGetTemporaryPropertyForItem', 'address': '0x7ffb3a09eb10'}, {'type': 'function', 'name': 'SHILCreateFromPath', 'address': '0x7ffb3a098f40'}, {'type': 'function', 'name': 'SHKnownFolderFromCSIDL', 'address': '0x7ffb39cd81b0'}, {'type': 'function', 'name': 'SHKnownFolderToCSIDL', 'address': '0x7ffb39d4bf60'}, {'type': 'function', 'name': 'SHParseDisplayName', 'address': '0x7ffb39ce63f0'}, {'type': 'function', 'name': 'SHPrepareKnownFoldersCommon', 'address': '0x7ffb39f9c7c0'}, {'type': 'function', 'name': 'SHPrepareKnownFoldersUser', 'address': '0x7ffb39f9c7d0'}, {'type': 'function', 'name': 'SHResolveLibrary', 'address': '0x7ffb39fa7a70'}, {'type': 'function', 'name': 'SHRestricted', 'address': '0x7ffb39ce67b0'}, {'type': 'function', 'name': 'SHSetFolderPathA', 'address': '0x7ffb3a02cb90'}, {'type': 'function', 'name': 'SHSetFolderPathW', 'address': '0x7ffb3a02cc10'}, {'type': 'function', 'name': 'SHSetKnownFolderPath', 'address': '0x7ffb39f9c7e0'}, {'type': 'function', 'name': 'SHSetLocalizedName', 'address': '0x7ffb3a09f1b0'}, {'type': 'function', 'name': 'SHSetTemporaryPropertyForItem', 'address': '0x7ffb39c6c650'}, {'type': 'function', 'name': 'SHSysErrorMessageBox', 'address': '0x7ffb3a096a30'}, {'type': 'function', 'name': 'SHTestTokenMembership', 'address': '0x7ffb39c87880'}, {'type': 'function', 'name': 'STORAGE_AddItemToRecentDocs', 'address': '0x7ffb39c2f090'}, {'type': 'function', 'name': 'STORAGE_AddNewFolderToFrequentPlaces', 'address': '0x7ffb3a02cd00'}, {'type': 'function', 'name': 'STORAGE_CEnumFiles_CreateInstance', 'address': '0x7ffb3a02cd10'}, {'type': 'function', 'name': 'STORAGE_CStatusProvider_CreateInstance', 'address': '0x7ffb3a02cd20'}, {'type': 'function', 'name': 'STORAGE_CStorageItem_GetValidatedStorageItem', 'address': '0x7ffb39c1a7f0'}, {'type': 'function', 'name': 'STORAGE_CStorageItem_GetValidatedStorageItemObject', 'address': '0x7ffb3a02cd30'}, {'type': 'function', 'name': 'STORAGE_ClearDestinationsForAllApps', 'address': '0x7ffb3a02cd40'}, {'type': 'function', 'name': 'STORAGE_CreateSortColumnArrayFromListDesc', 'address': '0x7ffb3a02cd50'}, {'type': 'function', 'name': 'STORAGE_CreateStorageItemFromPath_FullTrustCaller', 'address': '0x7ffb39e763a0'}, {'type': 'function', 'name': 'STORAGE_CreateStorageItemFromPath_FullTrustCaller_ForPackage', 'address': '0x7ffb3a02cd60'}, {'type': 'function', 'name': 'STORAGE_CreateStorageItemFromPath_PartialTrustCaller', 'address': '0x7ffb3a02cd70'}, {'type': 'function', 'name': 'STORAGE_CreateStorageItemFromShellItem_FullTrustCaller', 'address': '0x7ffb3a02cd80'}, {'type': 'function', 'name': 'STORAGE_CreateStorageItemFromShellItem_FullTrustCaller_ForPackage', 'address': '0x7ffb39c58d70'}, {'type': 'function', 'name': 'STORAGE_CreateStorageItemFromShellItem_FullTrustCaller_ForPackage_WithProcessHandle', 'address': '0x7ffb3a02cdb0'}, {'type': 'function', 'name': 'STORAGE_CreateStorageItemFromShellItem_FullTrustCaller_ForPackage_WithProcessHandleAndSecondaryStreamName', 'address': '0x7ffb39d7f6b0'}, {'type': 'function', 'name': 'STORAGE_CreateStorageItemFromShellItem_FullTrustCaller_UseImplicitFlagsAndPackage', 'address': '0x7ffb3a02cdf0'}, {'type': 'function', 'name': 'STORAGE_FillResultWithNullForKeys', 'address': '0x7ffb39d7f6b0'}, {'type': 'function', 'name': 'STORAGE_GetShellItemFromStorageItem', 'address': '0x7ffb39c672d0'}, {'type': 'function', 'name': 'STORAGE_GetSystemPersistedStorageItemList', 'address': '0x7ffb3a02ce00'}, {'type': 'function', 'name': 'STORAGE_MakeDestinationItem', 'address': '0x7ffb3a02ce10'}, {'type': 'function', 'name': 'STORAGE_PathIsEqualOrSubFolderOfKnownFolders', 'address': '0x7ffb3a02ce20'}, {'type': 'function', 'name': 'STORAGE_SHAddToRecentDocs', 'address': '0x7ffb39c67a90'}, {'type': 'function', 'name': 'STORAGE_SHAddToRecentDocsEx', 'address': '0x7ffb3a02ce30'}, {'type': 'function', 'name': 'STORAGE_SHConfirmOperation', 'address': '0x7ffb3a02ce40'}, {'type': 'function', 'name': 'STORAGE_SHCreateDirectory', 'address': '0x7ffb3a02ce50'}, {'type': 'function', 'name': 'STORAGE_SHCreateDirectoryExA', 'address': '0x7ffb3a02ce70'}, {'type': 'function', 'name': 'STORAGE_SHCreateDirectoryExWWorker', 'address': '0x7ffb39d7d1b0'}, {'type': 'function', 'name': 'STORAGE_SHCreateShellItemArray', 'address': '0x7ffb3a02ce90'}, {'type': 'function', 'name': 'STORAGE_SHCreateShellItemArrayFromDataObject', 'address': '0x7ffb39c83f30'}, {'type': 'function', 'name': 'STORAGE_SHCreateShellItemArrayFromIDLists', 'address': '0x7ffb39c83b20'}, {'type': 'function', 'name': 'STORAGE_SHCreateShellItemArrayFromShellItem', 'address': '0x7ffb39c673d0'}, {'type': 'function', 'name': 'STORAGE_SHFileOperation', 'address': '0x7ffb3a02cea0'}, {'type': 'function', 'name': 'STORAGE_SHFileOperationA', 'address': '0x7ffb3a02ceb0'}, {'type': 'function', 'name': 'STORAGE_SHFreeNameMappings', 'address': '0x7ffb3a02cec0'}, {'type': 'function', 'name': 'STORAGE_SHGetDesktopFolderWorker', 'address': '0x7ffb39cb9310'}, {'type': 'function', 'name': 'STORAGE_SHGetPathFromMsUri', 'address': '0x7ffb3a02cf30'}, {'type': 'function', 'name': 'STORAGE_SHPathPrepareForWriteA', 'address': '0x7ffb3a02cf40'}, {'type': 'function', 'name': 'STORAGE_SHPathPrepareForWriteW', 'address': '0x7ffb3a02cfc0'}, {'type': 'function', 'name': 'STORAGE_SHValidateMSUri', 'address': '0x7ffb3a02cfd0'}, {'type': 'function', 'name': 'SendNotificationsForLibraryItem', 'address': '0x7ffb39faabe0'}, {'type': 'function', 'name': 'SerializeLinkToText', 'address': '0x7ffb39c304e0'}, {'type': 'function', 'name': 'SetThreadFlags', 'address': '0x7ffb39d86b50'}, {'type': 'function', 'name': 'ShellExecuteExW', 'address': '0x7ffb3a0bc500'}, {'type': 'function', 'name': 'StateRepoVerbsCache_Destroy', 'address': '0x7ffb3a02e4e0'}, {'type': 'function', 'name': 'StateRepoVerbsCache_GetContextMenuVerbs', 'address': '0x7ffb39ca1820'}, {'type': 'function', 'name': 'StateRepoVerbsCache_RebuildCacheAsync', 'address': '0x7ffb39d73ae0'}, {'type': 'function', 'name': 'StorageItemHelpers_IsSupportedRemovablePath', 'address': '0x7ffb39eade50'}, {'type': 'function', 'name': 'Storage_Internal_GetAccessListForPackage', 'address': '0x7ffb39eb4450'}, {'type': 'function', 'name': '_CleanRecentDocs', 'address': '0x7ffb3a00dca0'}, {'type': 'function', 'name': '_PredictReasonableImpact', 'address': '0x7ffb39d71730'}] |
import requests
import numpy as np
from pandas import DataFrame, Series
import pandas as pd
from utilities import LICENSE_KEY, generate_token, master_player_lookup
import json
pd.options.mode.chained_assignment = None
LEAGUE_ID = 316893
TEAM_ID = 1605156
###############################################################################
# roster data
###############################################################################
roster_url = ('https://www.fleaflicker.com/api/FetchRoster?' +
f'leagueId={LEAGUE_ID}&teamId={TEAM_ID}')
# gets current data
# should run/look at, but we're overwriting with saved data next line
roster_json = requests.get(roster_url).json()
# saved data
with open('./projects/integration/raw/fleaflicker/roster.json') as f:
roster_json = json.load(f)
list_of_starter_slots = roster_json['groups'][0]['slots']
list_of_bench_slots = roster_json['groups'][1]['slots']
starter_slot0 = list_of_starter_slots[0]
starter_slot0['leaguePlayer']['proPlayer']
def process_player1(slot):
fleaflicker_player_dict = slot['leaguePlayer']['proPlayer']
fleaflicker_position_dict = slot['position']
dict_to_return = {}
dict_to_return['name'] = fleaflicker_player_dict['nameFull']
dict_to_return['player_position'] = fleaflicker_player_dict['position']
dict_to_return['fleaflicker_id'] = fleaflicker_player_dict['id']
dict_to_return['team_position'] = fleaflicker_position_dict['label']
return dict_to_return
process_player1(starter_slot0)
[process_player1(player) for player in list_of_starter_slots]
# [process_player1(player) for player in list_of_bench_slots] # error
qb_data_raw = roster_json['groups'][0]['slots'][0]
process_player1(qb_data_raw)
# error - what's happening?
# i had an open spot on my bench (put a guy on IR and didn't pick someone else
# up yet), so there's no league player there
# now we need to modify process_player1 to handle that
def process_player2(slot):
dict_to_return = {}
if 'leaguePlayer' in slot.keys():
fleaflicker_player_dict = slot['leaguePlayer']['proPlayer']
dict_to_return['name'] = fleaflicker_player_dict['nameFull']
dict_to_return['player_position'] = fleaflicker_player_dict['position']
dict_to_return['fleaflicker_id'] = fleaflicker_player_dict['id']
if 'position' in slot.keys():
fleaflicker_position_dict = slot['position']
dict_to_return['team_position'] = fleaflicker_position_dict['label']
return dict_to_return
[process_player2(x) for x in list_of_starter_slots]
def process_player3(slot):
dict_to_return = {}
if 'leaguePlayer' in slot.keys():
fleaflicker_player_dict = slot['leaguePlayer']['proPlayer']
dict_to_return['name'] = fleaflicker_player_dict['nameFull']
dict_to_return['player_position'] = fleaflicker_player_dict['position']
dict_to_return['fleaflicker_id'] = fleaflicker_player_dict['id']
if 'requestedGames' in slot['leaguePlayer']:
game = slot['leaguePlayer']['requestedGames'][0]
if 'pointsActual' in game:
if 'value' in game['pointsActual']:
dict_to_return['actual'] = game['pointsActual']['value']
if 'position' in slot.keys():
fleaflicker_position_dict = slot['position']
dict_to_return['team_position'] = fleaflicker_position_dict['label']
return dict_to_return
# list of dicts: put in DataFrame
starter_df1 = DataFrame([process_player3(x) for x in list_of_starter_slots])
starter_df1
# looks pretty good
# but duplicate team_positions might be a problem
# would be better to have it be RB1, RB2, etc
# start with specific example
wrs = starter_df1.query("team_position == 'WR'")
wrs
suffix = Series(range(1, len(wrs) + 1), index=wrs.index)
suffix
wrs['team_position'] = wrs['team_position'] + suffix.astype(str)
wrs
# so put in a function that takes any position
def add_pos_suffix(df_subset):
if len(df_subset) > 1:
suffix = Series(range(1, len(df_subset) + 1), index=df_subset.index)
df_subset['team_position'] = df_subset['team_position'] + suffix.astype(str)
return df_subset
# and we want to apply it to every position in the starter df
starter_df2 = pd.concat([
add_pos_suffix(starter_df1.query(f"team_position == '{x}'"))
for x in starter_df1['team_position'].unique()])
starter_df2
bench_df = DataFrame([process_player3(x) for x in list_of_bench_slots])
# now let's ID these and stick them together
starter_df2['start'] = True
bench_df['start'] = False
roster_df = pd.concat([starter_df2, bench_df], ignore_index=True)
roster_df
roster_df['team_id'] = TEAM_ID
roster_df.head()
# next: fantasymath ids
roster_df['name'].str.lower().str.replace(' ','-').head()
from utilities import (LICENSE_KEY, generate_token, master_player_lookup)
token = generate_token(LICENSE_KEY)['token']
fantasymath_players = master_player_lookup(token)
fantasymath_players = pd.read_csv('./projects/integration/raw/lookup.csv')
fantasymath_players.head()
roster_df_w_id = pd.merge(
roster_df, fantasymath_players[['fantasymath_id', 'fleaflicker_id']],
how='left')
# we can basically put everything we did above into a function
# put in a function
def get_team_roster(team_id, league_id, lookup):
roster_url = ('https://www.fleaflicker.com/api/FetchRoster?' +
f'leagueId={league_id}&teamId={team_id}')
roster_json = requests.get(roster_url).json()
starter_slots = roster_json['groups'][0]['slots']
bench_slots = roster_json['groups'][1]['slots']
starter_df = DataFrame([process_player3(x) for x in starter_slots])
bench_df = DataFrame([process_player3(x) for x in bench_slots])
starter_df['start'] = True
bench_df['start'] = False
team_df = pd.concat([starter_df, bench_df], ignore_index=True)
team_df['team_id'] = team_id
team_df_w_id = pd.merge(team_df,
lookup[['fantasymath_id', 'fleaflicker_id']],
how='left').drop('fleaflicker_id', axis=1)
if 'actual' not in team_df_w_id.columns:
team_df_w_id['actual'] = np.nan
return team_df_w_id
my_roster = get_team_roster(TEAM_ID, LEAGUE_ID, fantasymath_players)
###############################################################################
# team data
###############################################################################
# gets current data
# should run/look at, but we're overwriting with saved data next line
teams_url = ('https://www.fleaflicker.com/api/FetchLeagueStandings?' +
f'leagueId={LEAGUE_ID}')
# saved data
with open('./projects/integration/raw/fleaflicker/teams.json') as f:
teams_json = json.load(f)
# same process - look at json (dict) and see how it's structured
division0 = teams_json['divisions'][0]
team0_division0 = division0['teams'][0]
def process_team(team):
dict_to_return = {}
dict_to_return['team_id'] = team['id']
dict_to_return['owner_id'] = team['owners'][0]['id']
dict_to_return['owner_name'] = team['owners'][0]['displayName']
return dict_to_return
# works on one team
process_team(team0_division0)
def teams_from_div(division):
return DataFrame([process_team(x) for x in division['teams']])
teams_from_div(division0)
# now let's put inside function to get all teams from all divisions
def divs_from_league(divisions):
return pd.concat([teams_from_div(division) for division in divisions],
ignore_index=True)
divs_from_league(teams_json['divisions'])
# basically what we want, now let's put everything into a function that takes
# league, year and returns all teams, owners, divisions they're in
def get_teams_in_league(league_id):
teams_url = ('https://www.fleaflicker.com/api/FetchLeagueStandings?' +
f'leagueId={league_id}')
teams_json = requests.get(teams_url).json()
teams_df = divs_from_league(teams_json['divisions'])
teams_df['league_id'] = league_id
return teams_df
league_teams = get_teams_in_league(LEAGUE_ID)
league_teams
###############################################################################
# combining teams + roster functions
###############################################################################
def get_league_rosters(lookup, league_id):
teams = get_teams_in_league(league_id)
league_rosters = pd.concat(
[get_team_roster(x, league_id, lookup) for x in teams['team_id']],
ignore_index=True)
return league_rosters
league_rosters = get_league_rosters(fantasymath_players, LEAGUE_ID)
league_rosters.sample(20)
###############################################################################
# schedule
###############################################################################
WEEK = 1
schedule_url = (
'https://www.fleaflicker.com/api/FetchLeagueScoreboard?' +
f'leagueId={LEAGUE_ID}&scoringPeriod={WEEK}&season=2021')
# gets current data
# should run/look at, but we're overwriting with saved data next line
schedule_json = requests.get(schedule_url).json()
# saved data
with open('./projects/integration/raw/fleaflicker/schedule.json') as f:
schedule_json = json.load(f)
matchup_list = schedule_json['games']
matchup0 = matchup_list[0]
# basic: just need team info
def process_matchup(game):
return_dict = {}
return_dict['team1_id'] = game['home']['id']
return_dict['team2_id'] = game['away']['id']
return_dict['game_id'] = game['id']
return return_dict
process_matchup(matchup0)
# let's just do our usual, wrap it in a function that takes league_id, season,
# week and returns a dataframe
def get_schedule_by_week(league_id, week):
schedule_url = (
'https://www.fleaflicker.com/api/FetchLeagueScoreboard?' +
f'leagueId={LEAGUE_ID}&scoringPeriod={WEEK}&season=2021')
schedule_json = requests.get(schedule_url).json()
matchup_df = DataFrame([process_matchup(x) for x in schedule_json['games']])
matchup_df['season'] = 2021
matchup_df['week'] = week
matchup_df['league_id'] = league_id
return matchup_df
get_schedule_by_week(LEAGUE_ID, 1)
# now let's do for entire season
def get_league_schedule(league_id):
return pd.concat([get_schedule_by_week(league_id, week) for week in
range(1, 15)], ignore_index=True)
league_schedule = get_league_schedule(LEAGUE_ID)
league_schedule.head()
|
# -*- coding: utf-8 -*-
"""Generate the Resilient customizations required for fn_palo_alto_wildfire"""
from __future__ import print_function
from resilient_circuits.util import *
def codegen_reload_data():
"""Parameters to codegen used to generate the fn_palo_alto_wildfire package"""
reload_params = {"package": u"fn_palo_alto_wildfire",
"incident_fields": [],
"action_fields": [],
"function_params": [u"artifact_id", u"artifact_value", u"attachment_id", u"incident_id"],
"datatables": [u"palo_alto_wildfire_results"],
"message_destinations": [u"palo_alto_wildfire"],
"functions": [u"palo_alto_wildfire_file_submission_artifact", u"palo_alto_wildfire_file_submission_attachment", u"palo_alto_wildfire_url_submission"],
"phases": [],
"automatic_tasks": [],
"scripts": [],
"workflows": [u"example_palo_alto_wildfire_file_submission_artifact", u"example_palo_alto_wildfire_file_submission_attachment", u"example_palo_alto_wildfire_url_submission"],
"actions": [u"Example: Submit File (Artifact) to WildFire", u"Example: Submit File (Attachment) to WildFire", u"Example: Submit URL to WildFire"]
}
return reload_params
def customization_data(client=None):
"""Produce any customization definitions (types, fields, message destinations, etc)
that should be installed by `resilient-circuits customize`
"""
# This import data contains:
# Function inputs:
# artifact_id
# artifact_value
# attachment_id
# incident_id
# DataTables:
# palo_alto_wildfire_results
# Message Destinations:
# palo_alto_wildfire
# Functions:
# palo_alto_wildfire_file_submission_artifact
# palo_alto_wildfire_file_submission_attachment
# palo_alto_wildfire_url_submission
# Workflows:
# example_palo_alto_wildfire_file_submission_artifact
# example_palo_alto_wildfire_file_submission_attachment
# example_palo_alto_wildfire_url_submission
# Rules:
# Example: Submit File (Artifact) to WildFire
# Example: Submit File (Attachment) to WildFire
# Example: Submit URL to WildFire
yield ImportDefinition(u"""
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"""
) |
#!/usr/bin/env python
import numpy as np
import mirheo as mir
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--axes', type=float, nargs=3)
parser.add_argument('--coords', type=str)
parser.add_argument('--vis', action='store_true', default=False)
parser.add_argument('--drag', type=float, default=0.0)
args = parser.parse_args()
ranks = (1, 1, 1)
domain = [16, 16, 16]
dt = 1e-3
t_end = 10.0
t_dump_every = 1.0
L = 14.0
num_segments = 10
mass = 1.0
u = mir.Mirheo(ranks, tuple(domain), dt, debug_level=3, log_filename='log', no_splash=True)
# rod
com_q_rod = [[ 0.5 * domain[0],
0.5 * domain[1],
0.5 * domain[2] - L/2,
1.0, 0.0, 0.0, 0.0]]
def center_line(s): return (0, 0, (0.5-s) * L)
def torsion(s): return 0.0
def length(a, b):
return np.sqrt(
(a[0] - b[0])**2 +
(a[1] - b[1])**2 +
(a[2] - b[2])**2)
h = 1.0 / num_segments
l0 = length(center_line(h), center_line(0))
a0 = l0/2
pv_rod = mir.ParticleVectors.RodVector('rod', mass, num_segments)
ic_rod = mir.InitialConditions.Rod(com_q_rod, center_line, torsion, a0)
# ellipsoid
axes = tuple(args.axes)
com_q_ell = [[0.5 * domain[0],
0.5 * domain[1],
0.5 * domain[2] + axes[2],
1., 0, 0, 0]]
coords = np.loadtxt(args.coords).tolist()
if args.vis:
import trimesh
ell = trimesh.creation.icosphere(subdivisions=2, radius = 1.0)
for i in range(3):
ell.vertices[:,i] *= axes[i]
mesh = mir.ParticleVectors.Mesh(ell.vertices.tolist(), ell.faces.tolist())
pv_ell = mir.ParticleVectors.RigidEllipsoidVector('ellipsoid', mass, object_size=len(coords), semi_axes=axes, mesh=mesh)
else:
pv_ell = mir.ParticleVectors.RigidEllipsoidVector('ellipsoid', mass, object_size=len(coords), semi_axes=axes)
ic_ell = mir.InitialConditions.Rigid(com_q_ell, coords)
vv_ell = mir.Integrators.RigidVelocityVerlet("vv_ell")
u.registerParticleVector(pv_ell, ic_ell)
u.registerParticleVector(pv_rod, ic_rod)
u.registerIntegrator(vv_ell)
u.setIntegrator(vv_ell, pv_ell)
# interactions
prms = {
"a0" : a0,
"l0" : l0,
"k_s_center" : 100.0,
"k_s_frame" : 100.0,
"k_bending" : (10.0, 0.0, 10.0),
"k_twist" : 10.0,
"tau0" : 0,
"kappa0" : (0., 0.)
}
int_rod = mir.Interactions.RodForces("rod_forces", **prms);
u.registerInteraction(int_rod)
u.setInteraction(int_rod, pv_rod, pv_rod)
anchor=(0.0, 0.0, -axes[2])
torque = 0.1
k_bound = 100.0
int_bind = mir.Interactions.ObjRodBinding("binding", torque, anchor, k_bound);
u.registerInteraction(int_bind)
u.setInteraction(int_bind, pv_ell, pv_rod)
vv_rod = mir.Integrators.VelocityVerlet('vv_rod')
u.registerIntegrator(vv_rod)
u.setIntegrator(vv_rod, pv_rod)
if args.drag > 0.0:
u.registerPlugins(mir.Plugins.createParticleDrag('rod_drag', pv_rod, args.drag))
if args.vis:
dump_every = int (t_dump_every/dt)
u.registerPlugins(mir.Plugins.createDumpParticles('rod_dump', pv_rod, dump_every, [], 'h5/rod_particles-'))
u.registerPlugins(mir.Plugins.createDumpMesh("mesh_dump", pv_ell, dump_every, path="ply/"))
u.run(int (t_end / dt))
if pv_rod is not None:
pos_rod = pv_rod.getCoordinates()
pos_ell = pv_ell.getCoordinates()
np.savetxt("pos.txt", np.vstack((pos_rod, pos_ell)))
del u
# nTEST: bindings.obj_rod.one
# cd bindings
# rm -rf h5 pos*txt
# f="pos.txt"
# rho=8.0; ax=2.0; ay=1.0; az=1.0
# cp ../../data/ellipsoid_coords_${rho}_${ax}_${ay}_${az}.txt $f
# mir.run --runargs "-n 2" ./obj_rod.py --axes $ax $ay $az --coords $f --vis
# cat pos.txt > pos.out.txt
|
from src.shared.config.configuration import configuration
from src.layers.infrastructure.providers.temporary.queries import queryMfcTagsAndTitles, queryMfcTotal
import requests
import json
class ChapiProvider:
def __init__(self):
self.config = configuration.layers.infrastructure.providers.chapi_provider
def get_total_vids_number(self):
response = requests.post(
url=self.config.chapi_url,
json={'query': queryMfcTotal(self.config.chapi_token)}
)
return json.loads(response.content.decode('utf-8'))['data'] if response.status_code == 200 else None
def get_vids_at_page(self, page: str) -> dict:
response = requests.post(
url=self.config.chapi_url,
json={'query': queryMfcTagsAndTitles(self.config.chapi_token, page)}
)
return json.loads(response.content.decode('utf-8'))['data'] if response.status_code == 200 else None
|
# Generated by Django 3.0.7 on 2020-09-23 05:12
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('preorders', '0004_auto_20200923_0710'),
]
operations = [
migrations.AlterField(
model_name='shoppingcartline',
name='quantity',
field=models.PositiveIntegerField(default=1, verbose_name='Quantité'),
),
]
|
########################################################################
#
# Functions for calculating Total Return, Annualized Returns, etc.
#
########################################################################
#
# This file is part of FinanceOps:
#
# https://github.com/Hvass-Labs/FinanceOps
#
# Published under the MIT License. See the file LICENSE for details.
#
# Copyright 2018 by Magnus Erik Hvass Pedersen
#
########################################################################
import pandas as pd
from data_keys import *
########################################################################
# Public functions.
def total_return(df):
"""
Calculate the "Total Return" of a stock when dividends are
reinvested in the stock.
The formula is:
Total_Return[t] = Total_Return[t-1] * (Dividend[t] + Share_Price[t]) / Share_Price[t-1]
:param df:
Pandas data-frame assumed to contain SHARE_PRICE and DIVIDEND.
:return:
Pandas series with the Total Return.
"""
# Copy the relevant data so we don't change it.
df2 = df[[SHARE_PRICE, DIVIDEND]].copy()
# Fill NA-values in the Dividend-column with zeros.
df2[DIVIDEND].fillna(0, inplace=True)
# Calculate the daily Total Return.
tot_ret_daily = (df2[DIVIDEND] + df2[SHARE_PRICE]) / df2[SHARE_PRICE].shift(1)
# Calculate the cumulative Total Return.
tot_ret = tot_ret_daily.cumprod()
# Replace the first row's NA with 1.0
tot_ret.values[0] = 1.0
return tot_ret
def annualized_returns(series, years):
"""
Calculate the annualized returns for all possible
periods of the given number of years.
For example, given the Total Return of a stock we want
to know the annualized returns of all holding-periods
of 10 years.
:param series:
Pandas series e.g. with the Total Return of a stock.
Assumed to be daily data.
:param years:
Number of years in each period.
:return:
Pandas series of same length as the input series. Each
day has the annualized return of the period starting
that day and for the given number of years. The end of
the series has NA for the given number of years.
"""
# Number of days to shift data. All years have 365 days
# except leap-years which have 366 and occur every 4th year.
# So on average a year has 365.25 days.
days = int(years * 365.25)
# Calculate annualized returns for all periods of this length.
# Note: It is important we have daily (interpolated) data,
# otherwise the series.shift(365) would shift much more than
# a year, if the data only contains e.g. 250 days per year.
ann_return = (series.shift(-days) / series) ** (1 / years) - 1.0
return ann_return
def prepare_ann_returns(df, years, key=PSALES, subtract=None):
"""
Prepare annualized returns e.g. for making a scatter-plot.
The x-axis is given by the key (e.g. PSALES) and the y-axis
would be the annualized returns.
:param df:
Pandas DataFrame with columns named key and TOTAL_RETURN.
:param years:
Number of years for annualized returns.
:param key:
Name of the data-column for x-axis e.g. PSALES or PBOOK.
:param subtract:
Pandas Series to be subtracted from ann-returns
to adjust for e.g. growth in sales-per-share.
:return:
(x, y) Pandas Series with key and adjusted ANN_RETURN.
"""
# Create a new data-frame so we don't modify the original.
# We basically just use this to sync the data we are
# interested in for the common dates and avoid NA-data.
df2 = pd.DataFrame()
# Copy the key-data e.g. PSALES.
df2[key] = df[key]
# Calculate all annualized returns for all periods of
# the given number of years using the Total Return.
ann_return = annualized_returns(series=df[TOTAL_RETURN],
years=years)
if subtract is None:
# Add the ann-returns to the new data-frame.
df2[ANN_RETURN] = ann_return
else:
# Calculate all annaulized returns for the series
# that must be subtracted e.g. sales-per-share.
ann_return_subtract = annualized_returns(series=subtract,
years=years)
# Subtract the ann. returns for the total return
# and the adjustment (e.g. sales-per-share).
# Then add the result to the new data-frame.
df2[ANN_RETURN] = ann_return - ann_return_subtract
# Drop all rows with NA.
df2.dropna(axis=0, how='any', inplace=True)
# Retrieve the relevant data.
x = df2[key]
y = df2[ANN_RETURN]
return x, y
def bond_annualized_returns(df, num_years):
"""
Calculate the annualized returns from investing and reinvesting in a bond.
This results in a list of Pandas Series with the annualized returns for
[1, 2, ..., max_years] investment years.
For example ann_returns[0] are for 1-year investment periods and
ann_returns[9] are for 10-year periods.
:param df:
Pandas DataFrame with BOND_YIELD data for 1-year maturity.
:param num_years:
Max number of investment years.
:return:
List of Pandas Series.
"""
# The idea is to repeatedly shift the bond-yields
# and update a cumulative product so as to get the
# compounded return through the years.
# Init the list of annualized returns. For 1-year
# investment periods these are just the bond-yields.
ann_returns = [df[BOND_YIELD].copy()]
# Init the cumulative product of bond-yields,
# which will be updated when reinvesting in the
# bonds through the years.
cum_prod = df[BOND_YIELD].copy() + 1.0
# Init the bond-yields shifted one year.
# These will be shifted 365 steps for each year.
shifted = cum_prod.copy()
# For increasing number of investment years.
# The bond-yields were used as the 1st year above.
for years in range(2, num_years + 1):
# Shift the bond-yields one year.
# Note leap-years are not taken into account so
# there will be a slight drift for longer periods,
# but it probably only causes a very small error.
shifted = shifted.shift(-365)
# Accumulate the bond-yields so cum_prod holds the
# cumulative return from reinvesting in the bonds.
cum_prod *= shifted
# Remove NA from the end of the series.
cum_prod.dropna(inplace=True)
# Calculate the annualized returns.
ann_ret = cum_prod ** (1 / years) - 1.0
# Rename the data-column.
ann_ret.rename(ANN_RETURN)
# Add to the list of annualized returns for all years.
ann_returns.append(ann_ret)
return ann_returns
########################################################################
|
#!/usr/bin/env python
"""
Cleavage prediction src tool
usage: cleavageprediction.py [-h] [-m {proteasmm_i,netchop,proteasmm_c,pcm}]
[-v VERSION] -i INPUT [-l LENGTH] [-op OPTIONS]
-o OUTPUT
Commandline tool for cleavage site prediction
optional arguments:
-h, --help show this help message and exit
-m, --method {proteasmm_i,netchop,proteasmm_c,pcm}
The name of the prediction method
-v VERSION, --version VERSION
The version of the prediction method
-i INPUT, --input INPUT
Path to the input file (in fasta format)
-l LENGTH, --length LENGTH
The length of peptides
-op OPTIONS, --options OPTIONS
Additional options that get directly past to the tool
-o OUTPUT, --output OUTPUT
Path to the output file
"""
import sys
import argparse
from Fred2.Core import Protein
from Fred2.IO import read_fasta
from Fred2.CleavagePrediction import CleavageSitePredictorFactory
def main():
#Specify CTD interface
# Every CTD Model has to have at least a name and a version, plus any of the optional attributes below them.
model = argparse.ArgumentParser(
description='Commandline tool for cleavage site prediction',
)
model.add_argument('-m',
'--method',
type=str,
choices=CleavageSitePredictorFactory.available_methods().keys(),
default="pcm",
help='The name of the prediction method'
)
model.add_argument('-v',
'--version',
type=str,
default="",
help='The version of the prediction method'
)
model.add_argument('-i',
'--input',
type=str,
required=True,
help='Path to the input file (in fasta format)'
)
model.add_argument('-l',
'--length',
type=int,
default=0,
help='The length of peptides'
)
model.add_argument('-op',
'--options',
type=str,
default="",
help="Additional options that get directly past to the tool"
)
model.add_argument('-o',
'--output',
type=str,
required=True,
help='Path to the output file'
)
args = model.parse_args()
#fasta protein
peptides = read_fasta(args.input, in_type=Protein)
if args.version == "":
predictor = CleavageSitePredictorFactory(args.method)
result = predictor.predict(peptides, options=args.method)
else:
predictor = CleavageSitePredictorFactory(args.method, version=args.version)
result = predictor.predict(peptides, options=args.method)
#if length is specified, than generate compact output
if int(args.length) > 0:
length = int(args.length)
with open(args.output, "w") as f:
f.write("Sequence\tMethod\tScore\tProtein ID\tPosition\n")
for seq_id in set(result.index.get_level_values(0)):
seq = "".join(result.ix[seq_id]["Seq"])
for start in xrange(len(seq)-(length-1)):
pep_seq = seq[start:(start+length)]
score = result.loc[(seq_id, start+(length-1)), predictor.name]
f.write(pep_seq+"\t"+predictor.name+"\t"+"%.3f"%score+"\t"+seq_id+"\t"+str(start)+"\n")
else:
result.to_csv(args.output, float_format="%.3f", sep="\t")
return 0
if __name__ == "__main__":
sys.exit(main()) |
# terrascript/resource/hashicorp/azurestack.py
# Automatically generated by tools/makecode.py (24-Sep-2021 15:13:15 UTC)
import terrascript
class azurestack_availability_set(terrascript.Resource):
pass
class azurestack_dns_a_record(terrascript.Resource):
pass
class azurestack_dns_zone(terrascript.Resource):
pass
class azurestack_lb(terrascript.Resource):
pass
class azurestack_lb_backend_address_pool(terrascript.Resource):
pass
class azurestack_lb_nat_pool(terrascript.Resource):
pass
class azurestack_lb_nat_rule(terrascript.Resource):
pass
class azurestack_lb_probe(terrascript.Resource):
pass
class azurestack_lb_rule(terrascript.Resource):
pass
class azurestack_local_network_gateway(terrascript.Resource):
pass
class azurestack_managed_disk(terrascript.Resource):
pass
class azurestack_network_interface(terrascript.Resource):
pass
class azurestack_network_security_group(terrascript.Resource):
pass
class azurestack_network_security_rule(terrascript.Resource):
pass
class azurestack_public_ip(terrascript.Resource):
pass
class azurestack_resource_group(terrascript.Resource):
pass
class azurestack_route(terrascript.Resource):
pass
class azurestack_route_table(terrascript.Resource):
pass
class azurestack_storage_account(terrascript.Resource):
pass
class azurestack_storage_blob(terrascript.Resource):
pass
class azurestack_storage_container(terrascript.Resource):
pass
class azurestack_subnet(terrascript.Resource):
pass
class azurestack_template_deployment(terrascript.Resource):
pass
class azurestack_virtual_machine(terrascript.Resource):
pass
class azurestack_virtual_machine_extension(terrascript.Resource):
pass
class azurestack_virtual_machine_scale_set(terrascript.Resource):
pass
class azurestack_virtual_network(terrascript.Resource):
pass
class azurestack_virtual_network_gateway(terrascript.Resource):
pass
class azurestack_virtual_network_gateway_connection(terrascript.Resource):
pass
__all__ = [
"azurestack_availability_set",
"azurestack_dns_a_record",
"azurestack_dns_zone",
"azurestack_lb",
"azurestack_lb_backend_address_pool",
"azurestack_lb_nat_pool",
"azurestack_lb_nat_rule",
"azurestack_lb_probe",
"azurestack_lb_rule",
"azurestack_local_network_gateway",
"azurestack_managed_disk",
"azurestack_network_interface",
"azurestack_network_security_group",
"azurestack_network_security_rule",
"azurestack_public_ip",
"azurestack_resource_group",
"azurestack_route",
"azurestack_route_table",
"azurestack_storage_account",
"azurestack_storage_blob",
"azurestack_storage_container",
"azurestack_subnet",
"azurestack_template_deployment",
"azurestack_virtual_machine",
"azurestack_virtual_machine_extension",
"azurestack_virtual_machine_scale_set",
"azurestack_virtual_network",
"azurestack_virtual_network_gateway",
"azurestack_virtual_network_gateway_connection",
]
|
"""Code for low-memory processing of tabular data.
This code works with IDotDatas, which are immutable streams of records. An IDotData has a fixed number of named columns,
and lets you take out iterators to iterate sequentially over the records (table rows). Unlike DotData, it does not provide
efficient random access to the rows, and does not allow changing the table. In return, IDotData operations require only
a small constant amount of memory, compared to DotData's linear-memory requirements.
An IDotData represents an immutable stream of records from a table with a fixed number of named columns.
It must have the following attributes:
headings - a tuple of names of the table columns.
__iter__() - returns an iterator over the records.
It must yield immutable records, which are named tuples of field values.
notes:
comparisons between IDotDatas: equality comparisons produce an IDotData for a pairwise comparisons of rows.
Boolean testing tests if there are any rows.
so, in particular, bool( a == b ) is true as long as they're both nonempty (even if different),
and is false even if they're both equally empty (because the answer is an empty sequence of booleans).
the headings also do not play a role in comparisons.
given an iter you can make a routine that returns this iter once and fails on any other attempt,
which turns an iterator into a container.
- so, imap does not work here.
we need to create a new IDotDataRoot() object,
with the specified headings,
which, whenever asked, for an iterator, will ask _us_ for an iterator,
and return an object which takes items from our iterator and selects from them.
spokojno, ehto nuzhno sdelat' akkuratno.
- so, forget abstraction, implement what you want, what each method should do, even if with code/object duplication;
then, when everything is correct, re-abstract -- it will be more clear how to abstract.
but you already know what each method should _do_.
>>> z = IDotData( names = ( 'a', 'b' ), Records = ( ( 1, 2 ), ( 1, 3 ), ( 2, 4 ), ( 2, 5 ) ) )
... # doctest: +NORMALIZE_WHITESPACE
>>>
"""
from __future__ import with_statement, division
__all__ = ( 'IDotData', )
import sys, os, logging, itertools, operator, copy, contextlib, time, numbers, types, glob, math, collections, \
heapq, inspect, abc, __builtin__
import traceback as tb
from abc import ABCMeta, abstractmethod
from Operations.MiscUtil import chomp, dbg, coerceVal, is_sorted, IsSeq, MakeSeq, MakeDir, DumpFile, SlurpFileLines, \
WaitForFileToAppear, joinstr, Sfx, IsScalar, DictGet, flatten, SystemSucceed, ReplaceFileExt, BreakString, joinstr, \
RestrictDict, MergeDicts, tabwriten, AtomicForIsSeq, DbgIter, Dict, OpenForRead, OpenForWrite, SumKeeper, tmap, \
MapPath, DictGetNotNone, tabwrite, AddFileSfx, SplitStr, FirstVal, ExtractOpts, IsFileType, iter_ith
try:
import numpy as np
haveNumpy = True
except ImportError:
haveNumpy = False
try:
from Classes.DotData import DotData
haveDotData = True
except ImportError:
haveDotData = False
class IDotDataRecord(collections.Sequence):
"""Represents an immutable named tuple (i.e. where you can access tuple components by name as well
as by index). Very similar to collections.namedtuple, but column names can be arbitrary strings and
need not be valid Python identifiers.
IDotDataRecord represents one record (table row) of an IDotData, returned by an iterator taken out
over IDotData.
Fields:
colName2num - map from column name to integer column number. Used for accessing tuple
elements by column name. This map is created once for each .tsv or .data/ file,
and is shared by all IDotDataRecords read from that file.
lineVals - string values of the fields of this record. We do not convert them
to non-string datatypes until asked -- because often, they're just passed around as-is,
so not converting them from string to int or float and then back saves time.
fname - name of the file we are reading; needed for error reporting only.
headings - names of the columns. needed for error reporting.
"""
def __init__(self, lineVals, colName2num, headings, fname = None ):
self.lineVals = tuple( lineVals )
self.colName2num = colName2num
self.headings = headings
assert len( self.lineVals ) == len( self.headings ) == len( self.colName2num )
def __getattr__(self, name):
"""Get value of one element of this record, by its field name"""
if name.startswith( '__' ): return object.__getattr__( self, name )
try: return coerceVal( self.lineVals[ self.colName2num[ name ] ] )
except KeyError:
raise AttributeError(name)
def __getitem__(self, key):
"""Get value of one field of this named tuple, either by field name or by position (column number).
The key can be a sequence of keys, in which a tuple of field values is returned.
"""
if IsSeq( key ): return tuple( self[k] for k in key )
return coerceVal( self.lineVals[ self.colName2num[ key ] if isinstance( key, types.StringTypes ) else key ] )
def GetStrItem(self, key):
"""Get value of one field of this named tuple, either by field name or by position (column number).
The key can be a sequence of keys, in which a tuple of field values is returned.
The field is left as a string and is not coerced.
"""
if IsSeq( key ): return tuple( self[k] for k in key )
return self.lineVals[ self.colName2num[ key ] if isinstance( key, types.StringTypes ) else key ]
def __iter__(self): return itertools.imap( coerceVal, self.lineVals )
def __repr__(self): return str( zip( self.headings, self.lineVals ) )
def __len__(self): return len(self.headings)
def __add__(self,other):
"""Return a concatenation of the two records"""
return IDotDataRecord( lineVals = self.lineVals + other.lineVals,
colName2num = MergeDicts( self.colName2num, other.colName2num ),
headings = self.headings + other.headings )
def __eq__(self,other):
other = MakeSeq( other )
return len(self) == len(other) and all([ a == b for a,b in itertools.izip( self, other ) ])
def __ne__(self,other): return not self == other
def __lt__(self,other):
other = MakeSeq( other )
return any( a < b for a,b in itertools.izip( self, other ) ) \
or len(self) < len(other) and all([ a == b for a,b in itertools.izip( self, other ) ])
def __le__(self,other): return self == other or self < other
def __gt__(self,other): return not self <= other
def __ge__(self,other): return not self < other
def __hash__(self): return hash(tuple(lineVals))
def asDict(self): return dict( ( n, v ) for n, v in zip( self.headings, self ) )
class my_dtype(object):
"""For compatibility with numpy arrays which have a dtype field, we define a class that mimics the dtype interface
and add a field of this type to IDotData."""
def __init__(self, names ):
self.names = names
class IDotDataRoot(collections.Iterable, AtomicForIsSeq):
__metaclass__ = ABCMeta
"""Abstract base class for various IDotData implementations.
The implementation (a sublcass of IDotDataRoot) provides the tuple of headings when it calls
IDotDataRoot's __init__() method, and provides the _iter() method which returns an iterator yielding
the records as tuples. IDotDataRoot then adds many useful operations for manipulating IDotDatas.
Fields:
headings - names of columns of this IDotData. the names can be arbitrary strings and need not be valid Python
identifiers.
colName2num - map from column name to its position in headings. used for fast lookup of fields by their name.
parents - IDotDatas from which this IDotData's data is derived, if any.
notes:
- should we _require_ that our inputs are repeatable collections and not just iters?
that's not the common case though...
- have the method, IDotData_mergeOnKeyCols() and manage other static methods that way.
alternately, assign this as an attribute to the IDotData function object.
"""
# Mark IDotData as not hashable
__hash__ = None
isIDotData = True
@abstractmethod
def _iter(self):
"""Return the next record, as either a tuple or a single value or an IDotDataRecord """
pass
def __init__(self, headings, parents = () ):
headings = tuple( MakeSeq( headings ) )
#assert headings
if len( set( tuple( headings ) ) ) != len( headings ):
dbg( 'headings' )
assert len( set( tuple( headings ) ) ) == len( headings ), ( 'headings=%s' % str( headings ) )
self.headings = headings
self.colName2num = dict( ( colName, colNum ) for colNum, colName in enumerate( self.headings ) )
self.parents = parents
self.isInfinite = any( p.isInfinite for p in parents )
self.len = None
self.dtype = my_dtype( names = headings )
self.creationTraceback = tb.extract_stack()
def __getitem__(self,item):
"""Returns a sub-rectangle of this IDotData. What is returned depends on the type of item:
- if item is a string, it must be a column name, and we return a one-column IDotData consisting of just that
column from this IDotData
- if the item is a sequence of strings, each string must be a column name, and we return an IDotData
consisting of these columns of this IDotData
- if the item is a sequence of Booleans, we return an IDotData consisting of those rows of this IDotData
for which item is True.
"""
if all([ isinstance( it, types.StringTypes ) for it in MakeSeq( item ) ]):
for it in MakeSeq( item ):
if it not in self.headings: raise AttributeError( item )
return IDotDataGetColumns( parent = self, item = item )
if isinstance( item, ( types.IntType, types.LongType ) ): return iter_ith( self, item )
if isIDotData( item ): return IDotDataFilterBool( parent = self, filter = item )
if hasattr( type( item ), 'isDotData' ): return self[ IDotData.fromDotData( item ) ]
asDotData = self.toDotData()[ item ]
if hasattr( type( asDotData ), 'isDotData' ): return IDotData.fromDotData( asDotData )
return IDotData( headings = self.headings, Columns = ( asDotData, ) )
def __getattr__(self,name):
"""Return a given column"""
try: return collections.Iterable.__getattr__(self,name)
except AttributeError: return self[name] if name != '__setstate__' else None
def __iter__(self):
"""Returns an iterator over the records of this IDotData. If this IDotData consists of exactly one column,
yields values from that column; otherwise, yields named tuples of values.
"""
singleColumn = ( len( self.headings ) == 1 )
for r in self._iter():
r = MakeSeq( r )
if len( r ) != len( self.headings ):
dbg( 'len(r) len(self.headings) r tuple(r) self.headings zip(self.headings,r)' )
assert len( r ) == len( self.headings )
if singleColumn: yield coerceVal( r[0] )
else:
yield r if isinstance( r, IDotDataRecord ) else \
IDotDataRecord( lineVals = r, colName2num = self.colName2num, headings = self.headings )
def recordsIter(self):
"""Returns an iterator over the records of this IDotData. This iterator always yields IDotDataRecords,
even when the IDotData consists of exactly one column.
"""
return iter( self ) if len( self.headings ) > 1 else self.wrapIter()
def wrapIter(self):
assert len( self.headings ) == 1
for r in self:
yield IDotDataRecord( lineVals = ( r, ), colName2num = self.colName2num, headings = self.headings )
def flatIter(self):
"""Returns an iterator that iterates over all values in all records of this IDotData, returning a flat
stream of just the values."""
for r in self:
for v in MakeSeq( r ):
yield v
# def argsort(self, *cols):
# """Returns the tuple that would sort this IDotData by the given columns.
# Note that this currently requires reading the whole thing into memory."""
# assert len( cols ) == 1
# return np.array( tuple( self[ cols[0] ] ) ).argsort()
def argsort(self, *args, **kwargs): return np.argsort( np.asarray( self ), *args, **kwargs )
def getColNum( self, col ):
"""Returns the column number from column name"""
return self.colName2num[ col ]
def sortedOn(self, *cols):
"""Return a version of this IDotData that is sorted on the specified columns
(the first column being the primary key, the second column the secondary key, etc).
Note that at the moment, the results in instantiating the full IDotData in memory.
Later we'll add options for external-memory sorting.
"""
return self[ np.lexsort( [ self[c] for c in reversed( cols ) ] ) ]
sortedBy = sortedOn
def numCols(self):
"""Return number of columns in this IDotData."""
return len(self.headings)
def takewhile(self, pred):
"""Returns an IDotData consisting of those first rows of this IDotData for which the callable pred returns True.
"""
return IDotDataTakeWhile( parent = self, pred = pred )
def oneSlice( self, colNames, partNum, totalParts, numChunks = None):
"""Return one slice of an IDotData split by specified column(s)"""
return IDotDataOneSlice( parent = self, **Dict( 'colNames partNum totalParts numChunks' ) )
def oneRegion( self, keyCols, beg, end ):
"""Return one region of an IDotData split by specified column(s)"""
return IDotDataOneRegion( parent = self, **Dict( 'keyCols beg end' ) )
# versions for all other itertools methods, esp. groupby.
def hstack(self, *iDotDatas):
"""Return the horizontal (side-by-side) stacking of this IDotData and specified IDotDatas"""
return IDotDataHStack( self, *iDotDatas )
def addCols(self, names, cols):
"""Return this IDotData with horizontally stacked columns added."""
return self.hstack( IDotData( names = names, Columns = cols ) )
def addCol(self, name, col):
"""Return this IDotData with one horizontally stacked column added."""
return self.addCols( names = (name,), cols = (col,) )
def vstack(self, *iDotDatas, **kwargs):
"""Return the vertical stacking of this IDotData and specified IDotDatas"""
return IDotDataVStack( self, *iDotDatas, **kwargs )
def vstackFromIterable(self, iDotDatas, headings = None):
"""Return the vertical stacking of this IDotData and specified IDotDatas"""
return IDotDataVStackFromIterable( self, iDotDatas = iDotDatas, headings = headings )
def filter(self, pred):
"""Return an IDotData consisting of those rows of this IDotData for which the specified predicate returns True"""
return IDotDataFilter( self, pred )
def removeDups(self, keyCols = None ):
"""Return an IDotData consisting of non-duplicate rows of this IDotData. Rows are considered
duplicate if they're adjacent and equal at column(s) keyCols. Of each group of duplicate rows, the first
is selected to be part of the new IDotData."""
return IDotDataRemoveDups( self, keyCols )
def firstWhere(self, pred):
"""Return the first row, that matches the given condition.
If no such row, raises IndexError."""
for r in self:
if pred( r ): return r
raise IndexError( 'No row matches the specified condition!' )
def mapRecords(self, func, headings = None):
"""Return an IDotData whose records (rows) are obtained by applying a transformer function to the records (rows)
of this IDotData."""
if not headings: headings = self.headings
return IDotData.mapRecords( func = func, iDotDatas = (self,), headings = headings )
def mapVals(self, f):
"""Return an IDotData with all values mapped by the given function"""
return self.mapRecords( ( lambda x: f(x) ) if self.numCols() == 1 else ( lambda r: map( f, r ) ) )
def addComputedCols( self, newColNames, newColFn):
"""Add a column computed from the records according to specified function"""
if isinstance( newColNames, types.StringTypes ) and ( ' ' in newColNames or '\t' in newColNames ):
newColNames = tuple( newColNames.split( '\t' if '\t' in newColNames else None ) )
return self.mapRecords( func = lambda r: tuple( r ) + tuple( MakeSeq( newColFn( r ) ) ),
headings = self.headings + tuple( MakeSeq( newColNames ) ) )
def replaceCol( self, col, newColFn ):
"""Create a new IDotData where values in given col are replaced according to a given function on records"""
colNum = self.colName2num[ col ]
def mapRec( r ):
t = tuple( r )
return tuple( t[ :colNum ] ) + tuple( MakeSeq( newColFn( r ) ), ) + tuple( t[ colNum+1: ] )
return self.mapRecords( func = mapRec )
def starmap(self, func, headings = None):
if not headings: headings = self.headings
return IDotData.starmap( func = func, iDotData = self, headings = headings )
def renameCols(self, renamings):
"""Get a version of this IDotData with some columns renamed.
Parameters:
renamings - map from old column name to new column name. Columns not renamed
keep their old name.
"""
return IDotDataRenameCols( parent = self, renamings = renamings )
def columns(self, cols = None):
"""Return the columns, as a list. Convenient for mapping a function over the columns."""
if cols == None: cols = self.headings
return [ self[ colName ] for colName in cols ]
def __doOp(self, other, func, flip = False, headings = ( 'V', ) ):
"""Return an IDotData whose rows are obtained by applying the specified binary function to the corresponding
rows of this IDotData and another IDotData.
If 'other' is a scalar value, first promote it to a one-column IDotData consisting of that value repeated.
"""
if IsScalar( other ): return self.__doOp( IDotData.repeat( heading = 'N', value = other ), func, flip, headings )
if not isIDotData( other ): return NotImplemented
return IDotData.mapRecords( func = func, iDotDatas = ( self, other ) if not flip else ( other, self ),
headings = headings )
def __doROp(self, other, func, headings = ( 'V', ) ):
return self.__doOp( other, func, flip = True, headings = headings )
def __ge__(self, other): return self.__doOp( other, operator.ge )
def __gt__(self, other): return self.__doOp( other, operator.gt )
def __le__(self, other): return self.__doOp( other, operator.le )
def __lt__(self, other): return self.__doOp( other, operator.lt )
def __eq__(self, other): return self.__doOp( other, operator.eq )
def __ne__(self, other): return self.__doOp( other, operator.ne )
def __add__(self, other): return self.__doOp( other, operator.add )
def __sub__(self, other): return self.__doOp( other, operator.sub )
def __mul__(self, other): return self.__doOp( other, operator.mul )
def __div__(self, other): return self.__doOp( other, operator.div )
def __truediv__(self, other): return self.__doOp( other, operator.truediv )
def __floordiv__(self, other): return self.__doOp( other, operator.floordiv )
def __and__(self, other): return self.__doOp( other, operator.and_ )
def __or__(self, other): return self.__doOp( other, operator.or_ )
def __xor__(self, other): return self.__doOp( other, operator.xor )
def __lshift__(self, other): return self.__doOp( other, operator.lshift )
def __rshift__(self, other): return self.__doOp( other, operator.rshift )
def __mod__(self, other): return self.__doOp( other, operator.mod )
def __divmod__(self, other): return self.__doOp( other, divmod, headings = ( 'div', 'mod' ) )
def __pow__(self, other): return self.__doOp( other, operator.pow )
def __radd__(self, other): return self.__doROp( other, operator.add )
def __rsub__(self, other): return self.__doROp( other, operator.sub )
def __rmul__(self, other): return self.__doROp( other, operator.mul )
def __rdiv__(self, other): return self.__doROp( other, operator.div )
def __rtruediv__(self, other): return self.__doROp( other, operator.truediv )
def __rfloordiv__(self, other): return self.__doROp( other, operator.floordiv )
def __rmod__(self, other): return self.__doROp( other, operator.mod )
def __rdivmod__(self, other): return self.__doROp( other, divmod, headings = ( 'div', 'mod' ) )
def __rpow__(self, other): return self.__doROp( other, operator.pow )
def __rand__(self, other): return self.__doROp( other, operator.and_ )
def __ror__(self, other): return self.__doROp( other, operator.or_ )
def __rxor__(self, other): return self.__doROp( other, operator.xor )
def __rlshift__(self, other): return self.__doROp( other, operator.lshift )
def __rrshift__(self, other): return self.__doROp( other, operator.rshift )
def __neg__(self): return self.mapRecords( operator.neg )
def __pos__(self): return self.mapRecords( operator.pos )
def __abs__(self): return self.mapRecords( operator.abs )
def __invert__(self): return self.mapRecords( operator.invert )
def __len__(self):
"""Return the number of data rows in this IDotData. Note that the first time this is called,
it may take linear time to run."""
if self.len is None: self.len = sum( itertools.imap( lambda x: 1, self ) )
return self.len
def __nonzero__(self):
"""Return True if self has at least one row, False otherwise"""
try:
next( iter( self ) )
return True
except StopIteration: return False
#def __contains__(self, val):
# return 1
#def __neg__(self): return self.
def dbg(self, msg = None):
print '=============================='
if msg is not None:
print '\n' + msg + '\n'
print '=============================='
tabwrite( sys.stdout, *self.headings )
nlines = 0
for line in self:
tabwrite( sys.stdout, *line )
# sys.stdout.write( '\n' + '\t'.join( map( str, line )
# if ( hasattr(line,'__iter__') and not isinstance(line,types.StringTypes) )
# else (str(line),) ) )
nlines += 1
sys.stdout.write( '\n%d rows\n' % nlines )
print '=============================='
def dbgGraph(self, fname):
"""Write a graph representation of the current object"""
with open( fname, 'w' ) as f:
f.write( 'digraph A {\n' )
nodes = [ self ]
while nodes:
n = nodes.pop()
f.write( 'n%d' % id( n ) );
f.write( '[ shape = "box", label = "%s" ];\n' % '\\n'.join( BreakString( str( n ), 20 ) ) )
for p in n.parents:
nodes.append( p )
f.write( 'n%d -> n%d\n' % ( id( p ), id( n ) ) )
f.write( '}\n' )
SystemSucceed( 'dot -Tsvg -o' + ReplaceFileExt( fname, '.svg' ) + ' ' + fname )
def save(self, fname, useHeadings = True, fileType = None, sep = '\t', comments = () ):
"""Save this IDotData to either a .tsv file or to a .data/ directory, depending on the extension"""
with IDotData.openForWrite( fname, self.headings if useHeadings else None, **Dict( 'sep fileType comments' ) ) as f:
for line in self: f.writeRecord( line )
return self
saveToSV = save
saveToDotData = save
def toDotData(self):
"""Convert to DotData"""
assert haveDotData
return DotData.fromIDotData( self )
def __array__(self):
"""Convert this to a numpy.ndarray. This lets many numpy methods take
IDotDatas as arguments."""
return np.array( tuple( self ) ) if self.numCols() == 1 else self.toDotData()
def nanmax(self):
isFirst = True
for v in self.flatIter():
if isFirst or v > result: result = v
isFirst = False
return result if not isFirst else float('nan')
def groupby(self, *cols, **kwargs):
"""Yield groups of rows of this IDotData, grouped by values in the given columns, as individual IDotDatas.
Params:
cols - column name or sequence of column names of this IDotData.
Adjacent rows for which these columns have a given
combination of values will be grouped together in the output.
multiPass - if True ( default ), the IDotDatas yielded by the returned iterator (see 'Returns' below)
may be iterated over multiple times; if False, they can be iterated over only once (but this takes less
memory). If an integer k, we yield tuples of the form (key, idd_1, idd_2, ..., idd_k) where each of
idd_i is a single-pass IDotData over the grouped rows.
Returns:
an iterator yielding pairs of values; the first value is the value or tuple of values of the key columns;
the second is an IDotData representing the next group of adjacent rows with a particular combination of
values in the key columns.
"""
multiPass = DictGet( kwargs, 'multiPass', True )
if not isinstance( multiPass, bool ):
for v in itertools.izip( *[ self.groupby( *cols, multiPass = False ) for i in range( multiPass ) ] ):
yield ( v[0][0], ) + tmap( operator.itemgetter( 1 ), v )
return
for k, g in itertools.groupby( self, operator.itemgetter( cols ) ):
yield ( k[0] if isinstance( k, tuple ) and len( k ) == 1 else k ), \
IDotData.fromIterable( headings = self.headings,
iterable = tuple( g ) if multiPass else g )
def selectRowsUsingOneCriterion(self, expr):
"""Return selected rows.
If expr is a string or a code object, returns rows for which expr is True. Expr is
either a string or a code object; if a string, it is compiled into a code object. The
expression is evaluated for each row, in an environment in which column names are
bound to column values in that row. A DotData containing copies of rows for which
the expression evaluates to True is returned.
If expr is a map of column names to values, returns rows for which the specified
columns have the specified values. Column names mapped to None do not
participate in the filtering.
You can get the same effect by using self[expr], but the [] operator is getting very
overloaded...
See also: selectRows(), which can select rows according to a list of criteria.
"""
if isinstance(expr, types.CodeType) or isinstance(expr, types.StringTypes):
exprCode = expr if isinstance(expr, types.CodeType) \
else compile( expr, sys._getframe(0).f_code.co_filename, 'eval' )
# the expression may reference only the column names (as python variables),
# or it may reference the caller's local and global vars.
# check which is the case here, and if needed get the caller's
# local and global variable mappings.
callerGlobals = { }
callerLocals = None
exprVars = frozenset( exprCode.co_names )
if not exprVars <= frozenset( self.dtype.names ):
callerFrame = inspect.currentframe()
thisFileName = callerFrame.f_code.co_filename
while ( callerFrame.f_code.co_filename == thisFileName ):
callerFrame = callerFrame.f_back
callerLocals = RestrictDict( callerFrame.f_locals, exprVars )
callerGlobals = RestrictDict( callerFrame.f_globals, exprVars )
del callerFrame
def evalRow( rec, cGlobals = callerGlobals, cLocals = callerLocals ):
r = dict( ( n, v ) for n, v in zip( self.headings, rec ) )
return eval( exprCode, cGlobals,
r if not cLocals else MergeDicts( cLocals, r ) )
result = self.filter( evalRow )
del callerGlobals, callerLocals
return result
elif callable(expr):
return self.filter( expr )
elif isinstance(expr, dict):
exprItems = frozenset( [ (columnName, val) for columnName, val in expr.items() if val != None ] )
return self.filter( lambda r: exprItems <= frozenset( r.asDict().items() ) )
else:
raise TypeError('selectRows() requires either an expression (string or code object) or a dictionary')
def selectRows(self, expr, *exprs):
"""Returns a copy of rows matching all criteria in a list. See selectRowsAux()
for documentation of the criteria.
This method takes a variable number of arguments; each argument is a
separate criterion, and rows are returned which match ALL the criteria.
See also: __getitem__().
"""
result = self;
for e in (expr,) + exprs:
result = result.selectRowsUsingOneCriterion(e)
return result
def getColumnStats(self, *cols):
"""For each of the specified columns, compute some statistics.
Return the results as a new IDotData with one row per column in 'cols',
and the headings 'col', 'mean', 'std', 'n', 'ntot'. The meanings of the
columns are:
n - the number of non-NaN values in the column; these were the values
used for the mean and stddev computation.
ntot - the total number of values seen, including NaNs. same as
self.numRows().
"""
if len( cols ) == 1 and IsSeq( cols[0] ): cols = cols[0]
meansStds = imeanstd_plusStats( self[ cols ] )
if len( cols ) == 1: meansStds = [ meansStds ]
return IDotData( names = 'col mean std n ntot',
Records = [ ( col, mean, std, n, ntot )
for col, ( mean, std, n, ntot ) in zip( cols, meansStds ) ] )
def normalizeColumns(self, *cols, **kwargs):
"""Return a version of this IDotData with the specified columns normalized and the remaining columns unchanged"""
cols = flatten( cols )
if 'meansStds' in kwargs: meansStds = kwargs[ 'meansStds' ]
else:
meansStds = imeanstd( self[ cols ] )
if len( cols ) == 1: meansStds = [ meansStds ]
col2meanStd = dict( zip( cols, meansStds ) )
colMeanStds = [ DictGet( col2meanStd, c, ( None, None ) ) for c in self.headings ]
return self.mapRecords( lambda r: [ v if mean is None else ( ( v - mean ) / std if std > 0 else np.nan )
for v, ( mean, std ) in zip( r, colMeanStds ) ] )
def normalizeColumnsWithinGroups(self, cols, groupCols, normedSfx = None, col2normed = {}, meanSfx = None,
stdSfx = None, countSfx = None ):
"""Return a version of this IDotData with the specified columns normalized within row groups defined by groupCols
(each group of adjacent rows that have an identical combination of values in columns 'groupCols' is one row group),
and the remaining columns unchanged. If col2normed is given, it specifies which columns' normalized versions
should be added as new columns rather than replacing the old columns."""
cols = flatten( MakeSeq( cols ) )
groupCols = flatten( MakeSeq( groupCols ) )
colNums = [ self.colName2num[ c ] for c in cols ]
# Set up a place to keep the running sum and sum-of-squares for each
# column to be normalized. These are allocated once and then reused
# within each row block.
sums = [ None ] * len( cols )
sumsSq = [ None ] * len( cols )
for i in range( len( cols ) ):
sums[ i ] = SumKeeper()
sumsSq[ i ] = SumKeeper()
counts = np.zeros( len( cols ), dtype = int )
colsSet = frozenset( cols )
# var: isNormeds - for each of the original columns, whether
# a normalized version of this column will be computed.
isNormeds = [ h in colsSet for h in self.headings ]
colMeans = np.zeros( len( self.headings ) )
colStds = np.zeros( len( self.headings ) )
extraCols = []
extraColsFrom = []
for colNum, c in zip( colNums, cols ):
customName = ( c in col2normed and col2normed[ c ] != c )
if normedSfx or customName:
extraCols.append( col2normed[ c ] if customName else c + Sfx( normedSfx ) )
extraColsFrom.append( colNum )
def GetNormedRows():
yield self.headings + tuple( extraCols ) \
+ ( tuple( [ c + Sfx( meanSfx ) for c in cols ] ) if meanSfx else () ) \
+ ( tuple( [ c + Sfx( stdSfx ) for c in cols ] ) if stdSfx else () ) \
+ ( tuple( [ c + Sfx( countSfx ) for c in cols ] ) if countSfx else () )
for groupId, rows1, rows2 in self.groupby( *groupCols, multiPass = 2 ):
#
# Determine the mean and stddev for each column on which we're normalizing
#
for i in range( len( cols ) ):
sums[ i ].clear()
sumsSq[ i ].clear()
counts.fill( 0 )
for r in rows1:
for i, colNum in enumerate( colNums ):
x = r[ colNum ]
if not ( math.isnan( x ) or math.isinf( x ) ):
sums[ i ] += x
sumsSq[ i ] += x*x
counts[ i ] += 1
for i, colNum in enumerate( colNums ):
if counts[ i ]:
colMeans[ colNum ] = sums[ i ].getSum() / counts[ i ]
colStds[ colNum ] = \
math.sqrt( sumsSq[ i ].getSum() / counts[ i ] - colMeans[ colNum ]*colMeans[ colNum ] )
else:
colMeans[ colNum ] = np.nan
colStds[ colNum ] = np.nan
# Now normalize the rows in this group using the means and stds determined above.
for r in rows2:
r_new = [ x if not isNormed
else ( np.nan if math.isnan( colMean ) or colStd == 0.0
else ( x - colMean ) / colStd ) for x, isNormed, colMean, colStd in
zip( r, isNormeds, colMeans, colStds ) ]
newVals = []
for ecFrom in extraColsFrom:
newVals.append( r_new[ ecFrom ] )
r_new[ ecFrom ] = r[ ecFrom ]
yield r_new + newVals + ( [ colMeans[i] for i in colNums ] if meanSfx else [] ) + \
( [ colStds[j] for j in colNums ] if stdSfx else [] ) + \
( [ counts[k] for k in range( len( colNums ) ) ] if countSfx else [] )
return IDotData.fromFn( GetNormedRows )
def summarizeColumnsWithinGroups(self, cols, groupCols, groupsAreContiguous = True ):
"""Return an IDotData with the specified columns summarized within row groups defined by groupCols
(each group of adjacent rows that have an identical combination of values in columns 'groupCols' is one row group).
>>> z = IDotData( names = ( 'a', 'b' ), Records = ( ( 1, 2 ), ( 1, 3 ), ( 2, 4 ), ( 2, 5 ) ) )
>>> z.summarizeColumnsWithinGroups( cols = 'b', groupCols = 'a' ).dbg()
... # doctest: +NORMALIZE_WHITESPACE
==============================
a b_count b_sum b_sumSq b_numNaN
1 2 5.0 13.0 0
2 2 9.0 41.0 0
<BLANKLINE>
2 rows
==============================
>>> z.summarizeColumnsWithinGroups( cols = 'b', groupCols = () ).dbg()
... # doctest: +NORMALIZE_WHITESPACE
==============================
b_count b_sum b_sumSq b_numNaN
4 14.0 54.0 0
<BLANKLINE>
1 rows
==============================
>>> z = IDotData( names = ( 'a', 'b' ), Records = ( ( 1, 2 ), ( 2, 4 ), ( 2, 5 ), ( 1, 3 ) ) )
>>> z.summarizeColumnsWithinGroups( cols = 'b', groupCols = 'a', groupsAreContiguous = False ).dbg()
... # doctest: +NORMALIZE_WHITESPACE
==============================
a b_count b_sum b_sumSq b_numNaN
1 2 5.0 13.0 0
2 2 9.0 41.0 0
<BLANKLINE>
2 rows
==============================
"""
groupCols = flatten( MakeSeq( groupCols ) )
if not groupsAreContiguous and groupCols:
return self.sortedOn( *groupCols ).summarizeColumnsWithinGroups( cols = cols, groupCols = groupCols,
groupsAreContiguous = True )
cols = flatten( MakeSeq( cols ) )
colNums = [ self.colName2num[ c ] for c in cols ]
# Set up a place to keep the running sum and sum-of-squares for each
# column to be normalized. These are allocated once and then reused
# within each row block.
sums = [ None ] * len( cols )
sumsSq = [ None ] * len( cols )
for i in range( len( cols ) ):
sums[ i ] = SumKeeper()
sumsSq[ i ] = SumKeeper()
counts = np.zeros( len( cols ), dtype = int )
nanCounts = np.zeros( len( cols ), dtype = int )
def GetSummarizedRows():
#summarizedRowsHeadings = tuple( groupCols ) + tuple([ col + '_' + sfx for col in cols for sfx in ( 'count', 'sum', 'sumSq', 'numNaN' ) ])
#dbg( 'summarizedRowsHeadings' )
yield tuple( groupCols ) + tuple([ col + '_' + sfx for col in cols for sfx in ( 'count', 'sum', 'sumSq', 'numNaN' ) ])
prevGroupId = None
for groupId, rows1 in self.groupby( *groupCols ):
thisGroupId = tuple( MakeSeq( groupId ) )
assert prevGroupId is None or prevGroupId < thisGroupId
for i in range( len( cols ) ):
sums[ i ].clear()
sumsSq[ i ].clear()
counts.fill( 0 )
for r in rows1:
for i, colNum in enumerate( colNums ):
x = r[ colNum ]
if math.isnan( x ) or math.isinf( x ):
nanCounts[ i ] += 1
else:
sums[ i ] += x
sumsSq[ i ] += x*x
counts[ i ] += 1
yield thisGroupId + tuple( reduce( operator.concat, [ ( counts[ i ], sums[ i ].getSum(), sumsSq[ i ].getSum(), nanCounts[ i ] ) for i in range( len( cols ) ) ] ) )
return IDotData.fromFn( GetSummarizedRows )
@staticmethod
def mergeColumnSummaries( iDotDatas, cols, groupCols ):
"""Merge column summaries produced by summarizeColumnsWithinGroups"""
dbg( '"mergeColumnSummaries" cols groupCols' )
def GetMergedRows():
ourHeadings = tuple( groupCols ) + tuple([ col + '_' + sfx for col in cols for sfx in ( 'count', 'sum', 'sumSq', 'numNaN' ) ])
dbg( 'ourHeadings' )
yield tuple( groupCols ) + tuple([ col + '_' + sfx for col in cols for sfx in ( 'count', 'sum', 'sumSq', 'numNaN' ) ])
sumCols = ( 'count', 'sum', 'sumSq', 'numNaN' )
keyHeadings = tuple( 'grp_' + g for g in groupCols )
dbg( 'keyHeadings' )
for r in IDotData.mergeOnKeyCols( iDotDatas = iDotDatas, cols = ( groupCols, ) * len( iDotDatas ),
blanks = ( ( np.nan, ) * len( groupCols ) +
( 0, ) * len( cols ) * len( sumCols ), ) * len( iDotDatas ),
keyHeadings = keyHeadings ):
dbg( '"rrrrrr" r.headings' )
yield tuple( list( r[ keyHeadings ] ) + [ sum([ r[ col + '_' + sfx + ( ( '_%d' % i ) if len( iDotDatas ) > 1 else '' ) ]
for i in range( len( iDotDatas ) ) ])
for col in cols for sfx in sumCols ])
return IDotData.fromFn( GetMergedRows )
def addMeanStdCols( self, cols ):
"""Add columns representing mean and std within each group.
>>> z = IDotData( names = ( 'a', 'b' ), Records = ( ( 1, 2 ), ( 1, 3 ), ( 2, 4 ), ( 2, 5 ) ) )
>>> z.summarizeColumnsWithinGroups( cols = 'b', groupCols = 'a' ).addMeanStdCols( cols = 'b' ).dbg()
... # doctest: +NORMALIZE_WHITESPACE
==============================
a b_count b_sum b_sumSq b_numNaN b_mean b_std
1 2 5.0 13.0 0 2.5 0.5
2 2 9.0 41.0 0 4.5 0.5
<BLANKLINE>
2 rows
==============================
"""
cols = MakeSeq( cols )
def getMeanStd( a_sum, sumSq, count ):
if count < 1: return ( np.nan, np.nan )
avg = a_sum / count
return ( avg, math.sqrt( np.max( ( 0, sumSq / count - avg * avg ) ) ) )
return self.addComputedCols( newColNames = [ c + sfx for c in cols for sfx in ( '_mean', '_std' ) ],
newColFn = lambda r: \
reduce( operator.concat,
[ getMeanStd( r[ c + '_sum'], r[ c + '_sumSq' ], r[ c + '_count' ] ) for c in cols ], () ) )
def normalizeColumnsWithinGroups_using_means( self, cols, groupCols, means, groupsAreContiguous = True ):
"""Normalize the specified columns of a table within groups.
Params:
cols - the columns to be normalized
groupCols - the columns that define the groups: rows that have the same combination of values
in the group columns, are in the same group.
means - the means and stds computed e.g. with addMeanStdCols
groupsAreContiguous - if True, rows belonging to the same group must be contiguous in the table;
if False, no such assumption is made.
>>> z = IDotData( names = ( 'a', 'b' ), Records = ( ( 1, 2 ), ( 1, 3 ), ( 2, 4 ), ( 2, 5 ) ) )
>>> means = z.summarizeColumnsWithinGroups( cols = 'b', groupCols = 'a' ).addMeanStdCols( cols = 'b' )
>>> z.normalizeColumnsWithinGroups_using_means( cols = 'b', groupCols = 'a', means = means ).dbg()
... # doctest: +NORMALIZE_WHITESPACE
==============================
a b
1 -1.0
1 1.0
2 -1.0
2 1.0
<BLANKLINE>
4 rows
==============================
"""
if not groupsAreContiguous: grp2means = dict([ ( r[ groupCols ], r ) for r in means ])
cols = flatten( MakeSeq( cols ) )
groupCols = flatten( MakeSeq( groupCols ) )
isNormeds = [ h in cols for h in self.headings ]
meanCols = [ means.colName2num[ h + '_mean' ] if isNormed else -1 for h, isNormed in zip( self.headings, isNormeds ) ]
stdCols = [ means.colName2num[ h + '_std' ] if isNormed else -1 for h, isNormed in zip( self.headings, isNormeds ) ]
def GetRows():
yield self.headings
if groupsAreContiguous: meansIter = iter( means )
curGroup = None
for r in self:
if not groupsAreContiguous:
newGroup = True
curGroup = grp2means[ r[ groupCols ] ]
else:
newGroup = False
while curGroup is None or curGroup[ groupCols ] < r[ groupCols ]:
curGroup = next( meansIter )
newGroup = True
if newGroup:
curMeans = [ curGroup[ meanCol ] if meanCol >= 0 else np.nan for meanCol in meanCols ]
curStds = [ curGroup[ stdCol ] if stdCol >= 0 else np.nan for stdCol in stdCols ]
yield tuple([ ( ( val - mean_val ) / std_val if std_val > 0 else 0.0 ) if isNormed else val
for val, isNormed, mean_val, std_val in zip( r, isNormeds, curMeans, curStds ) ])
return IDotData.fromFn( GetRows )
def nlargest(self, n, *cols ):
"""Return a new IDotData consisting of n largest rows of this IDotData, *sorted from largest to smallest*
(i.e. NOT in original order), as given by comparison on columns cols.
Note that this forces the immediate reading of all values of this IDotData as soon as the first row is requested;
however, only O(n) memory will be used.
"""
if not cols: cols = self.headings
def GetResult():
yield self.headings
for r in heapq.nlargest( n, self.recordsIter(), key = operator.itemgetter( cols ) ): yield r
return IDotData.fromFn( GetResult )
def nsmallest(self, n, *cols ):
"""Return a new IDotData consisting of n smallest rows of this IDotData, *sorted from smallest to largest*
(i.e. NOT in original order), as given by comparison on columns cols.
Note that this forces the immediate reading of all values of this IDotData as soon as the first row is requested;
however, only O(n) memory will be used.
"""
if not cols: cols = self.headings
def GetResult():
yield self.headings
for r in heapq.nsmallest( n, self.recordsIter(), key = operator.itemgetter( cols ) ): yield r
return IDotData.fromFn( GetResult )
def isPrimaryKey(self, columnName):
"""Test whether the given column is a primary key, i.e. that each row has a
unique value in this column, different from the value in any other row."""
seen = set()
for x in self[ columnName ]:
if x in seen: return False
seen.add( x )
return True
def numRows(self):
i = 0
for r in self: i += 1
return i
def __str__(self): return str( type( self ) )
# end: class IDotDataRoot
class TableReaderBase(collections.Iterator):
"""Abstract base class for reading tables, whether in .tsv or .data/ format."""
def __init__(self, headings, fname = None, allFiles = (),
valueFixer = None):
"""Create a TableReaderBase"""
self.headings = tuple( headings )
if not len( set( headings ) ) == len( headings ):
print 'headings=', headings
seen = set()
for h in headings:
if h in seen:
print 'duplicate heading: ', h
seen.add( h )
assert len( set( headings ) ) == len( headings )
self.fname = fname
self.allFiles = allFiles
self.colName2num = dict( ( colName, colNum ) for colNum, colName in enumerate( self.headings ) )
self.valueFixer = valueFixer
self.lineNum = 0
def next(self):
try:
lineVals = self._nextLine()
self.lineNum += 1
if self.lineNum % 200000 == 0:
logging.info( joinstr(' ', self.lineNum, ' of ', \
self.fname, ': ', zip( self.headings, lineVals ) ) )
if len( lineVals ) != len( self.headings ):
logging.error( 'invalid table format: header has %d columns, line %d has %d items'
% ( len( self.headings ), self.lineNum, len( lineVals ) ) )
print 'lineVals=', lineVals, ' self.headings=', self.headings, ' \nzip=', zip( self.headings, lineVals )
assert len( lineVals ) == len( self.headings )
if self.valueFixer: lineVals = map( self.valueFixer, lineVals )
return IDotDataRecord( lineVals = lineVals,
colName2num = self.colName2num,
headings = self.headings, fname = self.fname )
except StopIteration:
self.close()
raise
class IDotDataGetColumns(IDotDataRoot):
"""Given an IDotData, constructs a new IDotdata consisting of some subset of its columns, possibly reordered.
"""
def __init__(self, parent, item):
item = MakeSeq( item )
badColumns = [ h for h in item if h not in parent.headings ]
if badColumns: raise AttributeError( ','.join( badColumns ) )
super(type(self), self).__init__( headings = item, parents = (parent,) )
self.parent = parent
itemCols = [ parent.colName2num[ c ] for c in item ]
igetter = operator.itemgetter( itemCols )
def itemGetter(rec):
return IDotDataRecord( lineVals = igetter(rec),
colName2num = self.colName2num,
headings = self.headings,
fname = None )
self.getter = itemGetter
self.item = item
def _iter(self):
return itertools.imap( self.getter, self.parent.recordsIter() )
def __str__(self):
return 'IDotDataGetColumns(' + ','.join( self.item ) + ')'
class IDotDataFilterBool(IDotDataRoot):
"""An IDotData that consists of selected rows of a parent IDotData. The rows are specified by an index
IDotData which is a one-column stream of Booleans. IDotDataFilterBool represents an IDotData
consisting of those rows of the parent IDotData for which the corresponding item in filter IDotData is True."""
def __init__(self, parent, filter):
assert len( filter.headings ) == 1
super(type(self), self).__init__( headings = parent.headings, parents = (parent,) )
self.parent = parent
self.filter = filter
def _iter(self):
for p, f in itertools.izip( self.parent, self.filter.flatIter() ):
assert isinstance( f, bool )
if f: yield p
class IDotDataRemoveDups(IDotDataRoot):
"""Return an IDotData consisting of non-duplicate rows of this IDotData. Rows are considered
duplicate if they're adjacent and equal at column(s) keyCols. Of each group of duplicate rows, the first
is selected to be part of the new IDotData."""
def __init__(self, parent, keyCols = None):
super(type(self), self).__init__( headings = parent.headings, parents = (parent,) )
self.parent = parent
self.keyCols = keyCols if keyCols is not None else parent.headings
def _iter(self):
lastRec = None
for r in self.parent.recordsIter():
if lastRec is None or r[ self.keyCols ] != lastRec[ self.keyCols ]:
yield r
lastRec = r
class IDotDataTakeWhile(IDotDataRoot):
def __init__(self, parent, pred):
super(type(self), self).__init__( headings = parent.headings, parents = (parent,) )
self.parent = parent
self.pred = pred
def _iter(self):
return itertools.takewhile( self.pred, iter( self.parent ) )
def __str__(self):
try: s = inspect.getsource( self.pred )
except IOError:
s = joinstr( ',', inspect.getsourcemodule( self.pred ), inspect.getsourcefile( self.pred ), inspect.getsourcelines( self.pred ) )
s = s.replace( '\n', '\\n' )
return 'IDotDataTakeWhile(%s)' % s
class IDotDataRenameCols(IDotDataRoot):
def __init__(self, parent, renamings):
renamings = dict( renamings )
assert set( renamings.keys() ) <= set( parent.headings ), \
'keys are %s headings are %s' % ( renamings.keys(), parent.headings )
super(type(self), self).__init__( headings =
[ DictGet( renamings, h, h) for h in parent.headings ],
parents = (parent,) )
self.parent = parent
def _iter(self): return self.parent.recordsIter()
class IDotDataOneSlice(IDotDataRoot):
"""One slice of an IDotData split by specified column(s)"""
def __init__(self, parent, colNames, partNum, totalParts, numChunks = None ):
super(type(self),self).__init__( headings = parent.headings, parents = ( parent, ) )
if not colNames: colNames = ()
self.parent = parent
self.colNames = colNames
if numChunks is None:
numChunks = 0
lastId = None
for i, r in enumerate( parent ):
thisId = r[ colNames ]
if thisId != lastId:
numChunks += 1
lastId = thisId
self.fromChunk = int( ( partNum / totalParts ) * numChunks )
self.toChunk = int( ( ( partNum+1 ) / totalParts ) * numChunks )
dbg( 'numChunks self.fromChunk self.toChunk' )
def _iter(self):
lastId = None
chunkNum = 0
for r in self.parent:
thisId = r[ self.colNames ]
if lastId is not None and thisId != lastId: chunkNum += 1
lastId = thisId
if chunkNum >= self.toChunk: break
if chunkNum >= self.fromChunk: yield r
class IDotDataOneRegion(IDotDataRoot):
"""One region of an IDotData split by specified column(s)"""
def __init__(self, parent, keyCols, beg, end ):
super(type(self),self).__init__( headings = parent.headings, parents = ( parent, ) )
self.parent = parent
self.keyCols = MakeSeq( keyCols or () )
self.beg = None if beg is None else tuple( map( coerceVal, MakeSeq( beg ) ) )
self.end = None if end is None else tuple( map( coerceVal, MakeSeq( end ) ) )
dbg( 'self.beg self.end' )
def _iter(self):
it = iter( self.parent )
if self.beg is not None: it = itertools.dropwhile( lambda r: tuple( r[ self.keyCols ] ) < self.beg, it )
if self.end is not None: it = itertools.takewhile( lambda r: tuple( r[ self.keyCols ] ) < self.end, it )
return it
class IDotData_tsv(IDotDataRoot):
"""An IDotData that reads a .tsv file"""
def __init__(self, fname, **tableReadOpts ):
self.fname = fname
self.tableReadOpts = tableReadOpts
exec ExtractOpts( tableReadOpts,
'headings', None,
'headingSep', None,
'commentPrefix', '##' )
self.comments = []
if headings is None:
# read headings from file.
with OpenForRead( fname ) as f:
headings = chomp( f.readline() )
while headings.startswith( commentPrefix ):
self.comments.append( headings )
headings = chomp( f.readline() )
headings = SplitStr( headings, FirstVal( headingSep, sep ) )
dbg( '"IDotData_tsv" headings' )
super(type(self),self).__init__( headings = headings, parents = () )
def _iter(self):
return TSVReader( fname = self.fname, **self.tableReadOpts )
def __str__(self):
return "IDotData_tsv('%s')" % self.fname
class IDotData_dotData(IDotDataRoot):
"""An IDotData that reads a .data/ directory"""
def __init__(self, fname, valueFixer = None):
if not fname.endswith('/'): fname += '/'
assert fname.endswith('.data/')
if not os.path.exists( fname ): raise IOError( 'Directory not found: ' + fname )
self.fname = fname
self.valueFixer = valueFixer
headerFN = fname + os.path.basename(fname[:-len('.data/')]) + '.header.txt'
if not os.path.exists( headerFN ):
headerFNs = glob.glob( fname + '*.header.txt' )
assert len( headerFNs ) == 1
headerFN = headerFNs[0]
super(type(self),self).__init__( headings = tuple( SlurpFileLines( headerFN ) ) )
tsvFiles = [ f for f in reduce( operator.concat,
map( operator.itemgetter(2), os.walk( fname ) ) ) if f.endswith('.csv') ]
colNames = [ os.path.splitext( os.path.splitext( tsvF )[0] )[0] for tsvF in tsvFiles ]
assert len( set( colNames ) ) == len( colNames )
assert sorted( colNames ) == sorted( self.headings )
col2file = dict( zip( colNames, tsvFiles ) )
self.allFiles = [ fname + col2file[ col ] for col in self.headings ]
def _iter(self):
return DotDataReader( fname = self.fname, headings = self.headings, valueFixer = self.valueFixer,
allFiles = self.allFiles )
def __str__(self):
return "IDotData_dotData('%s')" % self.fname
class TSVReader(TableReaderBase):
"""An iterator that yields records from a TSV file as named tuples."""
def __init__(self, fname, sep = '\t', headingSep = None, headings = None,
valueFixer = None, skipFirstLines = 0, lineFixer = None,
commentPrefix = '#' ):
f = OpenForRead( fname )
if headings == None:
headings = f.readline()
while headings.startswith( commentPrefix ): headings = f.readline()
headings = chomp( headings )
headings = SplitStr( headings, sep if headingSep is None else headingSep )
super(type(self),self).__init__(headings = headings, fname=fname, allFiles=(fname,), valueFixer = valueFixer )
self.sep = sep if sep != 'whitespace' else None
self.freader = iter( f )
self.f = f
self.skipFirstLines = skipFirstLines
self.lineFixer = lineFixer
def _nextLine(self):
"""Get next line"""
rawLine = chomp( self.freader.next() )
if not rawLine:
# possible bug: if there is only one heading and the line is
# a blank string, we'll stop here.
# we could have a "blank value allowed, and here is what it should be"
# option for each column.
raise StopIteration
# skip any comment lines. normally these only appear at the beginning.
while rawLine.startswith( commentPrefix ):
rawLine = chomp( self.freader.next() )
if not rawLine: raise StopIteration
while self.skipFirstLines > 0:
dbg( '"skipping" rawLine' )
rawLine = chomp( self.freader.next() )
if not rawLine: raise StopIteration
self.skipFirstLines -= 1
line = rawLine.split( self.sep )
if not line: raise StopIteration
if self.lineFixer: line = self.lineFixer( line )
return line
def close(self):
"""Close the file"""
if self.f is not None:
self.f.close()
self.f = None
self.freader = None
class DotDataReader(TableReaderBase):
"""Class for reading .tsv files"""
def __init__(self, fname, headings, allFiles, valueFixer = None):
super(type(self),self).__init__( headings = headings, fname=fname, allFiles=allFiles,
valueFixer = valueFixer )
self.fs = tuple( map( open, allFiles ) )
self.freaders = map( iter, self.fs )
def _nextLine(self):
"""Get next line"""
result = tuple( chomp( freader.next() ) for freader in self.freaders )
if not result: raise StopIteration
if len(result) != len( self.fs ):
print 'result=', result
assert len(result) == len( self.fs )
return result
def close(self):
"""Close all open files we had"""
if self.fs is not None:
for f in self.fs: f.close()
# var: iddSlice - map from IDotData file name to slicing params
iddSliceInfo = {}
iddRegionInfo = {}
def CanonFN( fname ): return fname[:-1] if fname.endswith( '.data/' ) else fname
def SetSliceInfo( fname, *args ):
"""Specify slice info for the given file"""
iddSliceInfo[ CanonFN( fname ) ] = args
def SetRegionInfo( fname, keyCols, beg, end ):
"""Specify slice info for the given file"""
iddRegionInfo[ CanonFN( fname ) ] = Dict( 'keyCols beg end' )
def IDotData( fname = None, sep = '\t', headingSep = None, fileType = None, headings = None, valueFixer = None, lineFixer = None,
skipFirstLines = 0, names = None, commentPrefix = '#',
Path = None, SVPath = None, SVValueFixer = None, SVLineFixer = None, SVDelimiter = None,
SVSkipFirstLines = None, Header = True, Records = None, Columns = None, multiPass = True,
ToLoad = None, lookupSlice = True, lookupRegion = True, useHeadings = False ):
"""Creates an IDotData from one of several possible sources: a .tsv file, a .data/ directory, a list of
records, or a list of columns.
"""
if IDotData.isA( fname ): return fname
if hasattr( type( fname ), 'isDotData' ): return IDotData.fromDotData( fname )
assert sum( map( bool, ( fname, Path, SVPath, Records is not None, Columns is not None ) ) ) == 1
# handle legacy arguments for backwards compatibility with DotData
if not fname and Path: fname = Path
if not fname and SVPath: fname = SVPath
if fname and lookupSlice and CanonFN( fname ) in iddSliceInfo:
return IDotData( lookupSlice = False,
**Dict( 'fname sep fileType headings valueFixer lineFixer skipFirstLines names commentPrefix '
'Path SVPath SVValueFixer SVLineFixer SVDelimiter SVSkipFirstLines Header Records '
'Columns multiPass ToLoad' ) ).oneSlice( *( iddSliceInfo[ CanonFN( fname ) ] ) )
if fname and lookupRegion and CanonFN( fname ) in iddRegionInfo:
return IDotData( lookupRegion = False,
**Dict( 'fname sep fileType headings valueFixer lineFixer skipFirstLines names commentPrefix '
'Path SVPath SVValueFixer SVLineFixer SVDelimiter SVSkipFirstLines Header Records '
'Columns multiPass ToLoad' ) ).oneRegion( **( iddRegionInfo[ CanonFN( fname ) ] ) )
if not headings and names: headings = names
if isinstance( headings, types.StringTypes ) and ( ' ' in headings or '\t' in headings ):
headings = tuple( headings.split( '\t' if '\t' in headings else None ) )
if not skipFirstLines and SVSkipFirstLines: skipFirstLines = SVSkipFirstLines
if SVDelimiter: sep = SVDelimiter
if SVValueFixer: valueFixer = SVValueFixer
if SVLineFixer: lineFixer = SVLineFixer
assert ( fname and ( Header or headings ) ) or ( ( Records is not None or Columns ) and headings )
assert not Columns or len( Columns ) == len( headings )
if Records is not None: return IDotData.fromIterable( headings = headings, iterable = Records )
if Columns:
# make a one-column IDotData from each column, then hstack them.
return IDotDataHStack( *[ IDotData.fromIterable( headings = (h,), iterable = c, multiPass = multiPass )
for h, c in zip( headings, Columns ) ])
logging.info( 'IDotData: creating iter over ' + fname )
if fileType is None and IsFileType( fname, '.data', '.data/' ): fileType = '.tsv'
if sep == ' ': fileType = '.tsv'
if IsFileType( fname, '.vcf' ): commentPrefix = '##'
if fname.endswith( '.data' ) or fname.endswith( '.data/' ):
result = IDotData_dotData( fname = fname, valueFixer = valueFixer )
else:
result = IDotData_tsv( **Dict( 'fname sep headingSep headings valueFixer skipFirstLines lineFixer commentPrefix' ) )
if ToLoad:
dbg( '"BBBBEF" result.headings ToLoad' )
result = result[ ToLoad ]
dbg( '"AAAAFT" result.headings' )
return result
def makeIdxPrepender( idx ):
"""Creates a function that prepends a given index to its argument, returning a tuple."""
return lambda( v ): ( idx, v )
class attrDbg(object):
def __getattribute__(self, attr):
print 'getting attr ', attr
return attr
def makeKeyGetter( k ):
"""Creates a function that gets a key"""
def myFunc( v ):
return k( v[1] )
return myFunc
def makeKeyApplier( k ):
"""Make a function that applies a key"""
def myApplier( v ):
r = ( k(v), v )
#print 'applying ', k, ' to ', v, ' got ', r
return r
return myApplier
def itermerge( iters, keys = None, ids = None, includeKeys = False ):
'''Merge multiple sorted inputs into a single sorted output.
Equivalent to: sorted(itertools.chain(*iters))
except that iterables need not be finite.
Params:
keys - if given, then for each iter gives a function to extract the ordering key
from elements yielded by that iter
ids - if True, the yielded sequence is a sequence of pairs with the first element of the tuple being
the index of the iterator from which the original element comes, and the second being the original element.
if a sequence, instead of the numeric index of the iterator, a corresponding value from this sequence is used.
>>> list(itermerge(iters=([1,3,5,7], [0,2,4,8], [5,10,15,20], [], [25])))
[0, 1, 2, 3, 4, 5, 5, 7, 8, 10, 15, 20, 25]
Adapted from http://code.activestate.com/recipes/535160/
'''
iters = tuple( iters )
if keys is not None:
keys = [ ( operator.itemgetter( k ) if isinstance( k, (int,tuple,list) ) else
( operator.attrgetter( k ) if isinstance( k, types.StringTypes ) else k ) ) for k in keys ]
if ids:
return itermerge( iters = [ itertools.imap( makeIdxPrepender( idx ), i ) for idx, i in
( enumerate( iters ) if ids == True else zip( ids, iters ) ) ],
keys = ( operator.itemgetter( 1 ), ) * len( iters ) if keys is None else
map( makeKeyGetter, keys ),
includeKeys = includeKeys )
if keys is not None:
result = itermerge( iters = [ itertools.imap( makeKeyApplier( k ), i )
for k, i in zip( keys, iters ) ] )
if not includeKeys: result = itertools.imap( operator.itemgetter( 1 ), result )
return result
def merge( i1, i2 ):
next1 = iter( i1 ).next
next2 = iter( i2 ).next
try:
v1 = next1()
except StopIteration:
while True:
yield next2()
try:
v2 = next2()
except StopIteration:
while True:
yield next1()
while True:
if v1 < v2:
yield v1
try:
v1 = next1()
except StopIteration:
yield v2
while True:
yield next2()
else:
yield v2
try:
v2 = next2()
except StopIteration:
yield v1
while True:
yield next1()
iters_cnt = len( iters )
if iters_cnt == 0:
return iter( () )
if iters_cnt == 1:
return iter( iters[0] )
if iters_cnt == 2:
return merge( iters[0], iters[1] )
bisect = int( iters_cnt / 2 )
return merge( itermerge( iters = iters[:bisect] ), itermerge( iters = iters[bisect:] ) )
def TableIterInnerJoinAux( tableIters, cols, headings, blanks, headingLens, colName2num, keyHeadings = () ):
cols = MakeSeq( cols )
assert sum( headingLens ) == len( headings )
blanks = [ blank if blank is None or IsSeq( blank ) else (blank,)*headingLen
for blank, headingLen in zip(blanks, headingLens) ]
prevKey = None
for k, g in itertools.groupby( itermerge( iters = tableIters, keys = cols, ids = True,
includeKeys = True ),
key = operator.itemgetter( 0 ) ):
# check that the keys are sorted in strictly increasing order -- important for correct operation of join
if not( prevKey==None or k > prevKey ):
dbg( 'prevKey k tuple(g)' )
assert prevKey==None or k > prevKey
prevKey = k
records = tuple( g )
origins = [ r[1][0] for r in records ]
if not is_sorted( origins, strict = True ):
dbg( 'records origins' )
assert is_sorted( origins, strict = True )
recordsList = [ None ] * len( tableIters )
positionFilled = [ False ] * len( tableIters )
for r in records:
recordsList[ r[1][0] ] = r[1][1]
positionFilled[ r[1][0] ] = True
for i in range( len( tableIters ) ):
if not positionFilled[ i ] and blanks[ i ] != None:
recordsList[ i ] = blanks[ i ]
positionFilled[ i ] = True
if all( positionFilled ):
rec = map( tuple, recordsList )
assert map( len, rec ) == headingLens
rec = reduce( operator.concat, rec )
yield IDotDataRecord( lineVals = rec + ( k if keyHeadings else () ), colName2num = colName2num,
fname = None, headings = headings + keyHeadings )
def mergeHeadings( iDotDatas, suffixes = None ):
if suffixes == None: suffixes = [ '_%d' % i for i in range( len( iDotDatas ) ) ]
assert len( suffixes ) == len( iDotDatas )
sharedColNames = set( [] )
allColNames = set( [] )
for iDotData in iDotDatas:
for heading in iDotData.headings:
( sharedColNames if heading in allColNames else allColNames ).add( heading )
return tuple([ n if n not in sharedColNames else n + sfx for iDotData, sfx in zip( iDotDatas, suffixes )
for n in iDotData.headings ])
def GetIDotDatas(iDotDatas):
return tuple( IDotData(iDotData) if isinstance(iDotData, types.StringTypes) else
( IDotData.fromDotData( iDotData ) if hasattr( type( iDotData ), 'isDotData' ) else iDotData )
for iDotData in iDotDatas )
class IDotDataJoin(IDotDataRoot):
def __init__(self, iDotDatas, cols, suffixes = None, blanks = None, keyHeadings = () ):
iDotDatas = GetIDotDatas( iDotDatas )
super(type(self),self).__init__( headings = mergeHeadings( iDotDatas, suffixes ) + keyHeadings, parents = iDotDatas )
if blanks == None: blanks = (None,) * len( iDotDatas )
assert all([ not hasattr(blank,'__len__') or len( blank ) == len( iDotData.headings )
for iDotData, blank in zip( iDotDatas, blanks ) ])
self.iDotDatas = iDotDatas
self.cols = cols
self.blanks = blanks
self.headingLens = [ len( iDotData.headings ) for iDotData in iDotDatas ]
self.keyHeadings = keyHeadings
def _iter(self):
return TableIterInnerJoinAux( tableIters = [ idd.recordsIter() for idd in self.iDotDatas ],
cols = self.cols, headings = self.headings if not self.keyHeadings else self.headings[ :-len( self.keyHeadings ) ], blanks = self.blanks,
headingLens = self.headingLens,
colName2num = self.colName2num,
keyHeadings = self.keyHeadings
)
def IDotDataAsTuples( iDotDatas, cols, blanks = None ):
return TableIterInnerJoinAuxAsTuples( tableIters = map( iter, iDotDatas ), cols = cols,
blanks = blanks if blanks is not None else [ (None,)*len(idd.headings)
for idd in iDotDatas ],
headingLens = [ len( idd.headings ) for idd in iDotDatas ] )
def TableIterInnerJoinAuxAsTuples( tableIters, cols, blanks, headingLens ):
"""Merge the outputs of several sorted iterators into one unified sorted iterator.
The unified iterator yields _tuples_ with each tuple position corresponding to one
input iterator. Where some input iterator does not have a value for some key,
that position is set to None.
"""
blanks = [ blank if blank is None or IsSeq( blank ) else (blank,)*headingLen
for blank, headingLen in zip(blanks, headingLens) ]
numJoinsSkipped, numJoinsAllowed = 0, 0
prevKey = None
for k, g in itertools.groupby( itermerge( iters = tableIters, keys = cols, ids = True,
includeKeys = True ),
key = operator.itemgetter( 0 ) ):
# check that the keys are sorted in strictly increasing order -- important for correct operation of join
if not( prevKey==None or k > prevKey ):
print 'prevKey=', prevKey, ' key=', k, ' g is ', tuple( g )
assert prevKey==None or k > prevKey
prevKey = k
records = tuple( g )
origins = [ r[1][0] for r in records ]
if not is_sorted( origins, strict = True ):
print 'records are ', records
print 'origins are ', origins
assert is_sorted( origins, strict = True )
recordsList = [ None ] * len( tableIters )
positionFilled = [ False ] * len( tableIters )
for r in records:
recordsList[ r[1][0] ] = r[1][1]
positionFilled[ r[1][0] ] = True
for i in range( len( tableIters ) ):
if not positionFilled[ i ] and blanks[ i ] != None:
recordsList[ i ] = blanks[ i ]
positionFilled[ i ] = True
if all( positionFilled ):
yield recordsList
numJoinsAllowed += 1
else: numJoinsSkipped += 1
dbg( 'cols numJoinsAllowed numJoinsSkipped' )
class IDotDataVStack(IDotDataRoot):
def __init__(self, *iDotDatas, **kwargs):
print 'befFlatten: ', iDotDatas
iDotDatas = flatten( iDotDatas )
print 'aftFlatten: ', iDotDatas
assert len( iDotDatas ) > 0
iDotDatas = map( IDotData, iDotDatas )
if 'sourceCol' in kwargs:
ids = DictGetNotNone( kwargs, 'sourceIds', map( operator.attrgetter( 'fname' ), iDotDatas ) )
dbg( 'kwargs["sourceCol"] ids' )
iDotDatas = [ iDotData.hstack( IDotData.repeat( kwargs[ 'sourceCol' ], idd_id ) )
for idd_id, iDotData in zip( ids, iDotDatas ) ]
if 'sourceLabels' in kwargs:
# hstack to each IDotData some identifying columns
sourceLabels = IDotData( kwargs[ 'sourceLabels' ] )
iDotDatas = [ iDotData.hstack( IDotData.repeat( headings = sourceLabels.headings,
values = sourceLabel ) )
for sourceLabel, iDotData in zip( sourceLabels, iDotDatas ) ]
headings = DictGet( kwargs, 'headings', iDotDatas[ 0 ].headings )
super(type(self), self).__init__( headings = headings )
self.iDotDatas = iDotDatas
self.comments = reduce( operator.concat, [ idd.comments if hasattr( idd, 'comments' ) else []
for idd in iDotDatas ] )
def _iter(self):
return itertools.chain( *self.iDotDatas )
class IDotDataVStackFromIterable(IDotDataRoot):
def __init__(self, iDotDatas, headings = None):
isInfinite = False
if not headings:
iDotDatas = iter( iDotDatas )
firstIDotData = next( iDotDatas )
headings = firstIDotData.headings
isInfinite = firstIDotData.isInfinite
iDotDatas = itertools.chain.from_iterable( ( (firstIDotData,), iDotDatas ) )
super(type(self), self).__init__( headings = headings )
self.iDotDatas = iDotDatas
self.iDotDatasIterUsedUp = False
self.isInfinite = isInfinite
def _iter(self):
assert not self.iDotDatasIterUsedUp
if isinstance( self.iDotDatas, collections.Iterator ): self.iDotDatasIterUsedUp = True
return itertools.chain.from_iterable( self.iDotDatas )
class IDotDataHStack(IDotDataRoot):
def __init__(self, *iDotDatas ):
iDotDatas = GetIDotDatas( iDotDatas )
self.iDotDatas = tuple( iDotDatas )
super(type(self), self).__init__( headings = mergeHeadings( iDotDatas ), parents = iDotDatas )
def _iter(self):
lineValsGetter = operator.attrgetter( 'lineVals' )
def mergeRecs( *vals ):
return IDotDataRecord( lineVals = reduce( operator.concat, map( lineValsGetter, vals ) ),
colName2num = self.colName2num,
headings = self.headings )
return itertools.imap( mergeRecs, *[ idd.recordsIter() for idd in self.iDotDatas ] )
class IDotDataFilter(IDotDataRoot):
def __init__(self, parent, pred):
super(type(self),self).__init__( headings = parent.headings, parents = (parent,) )
self.parent = parent
self.pred = pred
def _iter(self):
for r in self.parent:
if self.pred( r ):
yield r
#return itertools.ifilter( self.pred, iter( self.parent ) )
class IDotDataMapRecords(IDotDataRoot):
"""Create an IDotData whose rows (records) are obtained by applying a specified function to values from
specified IDotDatas."""
def __init__(self, func, iDotDatas, headings):
super(type(self),self).__init__( headings = headings, parents = iDotDatas )
self.iDotDatas = iDotDatas
self.func = func
def _iter(self): return itertools.imap( self.func, *self.iDotDatas )
class IDotDataStarMap(IDotDataRoot):
def __init__(self, func, iDotData, headings):
super(type(self),self).__init__( headings = headings, parents = (iDotData,) )
self.iDotData = iDotData
self.func = func
def _iter(self):
return itertools.starmap( self.func, self.iDotData )
IDotData.mergeOnKeyCols = IDotDataJoin
IDotData.merge = IDotDataJoin
IDotData.mergeAsTuples = IDotDataAsTuples
IDotData.mapRecords = IDotDataMapRecords
IDotData.starmap = IDotDataStarMap
IDotData.mergeColumnSummaries = IDotDataRoot.mergeColumnSummaries
IDotData.TableIterInnerJoinAuxAsTuples = TableIterInnerJoinAuxAsTuples
IDotData.setSliceInfo = SetSliceInfo
IDotData.setRegionInfo = SetRegionInfo
class IDotDataRepeat(IDotDataRoot):
"""A one-column IDotData consisting of a specified number of repeats
of a given constant value"""
def __init__(self, heading = None, value = None, headings = None, values = None, times = None):
assert heading is not None and value is not None and headings is None and values is None \
or heading is None and value is None and headings is not None and values is not None
super(type(self),self).__init__( headings = ( heading, ) if headings is None else headings )
self.values = value if values is None else values
self.times = times
if times is None:
self.repeater = itertools.repeat( self.values )
self.isInfinite = True
def _iter(self):
return self.repeater if self.times is None else itertools.repeat( self.values, self.times )
IDotData.repeat = IDotDataRepeat
IDotData.ones = lambda n = None: IDotData.repeat( heading = 'v', value = 1, times = n )
IDotData.zeros = lambda n = None: IDotData.repeat( heading = 'v', value = 0, times = n )
IDotData.vstack = IDotDataVStack
IDotData.vstackFromIterable = IDotDataVStackFromIterable
IDotData.hstack = IDotDataHStack
class IDotDataWhere(IDotDataRoot):
"""Creates an IDotData which takes each row from one of two given IDotDatas, depending on the value of a dispatcher
IDotData.
Fields:
which - a one-column IDotData whose values specify which values to take.
"""
def __init__(self, which, ifTrue, ifFalse):
assert len( which.headings ) == 1
assert ifTrue.headings == ifFalse.headings
super(type(self),self).__init__( headings = ifTrue.headings, parents = ( which, ifTrue, ifFalse ) )
self.which = which
self.ifTrue = ifTrue
self.ifFalse = ifFalse
def _iter(self):
for whichVal, ifTrueVal, ifFalseVal in itertools.izip( self.which, self.ifTrue, self.ifFalse ):
yield ifTrueVal if whichVal else ifFalseVal
IDotData.where = IDotDataWhere
class IDotDataChoose(IDotDataRoot):
"""Creates an IDotData which takes each row from one of the given IDotDatas, depending on the value of a dispatcher
IDotData.
Fields:
which - a one-column IDotData whose values specify which values to take.
"""
def __init__(self, which, choices):
assert len( which.headings ) == 1
assert all([ c.headings == choices[0].headings for c in choices ])
super(type(self),self).__init__( headings = choices[0].headings, parents = ( which, ) + tuple( choices ) )
self.which = which
self.choices = choices
def _iter(self):
for r in itertools.izip( self.which, *self.choices ):
yield r[ r[0] + 1 ]
IDotData.choose = IDotDataChoose
class IDotDataFromFn(IDotDataRoot):
def __init__(self, fn, *args, **kwargs):
super(type(self),self).__init__( headings = next( fn(*args, **kwargs) ) )
self.fn = fn
self.args = args
self.kwargs = kwargs
def _iter(self):
it = self.fn( *self.args, **self.kwargs)
next(it) # skip the headings
return it
IDotData.fromFn = IDotDataFromFn
class IDotDataFromIterable(IDotDataRoot):
"""An IDotData that takes its records from a specified iterable. The iterable may be a one-pass iterator,
in which case this IDotData may be iterated over at most once.
"""
def __init__(self, headings, iterable, multiPass = False):
if isinstance( headings, types.StringTypes ) and ( ' ' in headings or '\t' in headings ):
headings = tuple( headings.split( '\t' if '\t' in headings else None ) )
super(type(self),self).__init__( headings = headings )
if multiPass and isinstance( iterable, collections.Iterator ): iterable = tuple( iterable )
self.iterable = iterable
self.iteratorUsedUp = False
def _iter(self):
assert not self.iteratorUsedUp
iterHere = iter(self.iterable)
if iterHere is self.iterable or isinstance( self.iterable, collections.Iterator ): self.iteratorUsedUp = True
return iterHere
IDotData.fromIterable = IDotDataFromIterable
def IDotDataFromDotData( d ):
"""Create an IDotData from a DotData"""
if not hasattr( type( d ), 'isDotData' ):
if hasattr( d, 'shape' ) and len( d.shape ) == 1:
return IDotData( names = ( 'val', ), Columns = ( tuple( d ), ) )
assert False
result = IDotData.fromIterable( headings = d.dtype.names,
iterable = d )
result.len = len( d )
return result
IDotData.fromDotData = IDotDataFromDotData
class IDotDataWriterRoot(object):
__metaclass__ = ABCMeta
"""Abstract base class for classes that help you incrementally write out an IDotData file record-by-record."""
def __init__(self, headings):
#assert headings
if isinstance( headings, types.StringTypes ) and ( ' ' in headings or '\t' in headings ):
headings = tuple( headings.split( '\t' if '\t' in headings else None ) )
self.headings = headings
@abstractmethod
def writeRecord(self, *line): pass
def writeRecords(self, lines):
"""Write all lines from an iterable"""
for line in lines: self.writeRecord( line )
@abstractmethod
def close(self): pass
def __enter__(self): return self
def __exit__(self, exc_type, exc_value, traceback):
if exc_type is not None:
dbg( '"Error_while_writing_IDotData:" exc_type exc_value traceback' )
tb.print_exception( exc_type, exc_value, traceback )
self.close()
return False # don't suppress any exceptions
class IDotDataSVWriter(IDotDataWriterRoot):
"""Writes records to SV file"""
def __init__(self, fname, headings, sep = '\t', comments = () ):
logging.info( 'opening tsv for write: ' + str( fname ) )
self.f = OpenForWrite( fname )
super(type(self), self).__init__( headings = headings )
for c in comments: self.f.write( c + '\n' )
if self.headings:
tabwrite( self.f, *self.headings, sep = sep )
logging.info( 'headings are: ' + str( self.headings ) )
self.sep = sep
def writeRecord(self, *line):
"""Write one record to the file"""
if isinstance( line, collections.Sized ) and len( line ) == 1 and IsSeq( line[0] ): line = line[0]
if isinstance( line, types.GeneratorType ): line = tuple( line )
line = MakeSeq( line )
assert not self.headings or len( line ) == len( self.headings )
tabwrite( self.f, *MakeSeq( line ), sep = self.sep )
self.isFirstLine = False
def close(self): self.f.close()
class IDotDataDotDataWriter(IDotDataWriterRoot):
"""Class for incrementally writing to a .data/ directory."""
def __init__(self, fname, headings, comments = ()):
if not fname.endswith('/'): fname += '/'
assert fname.endswith('.data/')
self.fname = fname
super(type(self), self).__init__( headings = headings )
MakeDir( fname )
DumpFile( fname + os.path.basename( MapPath( fname )[:-len('.data/')] )
+ '.header.txt', '\n'.join( self.headings ) )
self.isFirstRecord = True
type2name = { float: 'float', int: 'int', str: 'str', bool: 'bool', long: 'long' }
if haveNumpy: type2name.update( ( ( np.int64, 'long' ), (np.bool_, 'bool' ), ( np.float64, 'float' ) ) )
typeOrder = ( bool, ) + ( ( np.bool_, ) if haveNumpy else () ) + \
( int, long ) + ( ( np.int64, np.float64 ) if haveNumpy else () ) + ( float, str )
def __initColTypes(self, colTypes):
self.colTypes = tuple( colTypes )
self.newColTypes = list( self.colTypes )
self.fs = [ open(self.fname + heading + '.' + type(self).type2name[colType] + '.csv', 'w')
for heading, colType in zip( self.headings, self.colTypes ) ]
def writeRecord( self, *line ):
"""Append a record to a .data/"""
if isinstance( line, collections.Sized ) and len( line ) == 1 and IsSeq( line[0] ): line = line[0]
if isinstance( line, types.GeneratorType ): line = tuple( line )
line = MakeSeq( line )
assert len( line ) == len( self.headings )
if self.isFirstRecord: self.__initColTypes( type(coerceVal(val)) for val in line )
typeOrder = type(self).typeOrder
for colNum, ( v, f ) in enumerate( zip( line, self.fs) ):
if not self.isFirstRecord: f.write( '\n' )
f.write( str(v) )
newType = type( coerceVal( v ) )
oldType = self.newColTypes[ colNum ]
if newType not in typeOrder: dbg( 'newType' )
if typeOrder.index( newType ) > typeOrder.index( oldType ): self.newColTypes[ colNum ] = newType
self.isFirstRecord = False
def close(self):
if self.isFirstRecord: self.__initColTypes( ( str, ) * len( self.headings ) )
for f in self.fs: f.close()
type2name = type(self).type2name
for heading, colType, newColType in zip( self.headings, self.colTypes, self.newColTypes ):
if type2name[ newColType ] != type2name[ colType ]:
# we have to rename the file to a new correct name.
# make sure it has been fully written and closed.
time.sleep(20)
oldName = self.fname + heading + '.' + type2name[colType] + '.csv'
WaitForFileToAppear( oldName )
newName = self.fname + heading + '.' + type2name[newColType] + '.csv'
os.rename( oldName, newName )
time.sleep(10)
WaitForFileToAppear( newName )
logging.info( 'saved IDotData to ' + self.fname )
@contextlib.contextmanager
def IDotDataOpenForWrite( fname, headings, sep = '\t', fileType = None, comments = () ):
"""Context manager for writing .data/ directories"""
openFn = IDotDataDotDataWriter if IsFileType( fname, '.data', '.data/' ) else IDotDataSVWriter
with openFn( fname = fname, headings = headings, comments = comments ) as f: yield f
IDotData.openForWrite = IDotDataOpenForWrite
def isIDotData(x):
"""Test if the given object is an IDotData.
We could test if it inherits from IDotDataRoot, but we also want to
allow unrelated objects to implement the iDotData interface.
If their class has an isIDotData attribute, we assume the object
is an IDotData. If a class inherits from IDotDataRoot then it automatically
inherits this attribute."""
return hasattr( type( x ), 'isIDotData' )
IDotData.isA = isIDotData
IDotData.rootClass = IDotDataRoot
def imean(iterable):
"""Return the mean value of an iterable, or nan if
there are no values."""
sum = SumKeeper()
count = 0
for x in iterable:
sum += x
count += 1
return sum.getSum() / count if count > 0 else np.nan
def imeanstd_old( iterable ):
"""Return the mean and stddev of an iterable,
or (nan,nan) if there are no values. """
sum = SumKeeper()
sumSq = SumKeeper()
n = 0
for x in iterable:
if not ( math.isnan( x ) or math.isinf( x ) ):
sum += x
sumSq += x*x
n += 1
if n == 0: return np.nan, np.nan
seqMean = sum.getSum() / n
seqStd = math.sqrt( sumSq.getSum() / n - seqMean * seqMean )
return seqMean, seqStd
def imeanstd( iterable ):
"""Return the mean and stddev of an iterable,
or (nan,nan) if there are no values. If iterable yields
sequence values, then return (mean,std) for each column
of the sequence."""
sums = None
sumSqs = None
numCols = None
ns = None
for x in iterable:
xSeq = MakeSeq( x )
if numCols is None:
numCols = len( xSeq )
sums = [ SumKeeper() for i in range( numCols ) ]
sumSqs = [ SumKeeper() for i in range( numCols ) ]
ns = [ 0 ] * len( xSeq )
else: assert numCols == len( xSeq )
for dim in range( numCols ):
xVal = xSeq[ dim ]
if not ( math.isnan( xVal ) or math.isinf( xVal ) ):
sums[ dim ] += xVal
sumSqs[ dim ] += xVal*xVal
ns[ dim ] += 1
if numCols is None: return np.nan, np.nan
seqMeans = [ sum.getSum() / n for sum, n in zip( sums, ns ) ]
seqStds = [ math.sqrt( sumSq.getSum() / n -
seqMean * seqMean )
for sumSq, seqMean, n in zip( sumSqs, seqMeans, ns ) ]
seqMeansStds = zip( seqMeans, seqStds )
return seqMeansStds if numCols > 1 else seqMeansStds[ 0 ]
def imeanstd_plusStats( iterable ):
"""Return the mean and stddev of an iterable, as well as
counts of values (total and the non-nan values on which
this is based),
or (nan,nan) if there are no values. If iterable yields
sequence values, then return (mean,std) for each column
of the sequence."""
sums = None
sumSqs = None
numCols = None
ns = None
ntots = None
for x in iterable:
xSeq = MakeSeq( x )
if numCols is None:
numCols = len( xSeq )
sums = [ SumKeeper() for i in range( numCols ) ]
sumSqs = [ SumKeeper() for i in range( numCols ) ]
ns = [ 0 ] * numCols
ntots = [ 0 ] * numCols
else: assert len( xSeq ) == numCols
for dim in range( numCols ):
xVal = xSeq[ dim ]
ntots[ dim ] += 1
if not ( math.isnan( xVal ) or math.isinf( xVal ) ):
sums[ dim ] += xVal
sumSqs[ dim ] += xVal*xVal
ns[ dim ] += 1
if numCols is None: return np.nan, np.nan, 0, 0
seqMeans = [ sum.getSum() / n for sum, n in zip( sums, ns ) ]
seqStds = [ math.sqrt( sumSq.getSum() / n -
seqMean * seqMean )
for sumSq, seqMean, n in zip( sumSqs, seqMeans, ns ) ]
seqMeansStds = zip( seqMeans, seqStds, ns, ntots )
return seqMeansStds if numCols > 1 else seqMeansStds[ 0 ]
if haveNumpy:
#
# Extend numpy routines to handle IDotData arguments.
#
def extendNumpy(f):
"""A decorator that replaces a function f with a new function which calls f
if any arguments are IDotDatas, otherwise calls a numpy function of the same name.
"""
orig_fn = eval( 'np.' + f.func_name )
def new_f( *args, **kwargs ):
return f( *args, **kwargs ) if any( IDotData.isA( a ) for a in args ) \
else orig_fn( *args, **kwargs )
new_f.__doc__ = orig_fn.__doc__
setattr( np, f.func_name, new_f )
return f
numpy_isnan = np.isnan
@extendNumpy
def isnan(arg): return arg.mapVals( numpy_isnan )
numpy_isfinite = np.isfinite
@extendNumpy
def isfinite(arg): return arg.mapVals( numpy_isfinite )
numpy_isinf = np.isinf
@extendNumpy
def isinf(arg): return arg.mapVals( numpy_isinf )
numpy_isneginf = np.isneginf
@extendNumpy
def isneginf(arg): return arg.mapVals( numpy_isneginf )
numpy_isposinf = np.isposinf
@extendNumpy
def isposinf(arg): return arg.mapVals( numpy_isposinf )
numpy_isscalar = np.isscalar
@extendNumpy
def isscalar(arg): return arg.mapVals( numpy_isscalar )
numpy_invert = np.invert
@extendNumpy
def invert(arg): return arg.mapVals( numpy_invert )
numpy_exp = np.exp
@extendNumpy
def exp(arg): return arg.mapVals( numpy_exp )
numpy_log = np.log
@extendNumpy
def log(arg): return arg.mapVals( numpy_log )
@extendNumpy
def nanmax(a): return a.nanmax()
@extendNumpy
def mean(a): return imean( a )
@extendNumpy
def min(a): return __builtin__.min( a )
@extendNumpy
def max(a): return __builtin__.max( a )
numpy_abs = np.abs
@extendNumpy
def abs(arg): return arg.mapVals( numpy_abs )
# @extendNumpy
# def asarray( a, dtype = None):
# assert a.numCols() == 1
# return np.fromiter( a, dtype = float if dtype is None else dtype )
# @extendNumpy
# def asanyarray( a, dtype = None):
# assert a.numCols() == 1
# return np.fromiter( a, dtype = float if dtype is None else dtype )
numpy_atleast_1d = np.atleast_1d
def atleast_1d(*arys):
if len( arys ) == 1:
a = arys[0]
if IDotData.isA( a ):
return np.asanyarray( a )
else:
return numpy_atleast_1d( *arys )
else:
return map( atleast_1d, arys )
np.atleast_1d = atleast_1d
@extendNumpy
def where( cond, ifTrue, ifFalse ): return IDotData.where( cond, ifTrue, ifFalse )
@extendNumpy
def choose( a, choices ): return IDotData.choose( a, choices )
|
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from collections import Counter
from matplotlib import rc
import pickle, sys, pdb, gzip
if sys.version_info[0]<3:
import cPickle
import numpy as np
from sklearn.metrics import log_loss, confusion_matrix
from sklearn.manifold import TSNE as tsne
import tensorflow as tf
from data.SSL_DATA import SSL_DATA
from data.mnist import mnist
from data.data import create_semisupervised, encode_onehot, load_dataset, make_dataset, get_non_wear_indices, load_feat_csv_file
from models.m2 import m2
from models.m2_k import m2_k
from models.gm_dgm import gm_dgm
from models.gm_dgm_conv import gm_dgm_conv
from models.adgm_k import adgm_k
from models.agm_dgm import agm_dgm
from keras.datasets import cifar10, cifar100
from utils.checkmate import get_best_checkpoint
from sklearn.metrics import cohen_kappa_score
import argparse
import os
# from models.adgm import adgm
# from models.sdgm import sdgm
# from models.blendedv2 import blended
# from models.sblended import sblended
# from models.b_blended import b_blended
### Script to run an MNIST experiment with generative SSL models ###
## argv[1] - proportion of training data labeled (or for mnist, number of labels from each class)
## argv[2] - Dataset seed
## argv[3] - noise level in moons dataset / Threshold for reduction in mnist
## argv[4] - model to use (m2, adgm, sdgm, sslpe, b_sdgm, msdgm)
## argv[5] - number of runs per beta
## argv[6] - number of MC samples
# Experiment parameters
#num_labeled, threshold = int(sys.argv[1]), float(sys.argv[3])
def predict_new(x):
saver = tf.train.Saver()
with tf.Session() as session:
ckpt = get_best_checkpoint(model.ckpt_dir)
saver.restore(session, ckpt)
if model_name == 'm2':
pred = session.run([model.predictions], {model.x:x})
else:
y_ = model.q_y_x_model(model.x)
pred = session.run([y_], {model.x:x})
return pred
def get_args():
'''This function parses and return arguments passed in'''
# Assign description to the help doc
parser = argparse.ArgumentParser(
description='Run different models over different datasets with different props labelled')
# Add arguments
parser.add_argument(
'-m', '--model_name', choices=['m2', 'm2_k', 'gm_dgm','gmm_vae', 'adgm_k', 'agm_dgm', 'gm_dgm_conv'], required=True)
parser.add_argument(
'-d', '--dataset', choices=['mnist', 'svhn', 'cifar10', 'cifar100', 'activity', 'biobank', 'activity_basic'], required=True)
parser.add_argument(
'-p', '--prop_labelled', type=float, required=True)
parser.add_argument(
'-r', '--number_of_runs', type=int, required=True)
parser.add_argument(
'-e', '--number_of_epochs', type=int, required=True)
parser.add_argument(
'-c', '--classes_to_hide', nargs='*', type=int)
parser.add_argument(
'-a', '--number_of_classes_to_add', type=int, default=0)
parser.add_argument(
'-z', '--number_of_dims_z', type=int, default=100)
# Array for all arguments passed to script
args = parser.parse_args()
# Assign args to variables
return args
args=get_args()
print(args)
model_name = args.model_name
#model_name = 'm2'
dataset_name = args.dataset
#dataset_name = 'svhn'
prop_labelled = args.prop_labelled
#prop_labelled=0.1
num_runs = args.number_of_runs
#num_runs=10
n_epochs = args.number_of_epochs
#n_epochs=10
classes_to_hide = args.classes_to_hide
#classes_to_hide = [7,8,9]
number_of_classes_to_add = args.number_of_classes_to_add
n_z = args.number_of_dims_z
#number_of_classes_to_add = 3
if dataset_name == 'activity':
learning_paradigm = 'un-semisupervised'
elif prop_labelled == 0:
learning_paradigm = 'unsupervised'
elif prop_labelled < 0:
learning_paradigm = 'supervised'
elif prop_labelled > 0 and classes_to_hide is not None:
learning_paradigm = 'un-semisupervised'
elif prop_labelled > 0:
learning_paradigm = 'semisupervised'
# Load and conver data to relevant type
print(learning_paradigm)
token_list = map(str, args.__dict__.values())
token_list.reverse()
token = "-".join(token_list)
token = token.replace('[','c')
token = token.replace(']','')
token = token.replace(' ','_')
token = token.replace(',','')
#make output directory if doesnt exist
output_dir = os.path.join('/jmain01/home/JAD017/sjr01/mxw35-sjr01/Projects/CVAE/output/results_masked', model_name ,dataset_name)
if os.path.isdir(output_dir) == False:
os.makedirs(output_dir)
x_train, y_train, x_test, y_test, binarize, x_dist, n_y, n_x, f_enc, f_dec = load_dataset(dataset_name, preproc=True, bio_t=[i for i in range(40)])
if dataset_name == 'activity' or dataset_name == 'biobank' or dataset_name == 'activity_basic':
num_labelled=None
num_classes = y_train[0][0].shape[1]
else:
num_labelled = int(prop_labelled*x_train.shape[0])
num_classes = y_train.shape[1]
if dataset_name == 'activity' or dataset_name == 'biobank':
classes_to_hide=[1]
elif dataset_name == 'activity_basic':
classes_to_hide=[7]
#remove certain classes:
if classes_to_hide is not None and dataset_name not in ['activity', 'biobank','activity_basic']:
num_labelled = [int(float(num_labelled)/num_classes)]*num_classes
for hide_class in classes_to_hide:
num_labelled[hide_class] = 0
prior = np.array([1.0/n_y]*n_y)
if classes_to_hide is not None and dataset_name not in ['activity', 'biobank','activity_basic']:
prior_for_other_classes = (1.0 - (float(n_y)-float(len(classes_to_hide)))/float(n_y))/(float(len(classes_to_hide))+float(number_of_classes_to_add))
for hide_class in classes_to_hide:
prior[hide_class] = prior_for_other_classes
if number_of_classes_to_add >0:
prior = np.concatenate((prior, np.ones(number_of_classes_to_add)*prior_for_other_classes))
elif classes_to_hide is not None and dataset_name in ['activity', 'biobank','activity_basic']:
weight_for_old_classes = 0.5
empricial_counts = np.array(Counter(y_train[0][0].argmax(1)).values(), 'float')/sum(Counter(y_train[0][0].argmax(1)).values())
prior_for_other_classes = (1-weight_for_old_classes)/float(number_of_classes_to_add+len(classes_to_hide))
mask = np.ones(prior.shape,dtype=bool)
mask[classes_to_hide] = 0
prior[mask] = weight_for_old_classes * empricial_counts
for hide_class in classes_to_hide:
prior[hide_class] = prior_for_other_classes
if number_of_classes_to_add >0:
prior = np.concatenate((prior, np.ones(number_of_classes_to_add)*prior_for_other_classes))
n_y = n_y + number_of_classes_to_add
if dataset_name=='activity' or dataset_name=='biobank' or dataset_name == 'activity_basic':
Data = make_dataset(learning_paradigm, x_train=[], y_train=[], x_test=x_test, y_test=y_test[0], dataset_name=dataset_name, num_labelled=num_labelled, number_of_classes_to_add=number_of_classes_to_add, do_split = False, x_labelled=x_train[0], y_labelled=y_train[0][0], x_unlabelled=x_train[1], y_unlabelled=y_train[0][1])
else:
Data = make_dataset(learning_paradigm, x_train, y_train, x_test, y_test, dataset_name, num_labelled=num_labelled, number_of_classes_to_add=number_of_classes_to_add)
if prop_labelled<0.002 and prop_labelled>0:
l_bs, u_bs = 10,100
alpha = 0.02
else:
l_bs, u_bs = 111,111
alpha = 0.1
loss_ratio = float(l_bs)/float(u_bs)
# Specify model parameters
lr = (3e-4,)
n_a = 100
n_w = 50
n_hidden = [500, 500]
temp_epochs, start_temp = None, 0.0
l2_reg, initVar, alpha = .5, -10., 1.1
#batchnorm, mc_samps = True, int(sys.argv[6])
batchnorm, mc_samps = False, 1
eval_samps = 1000
logging, verbose = False, 3
np.random.seed(seed=0)
Data.reset_counters()
results=[]
for i in range(num_runs):
print("Starting work on run: {}".format(i))
Data.reset_counters()
np.random.seed(2)
tf.set_random_seed(2)
tf.reset_default_graph()
model_token = token+'-'+str(i)+'---'
if model_name == 'm2':
model = m2(n_x, n_y, n_z, n_hidden, x_dist=x_dist, batchnorm=batchnorm, mc_samples=mc_samps, l2_reg=l2_reg, learning_paradigm=learning_paradigm, name=model_token, ckpt = model_token)
#
if model_name == 'm2_k':
model = m2_k(n_x, n_y, n_z, n_hidden, x_dist=x_dist, batchnorm=batchnorm, alpha=alpha, mc_samples=mc_samps, l2_reg=l2_reg, learning_paradigm=learning_paradigm, name=model_token, ckpt = model_token, prior=prior, loss_ratio=loss_ratio, output_dir=output_dir)
#
if model_name == 'gm_dgm':
model = gm_dgm(n_x, n_y, n_z, n_hidden, x_dist=x_dist, batchnorm=batchnorm, alpha=alpha, mc_samples=mc_samps, l2_reg=l2_reg, learning_paradigm=learning_paradigm, name=model_token, ckpt = model_token, prior=prior[0:n_y]/float(sum(prior[0:n_y])), loss_ratio=loss_ratio, output_dir=output_dir)
#
if model_name == 'gm_dgm_conv':
model = gm_dgm_conv(n_x, n_y, n_z, n_hidden, x_dist=x_dist, batchnorm=batchnorm, alpha=alpha, mc_samples=mc_samps, l2_reg=l2_reg, learning_paradigm=learning_paradigm, name=model_token, ckpt = model_token, prior=prior[0:n_y]/float(sum(prior[0:n_y])), loss_ratio=loss_ratio, output_dir=output_dir)
#
if model_name == 'gmm_vae':
model = gmm_vae(n_x, n_y, n_w, n_z, n_hidden, x_dist=x_dist, batchnorm=batchnorm, mc_samples=mc_samps, l2_reg=l2_reg, learning_paradigm=learning_paradigm, name=model_token, ckpt = model_token)
#
if model_name == 'dgmm_z':
model = dgmm_z(n_x, n_y, n_z1, n_z2, n_hidden, x_dist=x_dist, batchnorm=batchnorm, mc_samples=mc_samps, l2_reg=l2_reg, learning_paradigm=learning_paradigm, name=model_token, ckpt = model_token)
#
if model_name == 'adgm_k':
model = adgm_k(n_x, n_y, n_z, n_a, n_hidden, x_dist=x_dist, batchnorm=batchnorm, mc_samples=mc_samps, l2_reg=l2_reg, learning_paradigm=learning_paradigm, name=model_token, ckpt = model_token)
#
if model_name == 'agm_dgm':
model = agm_dgm(n_x, n_y, n_z, n_a, n_hidden, x_dist=x_dist, batchnorm=batchnorm, mc_samples=mc_samps, l2_reg=l2_reg, learning_paradigm=learning_paradigm, name=model_token, ckpt = model_token)
#
model.loss = model.compute_loss()
model.train(Data, n_epochs, l_bs, u_bs, lr, eval_samps=eval_samps, temp_epochs=temp_epochs, start_temp=start_temp, binarize=binarize, logging=logging, verbose=verbose)
results.append(model.curve_array)
np.save(os.path.join(output_dir,'curve_'+token+'_'+str(i)+'.npy'), model.curve_array)
y_pred_test = predict_new(Data.data['x_test'])[0]
conf_mat = confusion_matrix(Data.data['y_test'].argmax(1), y_pred_test.argmax(1))
np.save(os.path.join(output_dir,'conf_mat_'+token+'_'+str(i)+'.npy'), conf_mat)
np.savez(os.path.join(output_dir,'y_preds_labels_'+token+'_'+str(i)+'.npz'), y_true=Data.data['y_test'].argmax(1), y_pred=y_pred_test.argmax(1), y_labels = y_test[1])
if learning_paradigm == 'semisupervised' or learning_paradigm == 'un-semisupervised':
Data.recreate_semisupervised(i)
np.save(os.path.join(output_dir,'results_'+ token+'.npy'), results)
|
#!/usr/bin/env python
from __future__ import print_function
import sys
import pmagpy.pmag as pmag
def main():
"""
NAME
sort_specimens.py
DESCRIPTION
Reads in a pmag_specimen formatted file and separates it into different components (A,B...etc.)
SYNTAX
sort_specimens.py [-h] [command line options]
INPUT
takes pmag_specimens.txt formatted input file
OPTIONS
-h: prints help message and quits
-f FILE: specify input file, default is 'pmag_specimens.txt'
OUTPUT
makes pmag_specimen formatted files with input filename plus _X_Y
where X is the component name and Y is s,g,t for coordinate system
"""
dir_path='.'
inspec="pmag_specimens.txt"
if '-WD' in sys.argv:
ind=sys.argv.index('-WD')
dir_path=sys.argv[ind+1]
if '-h' in sys.argv:
print(main.__doc__)
sys.exit()
if '-f' in sys.argv:
ind=sys.argv.index('-f')
inspec=sys.argv[ind+1]
basename=inspec.split('.')[:-1]
inspec=dir_path+"/"+inspec
ofile_base=dir_path+"/"+basename[0]
#
# read in data
#
prior_spec_data,file_type=pmag.magic_read(inspec)
if file_type != 'pmag_specimens':
print(file_type, " this is not a valid pmag_specimens file")
sys.exit()
# get list of specimens in file, components, coordinate systems available
specs,comps,coords=[],[],[]
for spec in prior_spec_data:
if spec['er_specimen_name'] not in specs:specs.append(spec['er_specimen_name'])
if 'specimen_comp_name' not in list(spec.keys()):spec['specimen_comp_name']='A'
if 'specimen_tilt_correction' not in list(spec.keys()):spec['tilt_correction']='-1' # assume specimen coordinates
if spec['specimen_comp_name'] not in comps:comps.append(spec['specimen_comp_name'])
if spec['specimen_tilt_correction'] not in coords:coords.append(spec['specimen_tilt_correction'])
# work on separating out components, coordinate systems by specimen
for coord in coords:
print(coord)
for comp in comps:
print(comp)
speclist=[]
for spec in prior_spec_data:
if spec['specimen_tilt_correction']==coord and spec['specimen_comp_name']==comp:speclist.append(spec)
ofile=ofile_base+'_'+coord+'_'+comp+'.txt'
pmag.magic_write(ofile,speclist,'pmag_specimens')
print('coordinate system: ',coord,' component name: ',comp,' saved in ',ofile)
if __name__ == "__main__":
main()
|
#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: zlikun
import asyncio
import logging
import aiohttp
default_headers = {
"Host": "www.biquge5200.cc",
"Referer": "https://www.biquge5200.cc"
}
async def download(session, url, headers={}, error_url_queue=None):
"""
HTTP下载函数，下载失败后会重试三次（重试间隔1秒）
:param session: 下载客户端会话
:param url: 下载URL地址
:param headers: 请求消息头
:param error_url_queue: 失败URL队列，如果指定该参数则不直接重试，而是将失败的URL添加到该队列，该队列值必须是：asyncio.Queue对象
:return: 返回下载文档内容
"""
headers.update(default_headers)
for i in range(4):
try:
async with session.get(url, headers=headers) as resp:
resp.raise_for_status()
return await resp.text()
except aiohttp.ClientError:
logging.error("下载 {} 出错！".format(url))
# 如果队列非空，将URL添加到队列
if error_url_queue is not None:
await error_url_queue.put(url)
return None
else:
await asyncio.sleep(i / 10)
async def testing():
"""
测试函数
:return:
"""
async with aiohttp.ClientSession() as session:
# 下载章节列表
print("============================== 章节列表 ==============================")
print(await download(session, "https://www.biquge5200.cc/52_52542/"))
# 下载章节正文
print("============================== 章节正文 ==============================")
print(await download(session, "https://www.biquge5200.cc/52_52542/20380548.html"))
# 下载错误网页
print("============================== 错误网页 ==============================")
queue = asyncio.Queue(16)
print(await download(session, "https://zlikun.com/404", error_url_queue=queue))
await queue.put(None) # 当任务执行完成后，添加一个结束标记
while True:
data = await queue.get()
if data is None:
break
else:
print("失败URL：{}".format(data))
if __name__ == '__main__':
loop = asyncio.get_event_loop()
loop.run_until_complete(testing())
loop.close()
|
{
"cells": [
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"#Importing relevant libraries\n",
"import numpy as np\n",
"import pandas as pd\n",
"import datetime\n",
"from datetime import datetime,timedelta, date, time\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from statistics import mode\n",
"from itertools import chain"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Webhook Send"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"def webhook_send(data_to_send):\n",
" \n",
" import json \n",
" import requests \n",
" \n",
" answer = str(input('Send Data? (Yes/No): '))\n",
" \n",
" if answer == 'Yes':\n",
" destination_url = str(input('Enter URL to send data to: '))\n",
" \n",
" data = data_to_send.to_json(orient='split')\n",
" r = requests.post(destination_url,data=json.dumps(data), \n",
" headers = {'Content-Type':'application/json'})\n",
" \n",
" print('Data Succesfully Sent to {}'.format(destination_url))\n",
" else:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"custom_syle = {'axes.grid': False,'xtick.bottom': True,\n",
" 'ytick.left': True, 'patch.edgecolor': 'black',\n",
" 'patch.force_edgecolor': False}\n",
"\n",
"sns.set_style('darkgrid', rc= custom_syle)\n",
"plt.style.use('dark_background')"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"sleep_csv = 'SLEEP.csv'\n",
"ex_csv = 'HEARTRATE_AUTO.csv'\n",
"age_csv = 'USER.csv'\n",
"# num_of_days_to_show = 7"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"def choose_num_days_to_show(auto=bool):\n",
" \"\"\"\n",
" Select how many days to show for graphs and data throughout the report.\n",
" \n",
" Auto mode is for autoamted report generation\n",
" \n",
" \"\"\"\n",
" if auto == True:\n",
" \n",
" if len(sleep_data_adj_len) < 7:\n",
"\n",
" num_days_shown = len(sleep_data_adj_len)\n",
"\n",
" else:\n",
" num_days_shown = 7\n",
" \n",
" return num_days_shown\n",
" \n",
" else:\n",
" num_days_shown = int(input('Enter Number of Days to show: '))\n",
" return num_days_shown"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Importing Data"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"sleep_data_all = pd.read_csv(sleep_csv)\n",
"\n",
"#Change this so we can test for different number of days \n",
"sleep_data_adj_len = sleep_data_all[:4].copy()\n",
"\n",
"#number of days to show\n",
"num_days_shown = choose_num_days_to_show(auto=True)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"sleep_data_adj_len.rename(columns= {'start':'BT', 'stop' :'WT', 'date':'Date'}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>deepSleepTime</th>\n",
" <th>shallowSleepTime</th>\n",
" <th>wakeTime</th>\n",
" <th>BT</th>\n",
" <th>WT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2021-11-14</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2021-11-12 21:00:00+0000</td>\n",
" <td>2021-11-12 21:00:00+0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2021-11-15</td>\n",
" <td>109</td>\n",
" <td>343</td>\n",
" <td>1</td>\n",
" <td>2021-11-14 20:24:00+0000</td>\n",
" <td>2021-11-15 03:57:00+0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2021-11-16</td>\n",
" <td>80</td>\n",
" <td>238</td>\n",
" <td>2</td>\n",
" <td>2021-11-15 18:54:00+0000</td>\n",
" <td>2021-11-16 01:54:00+0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2021-11-17</td>\n",
" <td>114</td>\n",
" <td>316</td>\n",
" <td>9</td>\n",
" <td>2021-11-16 19:12:00+0000</td>\n",
" <td>2021-11-17 03:28:00+0000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date deepSleepTime shallowSleepTime wakeTime \\\n",
"0 2021-11-14 0 0 0 \n",
"1 2021-11-15 109 343 1 \n",
"2 2021-11-16 80 238 2 \n",
"3 2021-11-17 114 316 9 \n",
"\n",
" BT WT \n",
"0 2021-11-12 21:00:00+0000 2021-11-12 21:00:00+0000 \n",
"1 2021-11-14 20:24:00+0000 2021-11-15 03:57:00+0000 \n",
"2 2021-11-15 18:54:00+0000 2021-11-16 01:54:00+0000 \n",
"3 2021-11-16 19:12:00+0000 2021-11-17 03:28:00+0000 "
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sleep_data_adj_len"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sleep Data Manipulation + Calculating Sleep Duration"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"def avg_time(datetimes):\n",
" total = sum(dt.hour * 3600 + dt.minute * 60 + dt.second for dt in datetimes)\n",
" avg = total / len(datetimes)\n",
" minutes, seconds = divmod(int(avg), 60)\n",
" hours, minutes = divmod(minutes, 60)\n",
" return datetime.combine(date(1900, 1, 1), time(hours, minutes, seconds))\n",
"\n",
"def find_empty_rows(df,reset_index=False):\n",
" rows_to_change = []\n",
" \n",
" if reset_index == True:\n",
" df.reset_index(inplace=True, drop=True)\n",
" \n",
" for i in range(len(df)):\n",
" if df['deepSleepTime'][i]==0 and df['shallowSleepTime'][i]== 0 and df['wakeTime'][i] == 0:\n",
" rows_to_change.append(i)\n",
" return rows_to_change\n",
"\n",
"def find_and_replace_empty_times(df, wake_times_dt_format, rows_to_change):\n",
"\n",
" wake_times_dt_format = [i for j, i in enumerate(wake_times_dt_format) if j not in rows_to_change]\n",
" \n",
" for i in range(len(rows_to_change)):\n",
" new_dt = str(wake_times_dt_format[rows_to_change[i]-1].date() + timedelta(days=1)) + ' ' + str(avg_time(wake_times_dt_format).time()) \n",
" wake_times_dt_format.insert(rows_to_change[i],datetime.strptime(new_dt[0:16], '%Y-%m-%d %H:%M'))\n",
"\n",
" return wake_times_dt_format\n",
"\n",
"def find_and_replace_sleep_scores(sleep_duration_data, rows_to_change):\n",
"\n",
" sleep_duration_data = [i for j, i in enumerate(sleep_duration_data) if j not in rows_to_change]\n",
" \n",
" for i in range(len(rows_to_change)): \n",
" sleep_duration_data.insert(rows_to_change[i],np.mean(sleep_duration_data))\n",
"\n",
" return sleep_duration_data\n",
"\n",
"def find_and_replace_sleep_debt(sleep_debt_data, rows_to_change):\n",
" \n",
" sleep_debt_data = [i for j, i in enumerate(sleep_debt_data) if j not in rows_to_change]\n",
" \n",
" for i in range(len(rows_to_change)): \n",
" sleep_debt_data.insert(rows_to_change[i],0)\n",
"\n",
" return sleep_debt_data\n",
"\n",
"def find_and_replace_SC(bt_sleep_cons,wt_sleep_cons):\n",
" \n",
" rows_to_change = [] \n",
" \n",
" for i in range(len(bt_sleep_cons)):\n",
" if bt_sleep_cons[i]==1440 and wt_sleep_cons[i]== 1440:\n",
" rows_to_change.append(i)\n",
" rows_to_change\n",
" \n",
" if len(rows_to_change) == 0:\n",
" return bt_sleep_cons, wt_sleep_cons\n",
" \n",
" else:\n",
" \n",
" bt_sleep_cons_data = [i for j, i in enumerate(bt_sleep_cons) if j not in rows_to_change]\n",
" wt_sleep_cons_data = [i for j, i in enumerate(wt_sleep_cons) if j not in rows_to_change]\n",
" \n",
" new_bt = round(np.mean(bt_sleep_cons_data))\n",
" new_wt = round(np.mean(wt_sleep_cons_data))\n",
" \n",
" new_bt_sleep_cons_data = [new_bt if x==1440 else x for x in bt_sleep_cons]\n",
" new_wt_sleep_cons_data = [new_wt if x==1440 else x for x in wt_sleep_cons]\n",
"\n",
" \n",
" return new_bt_sleep_cons_data, new_wt_sleep_cons_data"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"rows_to_change = find_empty_rows(sleep_data_adj_len)\n",
"\n",
"#For graphs later on\n",
"labels_to_change = find_empty_rows(sleep_data_adj_len[-num_days_shown:], reset_index=True)\n",
"\n",
"#Converting Start and Stop times to datetime.datetime objects \n",
"## Use as Master List of Datetime.datetime for prediction later on, change col values for date to strings \n",
"wt_dt_form = [datetime.strptime(sleep_data_adj_len['WT'][i][0:19], '%Y-%m-%d %H:%M:%S') for i in range(len(sleep_data_adj_len))]\n",
"bt_dt_form = [datetime.strptime(sleep_data_adj_len['BT'][i][0:19], '%Y-%m-%d %H:%M:%S') for i in range(len(sleep_data_adj_len))]\n",
"\n",
"wt_dt_form = find_and_replace_empty_times(sleep_data_adj_len,wt_dt_form, rows_to_change=rows_to_change)\n",
"bt_dt_form = find_and_replace_empty_times(sleep_data_adj_len,bt_dt_form, rows_to_change=rows_to_change)\n",
"\n",
"#Converting from datetime.datetime objects --> Datetime format for table \n",
"\n",
"## S1: Convert datetime.datetime objects to unix timestamps\n",
"sleep_data_adj_len['BT'] = [bt_dt_form[i].timestamp() for i in range(len(sleep_data_adj_len))]\n",
"sleep_data_adj_len['WT'] = [wt_dt_form[i].timestamp() for i in range(len(sleep_data_adj_len))]\n",
"\n",
"sleep_data_adj_len['Date'] = [datetime.strftime(i, '%d/%m/%Y') for i in wt_dt_form]\n",
"\n",
"### Calculate Sleep duration while WT,BT while WT/BT data is float type\n",
"# print(sleep_data_adj_len['WT'][4:7].values)\n",
"# # print(sleep_data_adj_len['BT'][4:7].values)\n",
"\n",
"sleep_dur_mins_temp = [int(i) for i in (sleep_data_adj_len['WT'].values - sleep_data_adj_len['BT'].values)/60]\n",
"sleep_dur_hrs_temp = [round(i,2) for i in (sleep_data_adj_len['WT'].values - sleep_data_adj_len['BT'].values)/3600]\n",
"\n",
"sleep_data_adj_len['Sleep Duration Mins'] = [round(i) for i in find_and_replace_sleep_scores(sleep_dur_mins_temp, rows_to_change)]\n",
"sleep_data_adj_len['Sleep Duration Hrs'] = [round(i,2) for i in find_and_replace_sleep_scores(sleep_dur_hrs_temp, rows_to_change)]\n",
"sleep_debt_data = sleep_data_adj_len.apply(lambda row : round((row['Sleep Duration Hrs'] - 8),2),axis=1)\n",
"sleep_data_adj_len['Daily Sleep Debt'] = find_and_replace_sleep_debt(sleep_debt_data, rows_to_change)\n",
"\n",
"#Converting from Unixtimestamps to appropriate Datetime format for table \n",
"\n",
"## S2: Converting Unixtimestamps to Timestrings\n",
"sleep_data_adj_len['BT'] = [datetime.fromtimestamp(i).strftime('%H:%M') for i in sleep_data_adj_len['BT']]\n",
"sleep_data_adj_len['WT'] = [datetime.fromtimestamp(i).strftime('%H:%M') for i in sleep_data_adj_len['WT']]\n",
"\n",
"## S3: Converting Timestrings to Datetime format \n",
"sleep_data_adj_len['BT'] = [datetime.strptime(i,'%H:%M').time() for i in sleep_data_adj_len['BT']]\n",
"sleep_data_adj_len['WT'] = [datetime.strptime(i,'%H:%M').time() for i in sleep_data_adj_len['WT']]\n"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>deepSleepTime</th>\n",
" <th>shallowSleepTime</th>\n",
" <th>wakeTime</th>\n",
" <th>BT</th>\n",
" <th>WT</th>\n",
" <th>Sleep Duration Mins</th>\n",
" <th>Sleep Duration Hrs</th>\n",
" <th>Daily Sleep Debt</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>18/11/2021</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>19:30:00</td>\n",
" <td>03:06:00</td>\n",
" <td>456</td>\n",
" <td>7.61</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>15/11/2021</td>\n",
" <td>109</td>\n",
" <td>343</td>\n",
" <td>1</td>\n",
" <td>20:24:00</td>\n",
" <td>03:57:00</td>\n",
" <td>453</td>\n",
" <td>7.55</td>\n",
" <td>-0.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>16/11/2021</td>\n",
" <td>80</td>\n",
" <td>238</td>\n",
" <td>2</td>\n",
" <td>18:54:00</td>\n",
" <td>01:54:00</td>\n",
" <td>420</td>\n",
" <td>7.00</td>\n",
" <td>-1.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>17/11/2021</td>\n",
" <td>114</td>\n",
" <td>316</td>\n",
" <td>9</td>\n",
" <td>19:12:00</td>\n",
" <td>03:28:00</td>\n",
" <td>496</td>\n",
" <td>8.27</td>\n",
" <td>0.27</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date deepSleepTime shallowSleepTime wakeTime BT WT \\\n",
"0 18/11/2021 0 0 0 19:30:00 03:06:00 \n",
"1 15/11/2021 109 343 1 20:24:00 03:57:00 \n",
"2 16/11/2021 80 238 2 18:54:00 01:54:00 \n",
"3 17/11/2021 114 316 9 19:12:00 03:28:00 \n",
"\n",
" Sleep Duration Mins Sleep Duration Hrs Daily Sleep Debt \n",
"0 456 7.61 0.00 \n",
"1 453 7.55 -0.45 \n",
"2 420 7.00 -1.00 \n",
"3 496 8.27 0.27 "
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sleep_data_adj_len"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calculating Sleep Consistency + SDD + Daily Sleep Score"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <u>Formulas</u>"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"def convert_time(time_lst:list):\n",
" \"\"\"\n",
" Converts datetimes to integers so they are appropriately spaced based on the the 24hr clock.\n",
" \n",
" Any time between 0 - 12 converted to 24 hour clock:\n",
" e.g. 7:27 --> (24 + 7x60) + 27 ---> 1887\n",
" \n",
" Any other time (13 --> 23):\n",
" e.g. 21:35 --> (23*60) + 35 ---> 1295 \n",
" \n",
" \n",
" \"\"\"\n",
" converted_times = [] \n",
" \n",
" for i in range(len(time_lst)):\n",
"\n",
" if time_lst[i].hour == 0:\n",
" converted_time = 24*60 + time_lst[i].minute\n",
" converted_times.append(converted_time)\n",
" \n",
"\n",
" elif 0 < time_lst[i].hour < 12:\n",
" converted_time = (24 + time_lst[i].hour)*60 + time_lst[i].minute\n",
" converted_times.append(converted_time)\n",
" \n",
"\n",
" else:\n",
" converted_time = time_lst[i].hour*60 + time_lst[i].minute\n",
" converted_times.append(converted_time)\n",
"\n",
" return converted_times\n",
"\n",
"def daily_sleep_consistency(bed_time_list:list,wake_time_list:list):\n",
" \"\"\"\n",
" Calculates the Sleep Consistency based on the average BT/WT variability from the mean.\n",
" \n",
" \"\"\"\n",
" import numpy as np \n",
" \n",
" penalisation_factor = 5 \n",
" bt_mean = np.mean(bed_time_list)\n",
" wt_mean = np.mean(wake_time_list)\n",
" \n",
" bt_sub_mean = [] \n",
" wt_sub_mean = []\n",
" \n",
" for i in range(len(bed_time_list)):\n",
" bt_sub_mean.append(abs(bed_time_list[i] - bt_mean))\n",
" wt_sub_mean.append(abs(wake_time_list[i] - wt_mean))\n",
" \n",
" avg_bt_variability = np.mean(bt_sub_mean)/bt_mean\n",
" avg_wt_variability = np.mean(wt_sub_mean)/wt_mean\n",
" \n",
" daily_sleep_consistency = 100 - (((avg_bt_variability+avg_wt_variability)*100)*penalisation_factor)\n",
" \n",
" if daily_sleep_consistency < -100:\n",
" daily_sleep_consistency = -100\n",
" return round(daily_sleep_consistency,1)\n",
" \n",
" else:\n",
" return round(daily_sleep_consistency,1)\n",
" \n",
"def apply_sleep_consistency(wt_list:list, bt_list:list, print_text = True):\n",
" \"\"\"\n",
" Applies sleep consistency using BTs and WTs over the last 4 days. \n",
" \n",
" \"\"\"\n",
" daily_sleep_cons_lst_1 = []\n",
"\n",
" #1440 represents a complete 0 which will usually only occur in when theres missing sleep\n",
" # Replace this with \n",
" \n",
" [None if x==1440 else x for x in bt_list]\n",
" [None if x==1440 else x for x in wt_list]\n",
"\n",
"\n",
" \n",
" for i in range(len(wt_list)):\n",
"\n",
" if i == 0:\n",
"\n",
"# w_times = [wt_list[i]]\n",
"# b_times = [bt_list[i]]\n",
" daily_sleep_cons_lst_1.insert(i,daily_sleep_consistency(bed_time_list=[bt_list[i]],wake_time_list=[wt_list[i]]))\n",
" \n",
" if print_text == True:\n",
" print('Day ' + str(i))\n",
" print('Wake Time Range = {}'.format(max([wt_list[i]])-min([wt_list[i]])))\n",
" print('Bed Time Range = {}'.format(max([bt_list[i]])-min([bt_list[i]])))\n",
" print('Wake Times', [wt_list[i]])\n",
" print('Bed Times', [bt_list[i]])\n",
" print(\"Daily Sleep Consistency = {}\".format(daily_sleep_consistency(bed_time_list=[bt_list[i]],wake_time_list=[wt_list[i]])))\n",
" print('\\n')\n",
"\n",
" elif i == 1:\n",
"\n",
"# w_times_1 = [wt_list[i],wt_list[i-1]]\n",
"# b_times_1 = [bt_list[i],bt_list[i-1]]\n",
" \n",
" daily_sleep_cons_lst_1.insert(i,daily_sleep_consistency(bed_time_list=[bt_list[i],bt_list[i-1]],wake_time_list=[wt_list[i],wt_list[i-1]]))\n",
" \n",
" if print_text == True:\n",
" print('Day ' + str(i))\n",
" print('Wake Times', [wt_list[i],wt_list[i-1]])\n",
" print('Bed Times', [bt_list[i],bt_list[i-1]])\n",
" print('Wake Time Range = {}'.format(max([wt_list[i],wt_list[i-1]])-min([wt_list[i],wt_list[i-1]])))\n",
" print('Bed Time Range = {}'.format(max([bt_list[i],bt_list[i-1]])-min([bt_list[i],bt_list[i-1]])))\n",
" print(\"Daily Sleep Consistency = {}\".format(daily_sleep_consistency(bed_time_list=[bt_list[i],bt_list[i-1]],wake_time_list=[wt_list[i],wt_list[i-1]])))\n",
" print('\\n')\n",
"\n",
" elif i == 2 :\n",
"# w_times = [wt_list[i],wt_list[i-1], wt_list[i-2]]\n",
"# b_times = [bt_list[i],bt_list[i-1], bt_list[i-2]]\n",
" daily_sleep_cons_lst_1.insert(i,daily_sleep_consistency(bed_time_list=[bt_list[i],bt_list[i-1], bt_list[i-2]],wake_time_list=[wt_list[i],wt_list[i-1], wt_list[i-2]]))\n",
" \n",
" if print_text == True: \n",
" print('Day ' + str(i))\n",
" print('Wake Times', [wt_list[i],wt_list[i-1], wt_list[i-2]])\n",
" print('Bed Times', [bt_list[i],bt_list[i-1], bt_list[i-2]])\n",
" print('Wake Time Range = {}'.format(max([wt_list[i],wt_list[i-1], wt_list[i-2]])-min([wt_list[i],wt_list[i-1], wt_list[i-2]])))\n",
" print('Bed Time Range = {}'.format(max([bt_list[i],bt_list[i-1], bt_list[i-2]])-min([bt_list[i],bt_list[i-1], bt_list[i-2]])))\n",
" print(\"Daily Sleep Consistency = {}\".format(daily_sleep_consistency(bed_time_list=[bt_list[i],bt_list[i-1], bt_list[i-2]],wake_time_list=[wt_list[i],wt_list[i-1], wt_list[i-2]])))\n",
" print('\\n')\n",
"\n",
" else:\n",
" \n",
"# w_times = [wt_list[i],wt_list[i-1], wt_list[i-2], wt_list[i-3]] \n",
"# b_times = [bt_list[i],bt_list[i-1], bt_list[i-2], bt_list[i-3]] \n",
" daily_sleep_cons_lst_1.insert(i,daily_sleep_consistency(bed_time_list=[bt_list[i],bt_list[i-1], bt_list[i-2], bt_list[i-3]] ,wake_time_list=[wt_list[i],wt_list[i-1], wt_list[i-2], wt_list[i-3]]))\n",
" \n",
" if print_text == True:\n",
" print('Day ' + str(i))\n",
" print('Wake Times', [wt_list[i],wt_list[i-1], wt_list[i-2], wt_list[i-3]])\n",
" print('Bed Times', [bt_list[i],bt_list[i-1], bt_list[i-2], bt_list[i-3]] )\n",
" print('Wake Time Range = {}'.format(max([wt_list[i],wt_list[i-1], wt_list[i-2], wt_list[i-3]])-min([wt_list[i],wt_list[i-1], wt_list[i-2], wt_list[i-3]])))\n",
" print('Bed Time Range = {}'.format(max([bt_list[i],bt_list[i-1], bt_list[i-2], bt_list[i-3]] )-min([bt_list[i],bt_list[i-1], bt_list[i-2], bt_list[i-3]] )))\n",
" print(\"Daily Sleep Consistency = {}\".format(daily_sleep_consistency(bed_time_list=[bt_list[i],bt_list[i-1], bt_list[i-2], bt_list[i-3]] ,wake_time_list=[wt_list[i],wt_list[i-1], wt_list[i-2], wt_list[i-3]])))\n",
" print('\\n')\n",
" \n",
" return daily_sleep_cons_lst_1 \n",
"\n",
"def daily_SDD(sleep_duration:int,recommended_sleep:int,previous_3_days_sleep:list):\n",
" \"\"\"\n",
" Calculates the Daily SDD using recommended sleep duration, requires last 3 days of sleep to calculate.\n",
" \n",
" \"\"\"\n",
" w1 = 0.13833333 \n",
" \n",
" sleep_duration_mins = sleep_duration*60\n",
" recommended_sleep_duration = recommended_sleep*60\n",
" \n",
" recommended_sleep_4_days= recommended_sleep*(len(previous_3_days_sleep))\n",
" \n",
" total_previous_3_days_sleep_hours = sum(previous_3_days_sleep)\n",
" \n",
" sleep_debt_penalisation = (total_previous_3_days_sleep_hours-recommended_sleep_4_days)*2\n",
" \n",
" if 0 <=sleep_duration_mins<=480:\n",
" \n",
" if len(previous_3_days_sleep) == 0:\n",
" \n",
" sleep_duration_score = ((sleep_duration_mins/recommended_sleep_duration)*100)\n",
" return round(sleep_duration_score,1)\n",
" \n",
" else:\n",
" \n",
" if sleep_debt_penalisation > 0: \n",
" sleep_duration_score = ((sleep_duration_mins/recommended_sleep_duration)*100)\n",
" return round(sleep_duration_score,1)\n",
" \n",
" else:\n",
" sleep_duration_score = ((sleep_duration_mins/recommended_sleep_duration)*100) + sleep_debt_penalisation\n",
" return round(sleep_duration_score,1)\n",
"\n",
" elif 480 < sleep_duration_mins <= 540:\n",
" \n",
" if len(previous_3_days_sleep) == 0:\n",
" \n",
" sleep_duration_score = 200 - ((sleep_duration_mins/recommended_sleep_duration)*100)\n",
" return round(sleep_duration_score,1)\n",
" \n",
" else:\n",
" \n",
" if sleep_debt_penalisation > 0:\n",
" sleep_duration_score = 200 - ((sleep_duration_mins/recommended_sleep_duration)*100)\n",
" return round(sleep_duration_score, 1)\n",
"\n",
" else:\n",
" sleep_duration_score = 200 - ((sleep_duration_mins/recommended_sleep_duration)*100) + sleep_debt_penalisation\n",
" return round(sleep_duration_score, 1)\n",
"\n",
" elif 540 < sleep_duration_mins <= 1160:\n",
" \n",
" if len(previous_3_days_sleep) == 0:\n",
" s_debt_penalty = ((sleep_duration_mins/60) - recommended_sleep)*2\n",
" sleep_duration_score = (75 - ((sleep_duration_mins-600)*w1)) + s_debt_penalty\n",
" return round(sleep_duration_score,1)\n",
" \n",
" else:\n",
" \n",
" if sleep_debt_penalisation > 0:\n",
" sleep_duration_score = ((75 - (sleep_duration_mins-600)*w1)) + sleep_debt_penalisation\n",
" return round(sleep_duration_score, 1)\n",
" \n",
" else:\n",
" sleep_duration_score = ((75 - (sleep_duration_mins-600)*w1)) + sleep_debt_penalisation\n",
" return round(sleep_duration_score, 1)\n",
" \n",
" else:\n",
" return round(0,1)\n",
" \n",
"\n",
"def apply_SDD(rec_sleep_dur:int, sleep_duration_list:list, print_text=True):\n",
" \"\"\"\n",
" Applies the Daily SDD using sleep duration ussing previous 3 days sleep for each day.\n",
" \n",
" \"\"\"\n",
"\n",
" daily_SDD_scores = [] \n",
" \n",
" for i in range(len(sleep_duration_list)):\n",
" \n",
" if i == 0:\n",
"\n",
" sleep_duration = sleep_duration_list[i] \n",
" sleep_duration_times = []\n",
" daily_SDD_scores.append(daily_SDD(sleep_duration=sleep_duration, recommended_sleep=rec_sleep_dur,previous_3_days_sleep=sleep_duration_times))\n",
" \n",
" if print_text == True:\n",
" print('Day ' + str(i+1))\n",
" print('Sleep Duration Times', sleep_duration_times)\n",
" print('Day ' + str(i+1), 'Sleep Duration = {}'.format(sleep_duration) )\n",
" print('Daily SDD = {}'.format(daily_SDD(sleep_duration=sleep_duration, recommended_sleep=rec_sleep_dur,previous_3_days_sleep=sleep_duration_times)))\n",
" print('\\n')\n",
" \n",
"\n",
" elif i == 1:\n",
" \n",
" sleep_duration_times = [sleep_duration_list[i-1]]\n",
" sleep_duration = sleep_duration_list[i]\n",
" daily_SDD_scores.append(daily_SDD(sleep_duration=sleep_duration, recommended_sleep=rec_sleep_dur,previous_3_days_sleep=sleep_duration_times))\n",
" \n",
" if print_text == True:\n",
" print('Day ' + str(i+1))\n",
" print('Sleep Duration Times', sleep_duration_times)\n",
" print('Day ' + str(i+1), 'Sleep Duration = {}'.format(sleep_duration) )\n",
" print('Daily SDD = {}'.format(daily_SDD(sleep_duration=sleep_duration, recommended_sleep=rec_sleep_dur,previous_3_days_sleep=sleep_duration_times)))\n",
" print('\\n')\n",
" \n",
"\n",
" elif i == 2 :\n",
"\n",
" sleep_duration_times =[sleep_duration_list[i-1], sleep_duration_list[i-2]]\n",
" sleep_duration = sleep_duration_list[i] \n",
" daily_SDD_scores.append(daily_SDD(sleep_duration=sleep_duration, recommended_sleep=rec_sleep_dur,previous_3_days_sleep=sleep_duration_times))\n",
" \n",
" if print_text == True:\n",
" print('Day ' + str(i+1))\n",
" print('Sleep Duration Times', sleep_duration_times)\n",
" print('Day ' + str(i+1), 'Sleep Duration = {}'.format(sleep_duration) )\n",
" print('Daily SDD = {}'.format(daily_SDD(sleep_duration=sleep_duration, recommended_sleep=rec_sleep_dur,previous_3_days_sleep=sleep_duration_times)))\n",
" print('\\n')\n",
"\n",
" else:\n",
"\n",
" sleep_duration_times =[sleep_duration_list[i-1], sleep_duration_list[i-2], sleep_duration_list[i-3]]\n",
" sleep_duration = sleep_duration_list[i]\n",
" daily_SDD_scores.append(daily_SDD(sleep_duration=sleep_duration, recommended_sleep=rec_sleep_dur,previous_3_days_sleep=sleep_duration_times))\n",
" \n",
" if print_text == True:\n",
" print('Day ' + str(i+1))\n",
" print('Sleep Duration Times', sleep_duration_times)\n",
" print('Day ' + str(i+1), 'Sleep Duration = {}'.format(sleep_duration) )\n",
" print('Daily SDD = {}'.format(daily_SDD(sleep_duration=sleep_duration, recommended_sleep=rec_sleep_dur,previous_3_days_sleep=sleep_duration_times)))\n",
" print('\\n')\n",
" \n",
" return daily_SDD_scores"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Date', 'deepSleepTime', 'shallowSleepTime', 'wakeTime', 'BT', 'WT',\n",
" 'Sleep Duration Mins', 'Sleep Duration Hrs', 'Daily Sleep Debt'],\n",
" dtype='object')"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sleep_data_adj_len.columns"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"#Converting Times to format for Sleep Consistency, Applying SC formula, Inserting results\n",
"\n",
"##S1: Convert BT + WT to apporpiate nubmers\n",
"bt_sleep_cons = convert_time(sleep_data_adj_len['BT'].values)\n",
"wt_sleep_cons = convert_time(sleep_data_adj_len['WT'].values)\n",
"\n",
"##S2: Replace any missing data with mean of converted sleep conssistency values for BT and WT data \n",
"bt_consitency_data, wt_consistency_data = find_and_replace_SC(bt_sleep_cons=bt_sleep_cons, \n",
" wt_sleep_cons=wt_sleep_cons)\n",
"##S3:Apply SC formula to data \n",
"daily_SC_list = apply_sleep_consistency(wt_list=wt_consistency_data, \n",
" bt_list= bt_consitency_data, print_text=False)\n",
"##S3:Insert resultss to Dataframe \n",
"sleep_data_adj_len.insert(len(sleep_data_adj_len.columns), 'Daily Sleep Consistency',daily_SC_list)\n",
"\n",
"#Applying SDD formula, Inserting results\n",
"sleep_dur_hrs = sleep_data_adj_len['Sleep Duration Hrs'].values.flatten()\n",
"daily_SDD_list = apply_SDD(rec_sleep_dur=8,sleep_duration_list=sleep_dur_hrs, print_text=False)\n",
"sleep_data_adj_len.insert(len(sleep_data_adj_len.columns), 'Daily SDD',daily_SDD_list)\n",
"\n",
"#Calculating Overall Sleep Score, Applying sleep score formula across every row in the Table\n",
"sleep_data_adj_len['Daily Sleep Score'] = sleep_data_adj_len.apply(\n",
" lambda row : (row['Daily Sleep Consistency']*0.3) + (row['Daily SDD']*0.7), axis=1)\n"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"sleep_data_adj_len.drop(columns = ['deepSleepTime', 'shallowSleepTime', 'wakeTime'], inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Final Sleep Data Table"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>BT</th>\n",
" <th>WT</th>\n",
" <th>Sleep Duration Mins</th>\n",
" <th>Sleep Duration Hrs</th>\n",
" <th>Daily Sleep Debt</th>\n",
" <th>Daily Sleep Consistency</th>\n",
" <th>Daily SDD</th>\n",
" <th>Daily Sleep Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>18/11/2021</td>\n",
" <td>19:30:00</td>\n",
" <td>03:06:00</td>\n",
" <td>456</td>\n",
" <td>7.61</td>\n",
" <td>0.00</td>\n",
" <td>100.0</td>\n",
" <td>95.1</td>\n",
" <td>96.57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>15/11/2021</td>\n",
" <td>20:24:00</td>\n",
" <td>03:57:00</td>\n",
" <td>453</td>\n",
" <td>7.55</td>\n",
" <td>-0.45</td>\n",
" <td>81.0</td>\n",
" <td>93.6</td>\n",
" <td>89.82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>16/11/2021</td>\n",
" <td>18:54:00</td>\n",
" <td>01:54:00</td>\n",
" <td>420</td>\n",
" <td>7.00</td>\n",
" <td>-1.00</td>\n",
" <td>73.0</td>\n",
" <td>85.8</td>\n",
" <td>81.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>17/11/2021</td>\n",
" <td>19:12:00</td>\n",
" <td>03:28:00</td>\n",
" <td>496</td>\n",
" <td>8.27</td>\n",
" <td>0.27</td>\n",
" <td>77.3</td>\n",
" <td>92.9</td>\n",
" <td>88.22</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date BT WT Sleep Duration Mins Sleep Duration Hrs \\\n",
"0 18/11/2021 19:30:00 03:06:00 456 7.61 \n",
"1 15/11/2021 20:24:00 03:57:00 453 7.55 \n",
"2 16/11/2021 18:54:00 01:54:00 420 7.00 \n",
"3 17/11/2021 19:12:00 03:28:00 496 8.27 \n",
"\n",
" Daily Sleep Debt Daily Sleep Consistency Daily SDD Daily Sleep Score \n",
"0 0.00 100.0 95.1 96.57 \n",
"1 -0.45 81.0 93.6 89.82 \n",
"2 -1.00 73.0 85.8 81.96 \n",
"3 0.27 77.3 92.9 88.22 "
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sleep_data_adj_len"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prepping Data for Graphs "
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"def bt_conversion_graph(bed_times):\n",
" \"\"\"\n",
" Converts bed times into number that reprsents that times adjusted position on the graph.\n",
" datetime.time --> int \n",
" \n",
" \n",
" e.g. 22:24PM ---> -1.76\n",
" \n",
" \"\"\"\n",
" \n",
" time_lst = []\n",
" \n",
" for i in range(len(bed_times)):\n",
" if int(bed_times[i].strftime('%H%M'))/100 >=12:\n",
" time_lst.append((int(bed_times[i].strftime('%H%M'))/100)-24)\n",
" else:\n",
" time_lst.append((int(bed_times[i].strftime('%H%M'))/100))\n",
" \n",
" return time_lst"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"#MASTER VARIABLE TO CHANGE WHICH DECIDES HOW MANY OF THE PAST DAYS TO SHOW IN SLEEP CONSISTENCY GRAPH \n",
"\n",
"final_w_times = [(int(i.strftime('%H%M'))/100) for i in sleep_data_adj_len['WT'][-num_days_shown:].values]\n",
"final_b_times = bt_conversion_graph(sleep_data_adj_len['BT'][-num_days_shown:].values)\n",
"sleep_dur_times = [str(round(i,1)) + ' Hours' for i in sleep_data_adj_len['Sleep Duration Hrs'].values]\n",
" \n",
"bed_time_labels = [str(i)[0:5] for i in sleep_data_adj_len['BT'][-num_days_shown:].values]\n",
"wake_time_labels = [str(i)[0:5] for i in sleep_data_adj_len['WT'][-num_days_shown:].values]\n",
"\n",
"#Creating axis labels and annotation labels\n",
"sleep_dur_labels = [str(i).split('.')[0] + ' Hours' + '\\n' + str(int(int(str(round(i,1)).split('.')[1])/10*60)) + ' Mins' for i in sleep_data_adj_len['Sleep Duration Hrs'].values[-num_days_shown:]]\n",
"last7daylbls = [i.strftime('%A') for i in wt_dt_form[-num_days_shown:]]\n",
"date_labels = [i.strftime('%d/%m') for i in wt_dt_form[-num_days_shown:]]\n",
"final_date_labels = [last7daylbls[i] + '\\n(' + date_labels[i] + ')' for i in range(len(last7daylbls))]"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"final_w_times = [i for j, i in enumerate(final_w_times) if j not in labels_to_change]\n",
"final_b_times = [i for j, i in enumerate(final_b_times) if j not in labels_to_change] \n",
"sleep_dur_labels = [i for j, i in enumerate(sleep_dur_labels) if j not in labels_to_change]\n",
"bed_time_labels = [i for j, i in enumerate(bed_time_labels) if j not in labels_to_change] \n",
"wake_time_labels = [i for j, i in enumerate(wake_time_labels) if j not in labels_to_change] \n",
"\n",
"for i in range(len(labels_to_change)): \n",
" final_b_times.insert(labels_to_change[i],0)\n",
" final_w_times.insert(labels_to_change[i],0)\n",
" bed_time_labels.insert(labels_to_change[i],'')\n",
" wake_time_labels.insert(labels_to_change[i],'')\n",
" sleep_dur_labels.insert(labels_to_change[i],'No\\nSleep\\nDetected')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plotting Sleep Consistency "
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"def plot_sleep_cons(w_times, b_times, bt_labels, wt_labels, sd_labels, fd_labels):\n",
" bt_labels_h = [0,0.7,0.7,0.8,0.75,0.75,0.75,0.75]\n",
" wt_labels_h = [0,0.4,0.4,0.4,0.4,0.4,0.4,0.4] \n",
" sd_labels_h = [0,3.3,4,3.3,3.4,3.5,3.5,3.5]\n",
"\n",
" bar_width_adj = [0,0.1,0.4,0.27,0.45,0.55,0.63,0.75]\n",
" bt_labels_adj = [0,0.03,0.14,.08,0.13,0.17,0.17,0.21]\n",
" wt_labels_adj = [0,0.03,0.14,0.08,0.13,0.17,0.17,0.21] \n",
" sd_labels_adj = [0,0.04,0.19,0.11,0.16,0.22,0.26,0.32]\n",
" \n",
" y_labels = ['16:00','18:00','20:00','22:00','00:00','02:00', '04:00', '06:00', '08:00', '10:00','12:00','14:00','16:00']\n",
" ytickss= [-8,-6,-4,-2,0,2,4,6,8,10,12,14,16]\n",
"\n",
" fig = plt.figure(figsize=(10,7.5))\n",
" ax = plt.subplot(111)\n",
"\n",
" ax.spines['top'].set_visible(False)\n",
" ax.spines['right'].set_visible(False)\n",
"\n",
" plt.ylim(-6,16)\n",
" plt.xticks(rotation=0, fontsize=14)\n",
" plt.yticks(ticks = ytickss,labels=y_labels,fontsize=14)\n",
"# plt.title('Sleep Consistency from {} to {}'.format(date_labels[0],date_labels[-1]), pad=30,fontsize=18)\n",
" # plt.xlabel('Date', labelpad=20, fontsize=15, loc='center')\n",
" # plt.ylabel('Time', labelpad=40, fontsize=15, loc='center', rotation=0)\n",
" \n",
" if len(w_times) == 1:\n",
" ax.bar(0,0)\n",
" \n",
" if len(w_times) == 2: \n",
" ax.bar(0,0)\n",
" plt.gcf().subplots_adjust(left=0.01, right=0.35)\n",
"\n",
" upper = w_times\n",
" lower = b_times\n",
" height = [upper[i] - lower[i] for i in range(len(upper))]\n",
"\n",
" ax.bar(fd_labels, height, bottom=lower,color='mediumslateblue', width=bar_width_adj[num_days_shown],align='center')\n",
"\n",
" # print(num_of_days_to_show)\n",
" for i in range(num_days_shown):\n",
" ax.annotate(bt_labels[i],xy=(i-bt_labels_adj[num_days_shown],lower[i] - bt_labels_h[num_days_shown]),fontsize=14)\n",
" ax.annotate(wt_labels[i], xy= (i-wt_labels_adj[num_days_shown], upper[i] + wt_labels_h[num_days_shown]),fontsize=14)\n",
" ax.annotate(sd_labels[-(num_days_shown - i)], xy = (i-sd_labels_adj[num_days_shown], (upper[i]- height[i])+ sd_labels_h[num_days_shown]), fontsize=14)\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x540 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_sleep_cons(w_times=final_w_times, b_times=final_b_times, \n",
" wt_labels=wake_time_labels, bt_labels=bed_time_labels, \n",
" sd_labels=sleep_dur_labels, fd_labels=final_date_labels)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# <u>Calculating Weekly Consistency + SDD <u>"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"def weekly_sleep_consistency(bed_time_list:list,wake_time_list:list):\n",
" import numpy as np \n",
" \n",
" bt_mean = np.mean(bed_time_list)\n",
" wt_mean = np.mean(wake_time_list)\n",
" \n",
" bt_sub_mean = [] \n",
" wt_sub_mean = []\n",
" \n",
" assert len(bed_time_list) == len(wake_time_list), f\" length of bed time list {len(bed_time_list)} not the same as wake time list {len(wake_time_list)}\"\n",
" \n",
" for i in range(len(bed_time_list)):\n",
" bt_sub_mean.append(abs(bed_time_list[i] - bt_mean))\n",
" wt_sub_mean.append(abs(wake_time_list[i] - wt_mean))\n",
" \n",
" avg_bt_variability = np.mean(bt_sub_mean)/bt_mean\n",
" avg_wt_variability = np.mean(wt_sub_mean)/wt_mean\n",
" \n",
" weekly_sleep_consistency = 100 - ((avg_bt_variability+avg_wt_variability)*100)*5\n",
" \n",
" return round(weekly_sleep_consistency,1)\n",
"\n",
"def weekly_SDD(recommended_sleep_duration:int,weeks_sleep:list):\n",
" \n",
" total_weeks_sleep_hours = sum(weeks_sleep)\n",
" total_weeks_sleep_mins = total_weeks_sleep_hours*60\n",
" penalisation_factor = 1.5\n",
" \n",
" recommended_sleep_duration_mins = recommended_sleep_duration*60 \n",
" recommended_sleep_duration_hours = recommended_sleep_duration\n",
" \n",
" sleep_debt_mins = total_weeks_sleep_mins-recommended_sleep_duration_mins\n",
" sleep_debt_hours = total_weeks_sleep_hours - recommended_sleep_duration_hours\n",
" \n",
" \n",
" assert len(weeks_sleep) == 7, f\"Not calculating the last 7 days inclusive but{len(weeks_sleep)}\"\n",
" \n",
" if 0 <=total_weeks_sleep_mins<=3360:\n",
" \n",
" weekly_SDD_score = ((total_weeks_sleep_mins/3360)*100) + (sleep_debt_hours*penalisation_factor)\n",
" \n",
" return round(weekly_SDD_score,1)\n",
" \n",
" elif 3360 < total_weeks_sleep_mins <= 5726:\n",
" \n",
" weekly_SDD_score = 200 - ((total_weeks_sleep_mins/3360)*100) - (sleep_debt_hours*penalisation_factor)\n",
" return round(weekly_SDD_score,2)\n",
" \n",
" else:\n",
" return round(0,1)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"def weekly_sleep_table(sleep_data):\n",
" \n",
" if len(sleep_data_adj_len) < 7:\n",
" print('Not Enough Data')\n",
" \n",
" return None\n",
" \n",
" else:\n",
" past7_days_Sleep_cons = weekly_sleep_consistency(bed_time_list=convert_time(sleep_data['BT'].values[-7:]), \n",
" wake_time_list=convert_time(sleep_data['WT'].values[-7:]))\n",
"\n",
"\n",
" past7_days_SDD = weekly_SDD(recommended_sleep_duration=56, \n",
" weeks_sleep=sleep_data['Sleep Duration Hrs'][-7:].values)\n",
"\n",
" print('Past 7 Days Sleep Consistency = {}'.format(past7_days_Sleep_cons), '\\n')\n",
" print('Past 7 Days SDD = {}'.format(past7_days_SDD), '\\n')\n",
" print('Past 7 Days Sleep Score = {}'.format(round(past7_days_Sleep_cons*0.3 + past7_days_SDD*0.7),1), '\\n')\n",
"\n",
" weekly_sleep_data = pd.DataFrame()\n",
" weekly_sleep_data['Week Dates'] = [i for i in range(len(sleep_data)//7)]\n",
" weekly_sleep_data['Weekly Sleep Consistency'] = [i for i in range(len(sleep_data)//7)]\n",
" weekly_sleep_data['Weekly SDD'] = [i for i in range(len(sleep_data)//7)]\n",
" weekly_sleep_data['Weekly Sleep Debt(Hrs)'] = [i for i in range(len(sleep_data)//7)]\n",
" weekly_sleep_data['Weekly Sleep Score'] = [i for i in range(len(sleep_data)//7)]\n",
"\n",
" if len(sleep_data)%7 ==0:\n",
" print('Full Week')\n",
"\n",
" for i in range(len(sleep_data)//7):\n",
" weekly_sleep_data['Week Dates'].iloc[i] = str(sleep_data['Date'].values[0+(i*7)]) + ' to '+ str(sleep_data['Date'].values[6+(i*7)])\n",
" weekly_sleep_data['Weekly Sleep Consistency'].iloc[i] = weekly_sleep_consistency(bed_time_list=convert_time(sleep_data['BT'][0+(i*7):7+(i*7)].values), \n",
" wake_time_list=convert_time(sleep_data['WT'][0+(i*7):7+(i*7)].values))\n",
" weekly_sleep_data['Weekly SDD'].iloc[i] = weekly_SDD(recommended_sleep_duration=56, weeks_sleep=sleep_data['Sleep Duration Hrs'][0+(i*7):7+(i*7)].values)\n",
" weekly_sleep_data['Weekly Sleep Debt(Hrs)'].iloc[i] = sum(sleep_data['Sleep Duration Hrs'][0+(i*7):7+(i*7)].values) - 56\n",
" weekly_sleep_data['Weekly Sleep Score'].iloc[i] = round(weekly_sleep_data['Weekly Sleep Consistency'][i]*0.3 + weekly_sleep_data['Weekly SDD'][i]*0.7,1)\n",
" return weekly_sleep_data \n",
"\n",
" else:\n",
" print('Do not have full {} weeks data, can only display {} weeks data'.format(int(len(sleep_data)/7+1),len(sleep_data)//7))\n",
"\n",
" for i in range(len(sleep_data)//7):\n",
" weekly_sleep_data['Week Dates'] = [str(sleep_data['Date'].values[0+(i*7)]) + ' to '+ str(sleep_data['Date'].values[6+(i*7)])]\n",
" weekly_sleep_data['Weekly Sleep Consistency'] = weekly_sleep_consistency(bed_time_list=convert_time(sleep_data['BT'][0+(i*7):7+(i*7)].values), wake_time_list=convert_time(sleep_data['WT'][0+(i*7):7+(i*7)].values))\n",
" weekly_sleep_data['Weekly SDD'] = weekly_SDD(recommended_sleep_duration=56, weeks_sleep=sleep_data['Sleep Duration Hrs'][0+(i*7):7+(i*7)].values)\n",
" weekly_sleep_data['Weekly Sleep Debt(Hrs)'] = sum(sleep_data['Sleep Duration Hrs'][0+(i*7):7+(i*7)].values) - 56\n",
" weekly_sleep_data['Weekly Sleep Score'] = round(weekly_sleep_data['Weekly Sleep Consistency'][i]*0.3 + weekly_sleep_data['Weekly SDD'][i]*0.7,1)\n",
" \n",
" return weekly_sleep_data "
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Not Enough Data\n"
]
}
],
"source": [
"weekly_sleep_table(sleep_data_adj_len)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exercise data"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"def count_consec_ex_mins(listrand:list, consec_mins:int, print_txt= False):\n",
" \n",
" count=1\n",
" consec_list=[]\n",
" \n",
" #Count consecutives\n",
" for i in range(len(listrand[:-1])):\n",
" if listrand[i]+1 == listrand[i+1]:\n",
" count+=1\n",
" else:\n",
" consec_list.append(count)\n",
" count=1\n",
"\n",
" # Account for the last iteration\n",
" consec_list.append(count) \n",
" \n",
" final_lst = []\n",
" \n",
" for i in range(len(consec_list)):\n",
" if consec_list[i] > consec_mins:\n",
" final_lst.append(consec_list[i])\n",
" else:\n",
" continue\n",
" \n",
" if print_txt == True:\n",
" print(final_lst)\n",
" \n",
" return sum(final_lst)\n",
"\n",
"def daily_ex_score(vig_mins:int, mod_mins:int):\n",
" w1 = 3.72093023\n",
" w2 = 0.93023256\n",
" ex_mins = (vig_mins*2) + mod_mins\n",
" \n",
" if 0<=ex_mins<=21.5:\n",
" ex_score = ex_mins*w1\n",
" return round(ex_score,1)\n",
"\n",
" elif 21.5<ex_mins<=43:\n",
" ex_score = 80 + (ex_mins-21.5)*w2\n",
" return round(ex_score, 1)\n",
"\n",
" else:\n",
" return 100 \n",
"\n",
"def weekly_ex_score(vig_mins:int, mod_mins:int):\n",
" \n",
" ex_mins =(vig_mins*2)+mod_mins\n",
" w1 = 0.53333333\n",
" w2 = 0.13333333\n",
" \n",
" if 0<=ex_mins<=150:\n",
" ex_score = ex_mins*w1\n",
" return round(ex_score,1)\n",
"\n",
" elif 150<ex_mins<=300:\n",
" ex_score = 80 + (ex_mins-150)*w2\n",
" return round(ex_score, 1)\n",
"\n",
" else:\n",
" return 100"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"#Calculate Age\n",
"age_data = pd.read_csv(age_csv)\n",
"bday = datetime.strptime(age_data['birthday'][0], '%Y-%m')\n",
"today = date.today()\n",
"age = int(str(today)[0:4]) - int(str(bday)[0:4])\n",
"\n",
"#Calculate maximal HR and Vig and Mod HR thresholds\n",
"#Mod = 64% and Vig = 77% based on https://www.cdc.gov/physicalactivity/basics/measuring/heartrate.htm\n",
"\n",
"maximal_hr = 220-age\n",
"mod_thresh = int(maximal_hr*0.6)\n",
"vig_thresh = int(maximal_hr*0.75)\n",
"\n",
"#Load in Exercise data \n",
"ex_data_all = pd.read_csv(ex_csv)\n",
"dates = sorted(list(set(ex_data_all['date'])))[:]\n",
"# wake_time_int = [int(i.strftime('%H%M')) for i in ex_df['Wake Time'].values]\n",
"# w_times_bhr = [(int(str(i)[0:2])*60) + (int(str(i)[3:5])) for i in ex_df['Wake Time'].values]\n",
"\n",
"ex_df = pd.DataFrame()\n",
"ex_df['Date'] = sleep_data_adj_len['Date'].values\n",
"ex_df['Bed Time'] = sleep_data_adj_len['BT'].values\n",
"ex_df['Wake Time'] = sleep_data_adj_len['WT'].values\n",
"\n",
"total_ex_mins = [] \n",
"vig_ex_mins = [] \n",
"mod_ex_mins = [] \n",
"\n",
"#Filtering ex data and calculating consecutive mins above certain thresholds\n",
"consec_mins = 5\n",
"print_txt = False\n",
"for i in range(len(ex_df)):\n",
"# total_ex_mins.append(count_consec_ex_mins([i for i in ex_data_all[(ex_data_all['date'] == dates[i]) & (ex_data_all['heartRate'] >= mod_thresh)].index],consec_mins))\n",
" mod_ex_mins.append(count_consec_ex_mins([i for i in ex_data_all[(ex_data_all['date'] == dates[i]) & (ex_data_all['heartRate'] >= mod_thresh) & (vig_thresh >= ex_data_all['heartRate'])].index],consec_mins))\n",
" vig_ex_mins.append(count_consec_ex_mins([i for i in ex_data_all[(ex_data_all['date'] == dates[i]) & (ex_data_all['heartRate'] >= vig_thresh)].index],consec_mins))\n",
" total_ex_mins.append(count_consec_ex_mins([i for i in ex_data_all[(ex_data_all['date'] == dates[i]) & (ex_data_all['heartRate'] >= mod_thresh) & (vig_thresh >= ex_data_all['heartRate'])].index],consec_mins) + count_consec_ex_mins([i for i in ex_data_all[(ex_data_all['date'] == dates[i]) & (ex_data_all['heartRate'] >= vig_thresh)].index],consec_mins))\n",
"##Can use the following code to double check number of consecutive minutes being found per date \n",
"# print(dates[2])\n",
"# count_consec_ex_mins([i for i in ex_data_all[(ex_data_all['date'] == dates[2]) & (ex_data_all['heartRate'] >= mod_thresh)].index],consec_mins, print_txt=True)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/tomaszkostuch/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" iloc._setitem_with_indexer(indexer, value)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Bed Time</th>\n",
" <th>Wake Time</th>\n",
" <th>Exercise Mins</th>\n",
" <th>Moderate Ex Mins</th>\n",
" <th>Vig Ex Mins</th>\n",
" <th>Daily Ex Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>14/11/2021</td>\n",
" <td>07:27:00</td>\n",
" <td>14:04:00</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>29.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>15/11/2021</td>\n",
" <td>21:35:00</td>\n",
" <td>07:05:00</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>16/11/2021</td>\n",
" <td>22:40:00</td>\n",
" <td>06:36:00</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>22.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>17/11/2021</td>\n",
" <td>22:12:00</td>\n",
" <td>06:31:00</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>33.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Bed Time Wake Time Exercise Mins Moderate Ex Mins \\\n",
"0 14/11/2021 07:27:00 14:04:00 8 8 \n",
"1 15/11/2021 21:35:00 07:05:00 7 7 \n",
"2 16/11/2021 22:40:00 06:36:00 6 6 \n",
"3 17/11/2021 22:12:00 06:31:00 9 9 \n",
"\n",
" Vig Ex Mins Daily Ex Score \n",
"0 0 29.8 \n",
"1 0 26 \n",
"2 0 22.3 \n",
"3 0 33.5 "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Creating Ex Table\n",
"ex_df['Exercise Mins'] = total_ex_mins \n",
"ex_df['Moderate Ex Mins'] = mod_ex_mins\n",
"ex_df['Vig Ex Mins'] = vig_ex_mins\n",
"ex_df['Daily Ex Score'] = ''\n",
"\n",
"for i in range(len(ex_df)):\n",
" ex_df['Daily Ex Score'].iloc[i] = daily_ex_score(vig_mins=ex_df['Vig Ex Mins'].values[i], mod_mins=ex_df['Moderate Ex Mins'].values[i])\n",
"\n",
"ex_df"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"def weekly_ex_table(ex_df):\n",
" \n",
" if len(ex_df) < 7:\n",
" print('Not Enough Data')\n",
" return None\n",
" \n",
" else:\n",
" \n",
" weekly_ex_data = pd.DataFrame()\n",
" weekly_ex_data['Week'] = [i for i in range(len(ex_df)//7)]\n",
" weekly_ex_data['Total Ex Mins'] = [i for i in range(len(ex_df)//7)]\n",
" weekly_ex_data['Total Vig Mins'] = [i for i in range(len(ex_df)//7)]\n",
" weekly_ex_data['Total Mod Mins'] = [i for i in range(len(ex_df)//7)]\n",
" weekly_ex_data['Weekly Exercise Score'] = [i for i in range(len(ex_df)//7)]\n",
" weekly_ex_data['Average Exercise Mins per Day'] = [i for i in range(len(ex_df)//7)]\n",
"\n",
" if len(ex_df)%7 == 0:\n",
" print('Full {} Weeks Data'.format(int(len(ex_df)/7)))\n",
" for i in range(len(ex_df)//7):\n",
" weekly_ex_data['Week'].iloc[i]= str(ex_df['Date'].values[0+(i*7)]) + ' to '+ str(ex_df['Date'].values[0+(i*7)])\n",
" weekly_ex_data['Total Ex Mins'].iloc[i] = sum(ex_df['Exercise Mins'][0+(i*7):7+(i*7)])\n",
" weekly_ex_data['Total Vig Mins'].iloc[i] = sum(ex_df['Vig Ex Mins'][0+(i*7):7+(i*7)])\n",
" weekly_ex_data['Total Mod Mins'].iloc[i] = sum(ex_df['Moderate Ex Mins'][0+(i*7):7+(i*7)])\n",
" weekly_ex_data['Weekly Exercise Score'].iloc[i] = weekly_ex_score(vig_mins = weekly_ex_data['Total Vig Mins'].values[i], mod_mins=weekly_ex_data['Total Mod Mins'].values[i])\n",
" weekly_ex_data['Average Exercise Mins per Day'].iloc[i] = round(weekly_ex_data['Total Ex Mins'][i]/7,1)\n",
"\n",
" return weekly_ex_data \n",
"\n",
" else:\n",
" print('Do not have full {} weeks data, can only display {} weeks data'.format(int(len(ex_df)/7+1),len(ex_df)//7))\n",
"\n",
" for i in range(len(ex_df)//7):\n",
" weekly_ex_data['Week'].iloc[i] = str(ex_df['Date'].values[0+(i*7)]) + ' to '+ str(ex_df['Date'].values[6+(i*7)])\n",
" weekly_ex_data['Total Ex Mins'].iloc[i] = sum(ex_df['Exercise Mins'][0+(i*7):7+(i*7)])\n",
" weekly_ex_data['Total Vig Mins'].iloc[i] = sum(ex_df['Vig Ex Mins'][0+(i*7):7+(i*7)])\n",
" weekly_ex_data['Total Mod Mins'].iloc[i] = sum(ex_df['Moderate Ex Mins'][0+(i*7):7+(i*7)])\n",
" weekly_ex_data['Weekly Exercise Score'].iloc[i] = weekly_ex_score(vig_mins = weekly_ex_data['Total Vig Mins'].values[i], mod_mins=weekly_ex_data['Total Mod Mins'].values[i])\n",
" weekly_ex_data['Average Exercise Mins per Day'].iloc[i] = round(weekly_ex_data['Total Ex Mins'][i]/7,1)\n",
"\n",
"\n",
" return weekly_ex_data "
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Not Enough Data\n"
]
}
],
"source": [
"weekly_ex_table(ex_df)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>BT</th>\n",
" <th>WT</th>\n",
" <th>Sleep Duration Mins</th>\n",
" <th>Sleep Duration Hrs</th>\n",
" <th>Daily Sleep Debt</th>\n",
" <th>Daily Sleep Consistency</th>\n",
" <th>Daily SDD</th>\n",
" <th>Daily Sleep Score</th>\n",
" <th>Exercise Mins</th>\n",
" <th>Moderate Ex Mins</th>\n",
" <th>Vig Ex Mins</th>\n",
" <th>Daily Ex Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>18/11/2021</td>\n",
" <td>19:30:00</td>\n",
" <td>03:06:00</td>\n",
" <td>456</td>\n",
" <td>7.61</td>\n",
" <td>0.00</td>\n",
" <td>100.0</td>\n",
" <td>95.1</td>\n",
" <td>96.57</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>15/11/2021</td>\n",
" <td>20:24:00</td>\n",
" <td>03:57:00</td>\n",
" <td>453</td>\n",
" <td>7.55</td>\n",
" <td>-0.45</td>\n",
" <td>81.0</td>\n",
" <td>93.6</td>\n",
" <td>89.82</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>16/11/2021</td>\n",
" <td>18:54:00</td>\n",
" <td>01:54:00</td>\n",
" <td>420</td>\n",
" <td>7.00</td>\n",
" <td>-1.00</td>\n",
" <td>73.0</td>\n",
" <td>85.8</td>\n",
" <td>81.96</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>17/11/2021</td>\n",
" <td>19:12:00</td>\n",
" <td>03:28:00</td>\n",
" <td>496</td>\n",
" <td>8.27</td>\n",
" <td>0.27</td>\n",
" <td>77.3</td>\n",
" <td>92.9</td>\n",
" <td>88.22</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>22.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date BT WT Sleep Duration Mins Sleep Duration Hrs \\\n",
"0 18/11/2021 19:30:00 03:06:00 456 7.61 \n",
"1 15/11/2021 20:24:00 03:57:00 453 7.55 \n",
"2 16/11/2021 18:54:00 01:54:00 420 7.00 \n",
"3 17/11/2021 19:12:00 03:28:00 496 8.27 \n",
"\n",
" Daily Sleep Debt Daily Sleep Consistency Daily SDD Daily Sleep Score \\\n",
"0 0.00 100.0 95.1 96.57 \n",
"1 -0.45 81.0 93.6 89.82 \n",
"2 -1.00 73.0 85.8 81.96 \n",
"3 0.27 77.3 92.9 88.22 \n",
"\n",
" Exercise Mins Moderate Ex Mins Vig Ex Mins Daily Ex Score \n",
"0 0 0 0 0.0 \n",
"1 0 0 0 0.0 \n",
"2 0 0 0 0.0 \n",
"3 6 6 0 22.3 "
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_daily_data_table = pd.merge(sleep_data_adj_len,\n",
" ex_df[['Exercise Mins', 'Moderate Ex Mins', 'Vig Ex Mins', 'Daily Ex Score']],\n",
" left_index=True, right_index=True)\n",
"\n",
"final_daily_data_table"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sleep Debt Positive + Negative "
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"def add_accumulated_sleep_debt(df):\n",
"\n",
" asd = [] \n",
"\n",
" df.insert(6,'Sleep Debt(Neg)', '')\n",
" df.insert(7,'Sleep Debt(Pos)', '')\n",
"\n",
" for i in range(len(df)):\n",
" asd.append(sum(df['Daily Sleep Debt'][:i+1]))\n",
"\n",
" if df['Daily Sleep Debt'][i] > 0:\n",
" df['Sleep Debt(Pos)'][i] = df['Daily Sleep Debt'][i]\n",
" df['Sleep Debt(Neg)'][i] = 0 \n",
"\n",
" else:\n",
" df['Sleep Debt(Pos)'][i] = 0 \n",
" df['Sleep Debt(Neg)'][i] = df['Daily Sleep Debt'][i]\n",
"\n",
" df.insert(8,'ASD',asd)\n",
" \n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "cannot insert Sleep Debt(Neg), already exists",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Input \u001b[0;32mIn [89]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m final_daily_data_table \u001b[38;5;241m=\u001b[39m \u001b[43madd_accumulated_sleep_debt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfinal_daily_data_table\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m final_daily_data_table\n",
"Input \u001b[0;32mIn [62]\u001b[0m, in \u001b[0;36madd_accumulated_sleep_debt\u001b[0;34m(df)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21madd_accumulated_sleep_debt\u001b[39m(df):\n\u001b[1;32m 3\u001b[0m asd \u001b[38;5;241m=\u001b[39m [] \n\u001b[0;32m----> 5\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minsert\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSleep Debt(Neg)\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6\u001b[0m df\u001b[38;5;241m.\u001b[39minsert(\u001b[38;5;241m7\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSleep Debt(Pos)\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(df)):\n",
"File \u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/frame.py:4442\u001b[0m, in \u001b[0;36mDataFrame.insert\u001b[0;34m(self, loc, column, value, allow_duplicates)\u001b[0m\n\u001b[1;32m 4436\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 4437\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot specify \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mallow_duplicates=True\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m when \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 4438\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mself.flags.allows_duplicate_labels\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is False.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 4439\u001b[0m )\n\u001b[1;32m 4440\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m allow_duplicates \u001b[38;5;129;01mand\u001b[39;00m column \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns:\n\u001b[1;32m 4441\u001b[0m \u001b[38;5;66;03m# Should this be a different kind of error??\u001b[39;00m\n\u001b[0;32m-> 4442\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot insert \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcolumn\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, already exists\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 4443\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(loc, \u001b[38;5;28mint\u001b[39m):\n\u001b[1;32m 4444\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mloc must be int\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"\u001b[0;31mValueError\u001b[0m: cannot insert Sleep Debt(Neg), already exists"
]
}
],
"source": [
"final_daily_data_table = add_accumulated_sleep_debt(final_daily_data_table)\n",
"final_daily_data_table"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Send Data? (Yes/No): yes\n"
]
}
],
"source": [
"webhook_send(final_daily_data_table)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot Exercise vs time "
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"def plot_ex(df, num_of_days_to_show:int):\n",
" \n",
" #Adjustments for graphs\n",
" bar_adj_2 = [0,0.07,0.17,0.27,0.32,0.45,0.55,0.75]\n",
" ant_adj = [0,0.0225,0.059,0.095,0.12,0.15,0.17,0.21]\n",
" optimal_adj_2 = [0,0.02,0.05,0.1,0.12,0.13,0.13,0.2] \n",
" sufficient_adj_2 = [0,0.022,0.052,0.112,0.14,0.14,0.14,0.24] \n",
" low_adj_2 = [0,0.013,0.032,0.07,0.08,0.08,0.08,0.13] \n",
" \n",
" df = df[-num_of_days_to_show:]\n",
" df.reset_index(drop=True, inplace=True)\n",
" df.insert(0,'Date Labels', final_date_labels)\n",
" \n",
" \n",
" fig, axes = plt.subplots(2,1, sharex=True,figsize=(10,10))\n",
" c_map_1 = {'Moderate Ex Mins':'seagreen', 'Vig Ex Mins':'salmon'}\n",
" \n",
" \n",
" if len(df) < 2:\n",
" df[-num_of_days_to_show:].plot(kind='line', x='Date Labels', y= 'Daily Ex Score', ax=axes[0], \n",
" marker='o', markersize=5)\n",
" else:\n",
" df[-num_of_days_to_show:-1].plot(kind='line', x='Date Labels', y= 'Daily Ex Score', ax=axes[0], \n",
" marker='o', markersize=5)\n",
"\n",
" sns.despine()\n",
"\n",
" axes[1].set_ylim(0,140)\n",
" axes[0].set_ylim(-1.5,101)\n",
" axes[1].tick_params(axis='x', labelsize=15)\n",
" axes[0].tick_params(axis='y', labelsize=15)\n",
" axes[1].tick_params(axis='y', labelsize=15)\n",
" \n",
" x = [-1]+[i for i in range(num_of_days_to_show)]+[num_of_days_to_show+1]\n",
" red_zone = [50]*(num_of_days_to_show+2)\n",
" yellow_zone= [75]*(num_of_days_to_show+2)\n",
"\n",
"\n",
" #Shading Areas behind the graph \n",
" axes[0].fill_between(x, red_zone, -1.5,\n",
" facecolor=\"orange\", # The fill color\n",
" color='red', # The outline color\n",
" alpha=0.2)\n",
"\n",
" axes[0].fill_between(x, red_zone, 75,\n",
" facecolor=\"orange\", # The fill color\n",
" color='yellow', # The outline color\n",
" alpha=0.2)\n",
"\n",
" axes[0].fill_between(x, yellow_zone, 100,\n",
" facecolor=\"orange\", # The fill color\n",
" color='green', # The outline color\n",
" alpha=0.2)\n",
"\n",
" colors = ['acquamarine', 'lime']\n",
"\n",
" le = df[-num_of_days_to_show:].plot(kind='bar', stacked='True', \n",
" x='Date Labels', y = ['Moderate Ex Mins','Vig Ex Mins'],\n",
" ax=axes[1],width=bar_adj_2[num_of_days_to_show], rot=0, color=c_map_1, xlabel='')\n",
"\n",
"\n",
" for i in range(num_of_days_to_show):\n",
" axes[1].annotate(str(df['Exercise Mins'][-num_of_days_to_show:][i]) + ' Mins',\n",
" xy=(i-ant_adj[num_of_days_to_show],df['Exercise Mins'][-num_of_days_to_show:][i]+2),fontsize=12)\n",
"\n",
" axes[0].annotate('Optimal', xy=(((axes[0].get_xlim()[0] + axes[0].get_xlim()[1])/2)-optimal_adj_2[num_of_days_to_show],95), size=12)\n",
" axes[0].annotate('Sufficient', xy=(((axes[0].get_xlim()[0] + axes[0].get_xlim()[1])/2)-sufficient_adj_2[num_of_days_to_show],62.5), size=12)\n",
" axes[0].annotate('Low', xy=(((axes[0].get_xlim()[0] + axes[0].get_xlim()[1])/2)-low_adj_2[num_of_days_to_show], 30), size=12)\n",
" axes[1].grid(axis='y', linewidth=0.07)\n",
" \n",
" return fig, axes"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"(<Figure size 720x720 with 2 Axes>,\n",
" array([<AxesSubplot:xlabel='Date Labels'>, <AxesSubplot:>], dtype=object))"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x720 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_ex(final_daily_data_table, num_days_shown)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot Sleep vs time "
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"def plot_sleep_time(df, num_of_days_to_show):\n",
" \n",
" #Graph adjustments\n",
" optimal_adj_3 = [0,-0.12,-0.09,-0.07,-0.07,0,0,0] \n",
" sufficient_adj_3 = [0,-0.119,-0.085,-0.06,-0.04,0.04,0.04,0.04] \n",
" low_adj_3 = [0,-0.125,-0.11,-0.1,-0.1,-0.08,-0.08,-0.07] \n",
" bar_adj_3 = [0,0.05,0.125,0.175,0.2,0.25,0.35,0.45]\n",
" \n",
" c_map = {'Sleep Debt(Pos)':'green', 'Sleep Debt(Neg)':'red'}\n",
"\n",
" fig, axes = plt.subplots(2,1, sharex=True,figsize=(10,10))\n",
"\n",
" df = df[-num_of_days_to_show:]\n",
" df.reset_index(drop=True, inplace=True)\n",
" df.insert(0,'Date Labels', final_date_labels)\n",
" \n",
" df[-num_of_days_to_show:].plot(kind='line', x = 'Date Labels', y = 'Daily Sleep Score', ax=axes[0],\n",
" marker='o', markersize=5, )\n",
"\n",
" df.plot.area(x='Date', y='ASD', ax=axes[1], style='-o', alpha=0.3, stacked=False)\n",
"\n",
" df[-num_of_days_to_show:].plot(kind='bar', x='Date Labels', y = ['Sleep Debt(Pos)','Sleep Debt(Neg)'], \n",
" width=bar_adj_3[num_of_days_to_show],align='center', ax=axes[1], color=c_map, stacked=True, \n",
" rot=0)\n",
"\n",
"\n",
" sns.despine()\n",
" axes[0].set_ylim(0,101)\n",
" axes[1].set_ylim(-5,5)\n",
" # axes[0].set_ylabel('Daily \\n Sleep \\n Performance', rotation=0, labelpad=30, size=13)\n",
" # axes[1].set_ylabel('Hours', rotation=0, \n",
" # labelpad=25, size=13)\n",
" # axes[1].set_xlabel('Date',labelpad=15, size=15)\n",
" axes[1].tick_params(axis='x', labelsize=14)\n",
" axes[0].tick_params(axis='y', labelsize=14)\n",
" axes[1].tick_params(axis='y', labelsize=14)\n",
" axes[1].grid(axis='y', linewidth=0.07)\n",
"\n",
" x = [-1]+[i for i in range(num_of_days_to_show)]+[num_of_days_to_show+1]\n",
" red_zone = [50]*(num_of_days_to_show+2)\n",
" yellow_zone= [75]*(num_of_days_to_show+2)\n",
"\n",
"\n",
" #Shading Areas behind the graph \n",
" axes[0].fill_between(x, red_zone, 0,\n",
" facecolor=\"orange\", # The fill color\n",
" color='red', # The outline color\n",
" alpha=0.2)\n",
"\n",
" axes[0].fill_between(x, red_zone, 75,\n",
" facecolor=\"orange\", # The fill color\n",
" color='yellow', # The outline color\n",
" alpha=0.2)\n",
"\n",
" axes[0].fill_between(x, yellow_zone, 100,\n",
" facecolor=\"orange\", # The fill color\n",
" color='green', # The outline color\n",
" alpha=0.2)\n",
"\n",
" axes[0].annotate('Optimal', xy=((axes[0].get_xlim()[0] + axes[0].get_xlim()[1]/2 -optimal_adj_3[num_of_days_to_show],97)), size=13)\n",
" axes[0].annotate('Sufficient', xy=((axes[0].get_xlim()[0] + axes[0].get_xlim()[1]/2 - sufficient_adj_3[num_of_days_to_show],62.5)), size=13)\n",
" axes[0].annotate('Low', xy=((axes[0].get_xlim()[0] + axes[0].get_xlim()[1]/2 - low_adj_3[num_of_days_to_show],30)), size=13)\n",
" \n",
" return fig, axes"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"(<Figure size 720x720 with 2 Axes>,\n",
" array([<AxesSubplot:xlabel='Date Labels'>,\n",
" <AxesSubplot:xlabel='Date Labels'>], dtype=object))"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x720 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_sleep_time(final_daily_data_table, num_days_shown)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Final Weekly Dataset"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Not Enough Data\n"
]
}
],
"source": [
"weekly_sleep = weekly_sleep_table(sleep_data_adj_len)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Not Enough Data\n"
]
}
],
"source": [
"weekly_ex = weekly_ex_table(ex_df)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"weekly_ex"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
"def final_weekly_table(weekly_ex, weekly_sleep):\n",
" \n",
" if ((weekly_ex is None) and (weekly_sleep is None)):\n",
" \n",
" print('Not Enough Data')\n",
" \n",
" return None\n",
" else:\n",
" \n",
" if len(weekly_ex) > 0:\n",
"\n",
" weekly_ex_data_final = weekly_ex.drop('Week', axis=1)\n",
" weekly_data_all = pd.merge(weekly_sleep,weekly_ex, left_index=True, right_index=True)\n",
" weekly_data_all.columns\n",
"\n",
" else:\n",
" weekly_data_all = pd.merge(weekly_sleep_data,weekly_ex_data, left_index=True, right_index=True)\n",
"\n",
" weekly_data_all.drop('Week', axis=1, inplace=True) \n",
" \n",
" return weekly_data_all\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Not Enough Data\n"
]
}
],
"source": [
"final_weekly_table(weekly_ex, weekly_sleep)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Predicting Performance "
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
"def roundTime(dt=None, roundTo=60):\n",
" \"\"\"Round a datetime object to any time lapse in seconds\n",
" dt : datetime.datetime object, default now.\n",
" roundTo : Closest number of seconds to round to, default 1 minute.\n",
" Author: Thierry Husson 2012 - Use it as you want but don't blame me.\n",
" \"\"\"\n",
" if dt == None : dt = datetime.datetime.now()\n",
" seconds = (dt.replace(tzinfo=None) - dt.min).seconds\n",
" rounding = (seconds+roundTo/2) // roundTo * roundTo\n",
" return dt + timedelta(0,rounding-seconds,-dt.microsecond)\n",
"\n",
"def create_sleep_cycle_df(df, bt_dt_form:list, wt_dt_form:list, days_to_calculate:int, avg_cycle_start_print=False):\n",
" \"\"\"\n",
" Calculates Sleep Midpoints, Cycle Starts and Sleep Chronotype. \n",
" \n",
" Returns a pandas dataframe with relvant data and the average cycle start time. \n",
" \n",
" \"\"\"\n",
" from statistics import mode\n",
" \n",
" sleep_duration_halved = [i for i in round(df['Sleep Duration Hrs']/2,1)]\n",
"\n",
" sleep_cycle = pd.DataFrame()\n",
" sleep_midpoints = [] \n",
"\n",
" #Calculating sleep midpoints by adding half of the sleep duration to the bedtime\n",
" for i in range(len(df)):\n",
" sleep_midpoints.append(bt_dt_form[i]+ timedelta(hours=int(str(sleep_duration_halved[i]).split('.')[0]), minutes=int(int(str(sleep_duration_halved[i]).split('.')[1])*0.1*60))) \n",
"\n",
" sleep_cycle['Date'] = [i.date() for i in wt_dt_form]\n",
" sleep_cycle['Day'] = [i.strftime('%A') for i in sleep_cycle['Date']]\n",
" sleep_cycle['Sleep Midpoint'] = [i.time() for i in sleep_midpoints]\n",
" cycle_starts = [roundTime(i,roundTo=3600) for i in wt_dt_form]\n",
" sleep_cycle['Cycle Starts'] = [i.time() for i in cycle_starts]\n",
" act_cycle_starts = [i for i in wt_dt_form]\n",
" sleep_cycle['Actual Cycle Starts'] = act_cycle_starts\n",
" \n",
" #Calculating Avg Cycle Start Time \n",
" avg_cycle_start = roundTime(avg_time(sleep_cycle['Cycle Starts']),roundTo=1800)\n",
" \n",
" if avg_cycle_start_print == True: \n",
" print('Average Cycle Start Time = {}'.format(avg_cycle_start.time()),'\\n')\n",
"\n",
" #Calculating Sleep Chronotype\n",
" sleep_cycle['Sleeping Chronotype'] = ''\n",
"\n",
" three_am = datetime(6,1,2,3,0).time()\n",
" six_am = datetime(6,1,2,6,0).time()\n",
" midnight = datetime(6,1,2,0,0).time()\n",
"\n",
" sleep_chrono_type = []\n",
" \n",
" for i in range(len(sleep_midpoints)):\n",
"\n",
" if midnight > sleep_midpoints[i].time() > six_am:\n",
" sleep_chrono_type.append('Night Owl')\n",
"\n",
" elif three_am > sleep_midpoints[i].time()> midnight:\n",
" sleep_chrono_type.append('Lark')\n",
"\n",
" else:\n",
" sleep_chrono_type.append('Third Bird')\n",
"\n",
"\n",
" sleep_cycle['Sleeping Chronotype'] = sleep_chrono_type\n",
" \n",
" if len(sleep_cycle) < days_to_calculate:\n",
" print('Your Sleep Chronotype is currently being determined, ready in {} days'.format(days_to_calculate - len(sleep_cycle)))\n",
" \n",
" else:\n",
" print('Your Sleeping Type is {}'.format(mode(list(sleep_cycle['Sleeping Chronotype'].values))))\n",
" \n",
" \n",
" return sleep_cycle, avg_cycle_start\n",
"\n",
"def calculate_avg_cs_per_day(df):\n",
" \"\"\"\n",
" Calculates the average cycle starts per each day of the week.\n",
" \n",
" Returns Datagrame with results and the dictionary with the values \n",
" \"\"\"\n",
" \n",
" days_of_week = list(set(sleep_cycle_df['Day'].values))\n",
" avg_cycle_starts_per_day = {} \n",
" dict_list_cycle_starts = {} \n",
"\n",
" #Calculating the average and rounding then storeing in Dictionary\n",
" for i in range(len(days_of_week)):\n",
"\n",
" #Dict with the averages for each day \n",
" avg_cycle_starts_per_day[days_of_week[i]]= roundTime(avg_time(df[sleep_cycle_df['Day'] == days_of_week[i]]['Actual Cycle Starts']), roundTo=1800)\n",
"\n",
" #Dict with list of cycle starts for each day \n",
" dict_list_cycle_starts[days_of_week[i]] = df[sleep_cycle_df['Day'] == days_of_week[i]]['Actual Cycle Starts']\n",
"\n",
" #Creating DF\n",
" avg_cycle_per_day= pd.DataFrame.from_dict(data = {'Avg Cycle Start Time':[i.time() for i in avg_cycle_starts_per_day.values()], \n",
" 'Day':avg_cycle_starts_per_day.keys()})\n",
" return avg_cycle_per_day,avg_cycle_starts_per_day\n",
" \n",
"def calculating_hrly_perf_capacity(sleep_df, final_daily_df, sleep_cycle_df):\n",
" \"\"\"\n",
" Calculates the performance capacity values for every half hour of the day using sleep and exercise data.\n",
" \n",
" \"\"\"\n",
" \n",
" ## ThirdBird Graph preset built of someone who's sleep midpoint is 3 and sleeps total of 8 hours so cycle starts at 7 \n",
" ### Will have to shift numbers accordingly\n",
" ### e.g. if someones cycles starts at 5 and are 3rdbird then has to shift by -2\n",
" #### Amount to shift array = cycle start - 7 \n",
" ##### From 7AM to 6AM(Next Day)\n",
" lark_third_bird_hardcoded = [50,57.5,65,75,90,100,90,75,50,60,70,80,85,80,70,60,50,45,30,25,25,25,30,40]\n",
"\n",
"\n",
" ## NightOwl Graph preset built of someone who's sleep midpoint is 6 and sleeps total of 8 hours so cycle starts at 10 \n",
" ### Will have to shift numbers accordingly\n",
" ### e.g. if someones cycles starts at 7 and are nightowl then has to shift by +1\n",
" #### Amount to shift array = cycle start - 6 \n",
" #####These start from 5AM to 4AM(Next Day)\n",
" night_owl_harcoded = [25.0,25.0,30.0,45.0,50.0,60.0,70.0,80.0,85.0,80.0,70.0,60.0,50.0,75.0,90.0,100.0,90.0,75.0,65.0,57.5,50.0,40.0,30.0,25.0]\n",
"\n",
" last_7_days = sleep_df[-6:]\n",
" prdikt_perf_capacity = round((last_7_days['Daily Sleep Score'].values[-1]*0.7)+ (weekly_ex_score(mod_mins = sum(final_daily_df['Moderate Ex Mins']), vig_mins = sum(final_daily_df['Vig Ex Mins']))*0.3),1)\n",
"\n",
" print('Sleep Performance = ', last_7_days['Daily Sleep Score'].values[-1])\n",
" print('Ex Performance = ', weekly_ex_score(mod_mins = sum(final_daily_df['Moderate Ex Mins']), vig_mins = sum(final_daily_df['Vig Ex Mins'])))\n",
" print('Prdikt Perforamnce Capacity = ', prdikt_perf_capacity)\n",
"\n",
" # hrly_perf_capacity = [round((prdikt_perf_capacity*0.01*i),1) for i in lark_third_bird_hardcoded]\n",
" # hrly_perf_capacity\n",
"\n",
" lisst = list(sleep_cycle_df['Sleeping Chronotype'].values)\n",
" print('Mode =',mode(lisst))\n",
"\n",
" #Deciding which hourly performance capacity values to use based on most occuring \n",
" ##This step may need to change to when graphs are produced \n",
" ###My logic thinks if you are more than often sleeping like a lark then your circadian ryhytm will adjust to this \n",
"\n",
" if mode(lisst) == 'Lark':\n",
" hrly_perf_capacity = [round((prdikt_perf_capacity*0.01*i),1) for i in lark_third_bird_hardcoded]\n",
"\n",
" elif mode(lisst) == 'Third Bird':\n",
" hrly_perf_capacity = [round((prdikt_perf_capacity*0.01*i),1) for i in lark_third_bird_hardcoded]\n",
" else:\n",
" hrly_perf_capacity = [round((prdikt_perf_capacity*0.01*i),1) for i in night_owl_harcoded]\n",
"\n",
"\n",
" return hrly_perf_capacity\n",
"\n",
"def plot_todays_perf_curve(y, sleep_cycle_df, rows_to_change):\n",
" \n",
" \"\"\"\n",
" \n",
" Plots todays predicted performance capacity. \n",
" \n",
" \"\"\"\n",
" #More data entries we are missing the creater the error shading(purple)will become \n",
" deteoriation_factor = [0.01,0.025,0.05, 0.075,0.1,0.125,0.15, 0.175, 0.2, 0.225]\n",
" upper_bound = [round((deteoriation_factor[len(rows_to_change)]*i)+i,1) for i in hrly_perf_capacity]\n",
" lower_bound = [round(i-(deteoriation_factor[len(rows_to_change)]*i),1) for i in hrly_perf_capacity]\n",
" \n",
" x = [str((avg_time(sleep_cycle_df['Cycle Starts']) + timedelta(hours=j)).time())[:5] for j in range(len(y))]\n",
" \n",
" plt.figure(figsize=(25,15))\n",
" plt.ylim(0,100)\n",
" plt.scatter(x,y, s=150, c=y,cmap ='RdYlGn',alpha=1)\n",
" plt.xticks(size=20, rotation=45)\n",
" plt.yticks(ticks = [i for i in range(0,110,10)],labels =[i for i in range(0,110,10)], size=20)\n",
" plt.tick_params(axis='y', labelsize=25)\n",
" plt.tick_params(axis='x', labelsize=25)\n",
" plt.fill_between(x, lower_bound, upper_bound, alpha=0.3, color='Purple')\n",
" plt.title('Today\\'s Predicted Performance',size=30)\n",
" sns.despine()\n",
" plt.grid(axis='x', linewidth=0.07)\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Calculate all Sleep Midpoints and Cycle Starts and Print Sleeping Chronotype"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your Sleep Chronotype is currently being determined, ready in 10 days\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Day</th>\n",
" <th>Sleep Midpoint</th>\n",
" <th>Cycle Starts</th>\n",
" <th>Actual Cycle Starts</th>\n",
" <th>Sleeping Chronotype</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2021-11-18</td>\n",
" <td>Thursday</td>\n",
" <td>23:18:00</td>\n",
" <td>03:00:00</td>\n",
" <td>2021-11-18 03:06:00</td>\n",
" <td>Third Bird</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2021-11-15</td>\n",
" <td>Monday</td>\n",
" <td>00:12:00</td>\n",
" <td>04:00:00</td>\n",
" <td>2021-11-15 03:57:00</td>\n",
" <td>Lark</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2021-11-16</td>\n",
" <td>Tuesday</td>\n",
" <td>22:24:00</td>\n",
" <td>02:00:00</td>\n",
" <td>2021-11-16 01:54:00</td>\n",
" <td>Third Bird</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2021-11-17</td>\n",
" <td>Wednesday</td>\n",
" <td>23:18:00</td>\n",
" <td>03:00:00</td>\n",
" <td>2021-11-17 03:28:00</td>\n",
" <td>Third Bird</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Day Sleep Midpoint Cycle Starts Actual Cycle Starts \\\n",
"0 2021-11-18 Thursday 23:18:00 03:00:00 2021-11-18 03:06:00 \n",
"1 2021-11-15 Monday 00:12:00 04:00:00 2021-11-15 03:57:00 \n",
"2 2021-11-16 Tuesday 22:24:00 02:00:00 2021-11-16 01:54:00 \n",
"3 2021-11-17 Wednesday 23:18:00 03:00:00 2021-11-17 03:28:00 \n",
"\n",
" Sleeping Chronotype \n",
"0 Third Bird \n",
"1 Lark \n",
"2 Third Bird \n",
"3 Third Bird "
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sleep_cycle_df, avg_cycle_start = create_sleep_cycle_df(df=sleep_data_adj_len, bt_dt_form=bt_dt_form, wt_dt_form=wt_dt_form, days_to_calculate=14)\n",
"\n",
"#If sleep dates and days are the same this implies the sleeping pattern is very inconsistent likely due to shift work \n",
"sleep_cycle_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1b. Calculate Avg Cycle Starts based on Day of the week "
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Avg Cycle Start Time</th>\n",
" <th>Day</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>04:00:00</td>\n",
" <td>Monday</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>03:00:00</td>\n",
" <td>Thursday</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>02:00:00</td>\n",
" <td>Tuesday</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>03:30:00</td>\n",
" <td>Wednesday</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Avg Cycle Start Time Day\n",
"0 04:00:00 Monday\n",
"1 03:00:00 Thursday\n",
"2 02:00:00 Tuesday\n",
"3 03:30:00 Wednesday"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"avg_cycle_per_day, avg_cycle_starts_per_day = calculate_avg_cs_per_day(sleep_cycle_df)\n",
"avg_cycle_per_day"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Combine Hardcoded values to the Sleep + Exercise Index to get values for each our "
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>BT</th>\n",
" <th>WT</th>\n",
" <th>Sleep Duration Mins</th>\n",
" <th>Sleep Duration Hrs</th>\n",
" <th>Daily Sleep Debt</th>\n",
" <th>Sleep Debt(Neg)</th>\n",
" <th>Sleep Debt(Pos)</th>\n",
" <th>ASD</th>\n",
" <th>Daily Sleep Consistency</th>\n",
" <th>Daily SDD</th>\n",
" <th>Daily Sleep Score</th>\n",
" <th>Exercise Mins</th>\n",
" <th>Moderate Ex Mins</th>\n",
" <th>Vig Ex Mins</th>\n",
" <th>Daily Ex Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>18/11/2021</td>\n",
" <td>19:30:00</td>\n",
" <td>03:06:00</td>\n",
" <td>456</td>\n",
" <td>7.61</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0.00</td>\n",
" <td>100.0</td>\n",
" <td>95.1</td>\n",
" <td>96.57</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>15/11/2021</td>\n",
" <td>20:24:00</td>\n",
" <td>03:57:00</td>\n",
" <td>453</td>\n",
" <td>7.55</td>\n",
" <td>-0.45</td>\n",
" <td>-0.45</td>\n",
" <td>0</td>\n",
" <td>-0.45</td>\n",
" <td>81.0</td>\n",
" <td>93.6</td>\n",
" <td>89.82</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>16/11/2021</td>\n",
" <td>18:54:00</td>\n",
" <td>01:54:00</td>\n",
" <td>420</td>\n",
" <td>7.00</td>\n",
" <td>-1.00</td>\n",
" <td>-1.0</td>\n",
" <td>0</td>\n",
" <td>-1.45</td>\n",
" <td>73.0</td>\n",
" <td>85.8</td>\n",
" <td>81.96</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>17/11/2021</td>\n",
" <td>19:12:00</td>\n",
" <td>03:28:00</td>\n",
" <td>496</td>\n",
" <td>8.27</td>\n",
" <td>0.27</td>\n",
" <td>0</td>\n",
" <td>0.27</td>\n",
" <td>-1.18</td>\n",
" <td>77.3</td>\n",
" <td>92.9</td>\n",
" <td>88.22</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>22.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date BT WT Sleep Duration Mins Sleep Duration Hrs \\\n",
"0 18/11/2021 19:30:00 03:06:00 456 7.61 \n",
"1 15/11/2021 20:24:00 03:57:00 453 7.55 \n",
"2 16/11/2021 18:54:00 01:54:00 420 7.00 \n",
"3 17/11/2021 19:12:00 03:28:00 496 8.27 \n",
"\n",
" Daily Sleep Debt Sleep Debt(Neg) Sleep Debt(Pos) ASD \\\n",
"0 0.00 0.0 0 0.00 \n",
"1 -0.45 -0.45 0 -0.45 \n",
"2 -1.00 -1.0 0 -1.45 \n",
"3 0.27 0 0.27 -1.18 \n",
"\n",
" Daily Sleep Consistency Daily SDD Daily Sleep Score Exercise Mins \\\n",
"0 100.0 95.1 96.57 0 \n",
"1 81.0 93.6 89.82 0 \n",
"2 73.0 85.8 81.96 0 \n",
"3 77.3 92.9 88.22 6 \n",
"\n",
" Moderate Ex Mins Vig Ex Mins Daily Ex Score \n",
"0 0 0 0.0 \n",
"1 0 0 0.0 \n",
"2 0 0 0.0 \n",
"3 6 0 22.3 "
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_daily_data_table"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sleep Performance = 88.22\n",
"Ex Performance = 3.2\n",
"Prdikt Perforamnce Capacity = 62.7\n",
"Mode = Third Bird\n"
]
}
],
"source": [
"hrly_perf_capacity = calculating_hrly_perf_capacity(sleep_data_adj_len, final_daily_data_table, sleep_cycle_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Plotting Graph "
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1800x1080 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_todays_perf_curve(hrly_perf_capacity, sleep_cycle_df, rows_to_change)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Creating Calendar Heatmap "
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [],
"source": [
"def prep_data_for_heatmap(sleep_cycle_df,avg_cycle_starts_per_day:dict,\n",
" hrly_perf_capacity:list, calculation_time:int):\n",
" \n",
" if len(sleep_cycle_df) < calculation_time:\n",
" print('Not Enough Data')\n",
" \n",
" return None, None\n",
" else:\n",
" \n",
" #Creating Labels for Graph \n",
" next7dates = [sleep_cycle_df['Date'].iloc[-1] + timedelta(hours=24*(i+1)) for i in range(0,7)]\n",
" next7_fut_dates = [datetime.strftime(i, '%d/%m') for i in next7dates]\n",
" next7days = [i.strftime('%A') for i in next7dates]\n",
" next7_df = pd.DataFrame(data={'Days':next7days, 'Dates':next7dates})\n",
" next7_df['Cycle Start'] = ''\n",
" x_labels = [next7days[i] + '\\n' + '('+ next7_fut_dates[i]+ ')' for i in range(len(next7days))]\n",
"\n",
" #Creating new Dataframe with predicted cycle starts for the next 7 days based on the average times for previous days \n",
" for i in range(len(next7_df['Days'])):\n",
"\n",
" #If we don't have the seperate date then use the avg \n",
" if next7_df['Days'][i] in avg_cycle_starts_per_day.keys():\n",
" next7_df['Cycle Start'].iloc[i] = avg_cycle_starts_per_day[next7_df['Days'][i]]\n",
"\n",
" else:\n",
" print('{} not in data so will use avg = {}'.format(next7_df['Days'][i], avg_cycle_start))\n",
" next7_df['Cycle Start'].iloc[i] = avg_cycle_start \n",
"\n",
"\n",
" #Creating New Calendar dataframe which contains performance capcity for every half an hour\n",
" daypredict_fix = pd.DataFrame()\n",
" daypredict_fix['Hrly Capacity'] = hrly_perf_capacity\n",
" time_hours= [str((avg_cycle_start + timedelta(hours=j)).time())[:5] for j in range(len(hrly_perf_capacity))]\n",
" daypredict_fix['Hours'] = time_hours\n",
" daypredict_fix['New Hours'] = ''\n",
"\n",
" new_hours_lst = [] \n",
" for i in range(len(hrly_perf_capacity)):\n",
" new_hours_lst.append(daypredict_fix['Hours'][i][:2] + ':30')\n",
"\n",
" daypredict_fix['New Hours'] = new_hours_lst\n",
"\n",
" #Calculating performance capacity values for half hourly points between hours \n",
" perf_capac_vals= [round((hrly_perf_capacity[i] + hrly_perf_capacity[i+1])/2,1) for i in range(23)] + [round((hrly_perf_capacity[0] + hrly_perf_capacity[-1])/2,1)]\n",
" daypredict_fix['New Hours PC']= perf_capac_vals\n",
"\n",
" #Appending hourly and half hourly into one list of performance capacity values\n",
" final_pc = [] \n",
" for i in range(len(daypredict_fix)):\n",
" final_pc.append(daypredict_fix['Hrly Capacity'][i])\n",
" final_pc.append(daypredict_fix['New Hours PC'][i])\n",
"\n",
"\n",
" #Creating long list of of times for each day 48 time points for each day - 48*7 \n",
" z_list = [] \n",
" for i in range(0,7):\n",
" z_list.append([str((next7_df['Cycle Start'].values[i] + timedelta(hours=j/2)).time())[:5] for j in range(len(final_pc))])\n",
"\n",
"\n",
" cycle_starts = [roundTime(i,roundTo=3600) for i in wt_dt_form]\n",
" x = [str((cycle_starts[0] + timedelta(hours=j)).time())[:5] for j in range(len(final_pc))]\n",
" y = final_pc\n",
"\n",
" calendar_df = pd.DataFrame({'Time': x, 'Performance Capacity':y})\n",
"\n",
" #Flattening long list \n",
" x_new = list(chain.from_iterable(z_list))\n",
" y_new = final_pc * 7\n",
"\n",
" #Creating final table that graph wil lbe generated from \n",
" calendar_df = pd.DataFrame({'Time': x_new, 'Performance Capacity':y_new})\n",
" calendar_df['Date'] = list(chain.from_iterable([[next7_fut_dates[i]]*len(final_pc) for i in range(7)]))\n",
" calendar_final = calendar_df.pivot(columns='Date', index='Time', values='Performance Capacity')\n",
"\n",
" #Find average wake time and natch the cycle start on the graph to that\n",
" new_axis = [str((avg_cycle_start + timedelta(hours=j/2)).time())[:5] for j in range(len(final_pc))]\n",
" calendar_final= calendar_final.reindex(new_axis)\n",
"\n",
" return calendar_final, x_labels \n",
"\n",
"\n",
"def plot_weekly_predicted_performance(heatmap_df, next): \n",
" \n",
" #Plotting Heatmap \n",
" x_labels = [next7days[i] + '\\n' + '('+ next7_fut_dates[i]+ ')' for i in range(len(next7days))]\n",
" plt.figure(figsize=(22.5,17.5))\n",
" ax_1 = sns.heatmap(heatmap_df, cmap = 'RdYlGn', linewidths=0.01, linecolor='black', annot=False,\n",
" cbar_kws={'ticks': [0,10,20,30,40,50,60,70,80,90,100],\n",
" 'extend':'both'})\n",
"\n",
"\n",
" plt.yticks(rotation=0, size=20)\n",
"\n",
" plt.xticks(ticks = [0.5,1.5,2.5,3.5,4.5,5.5,6.5],labels = x_labels , rotation = 0, size=20)\n",
" plt.tick_params(axis='y', labelsize=22)\n",
" plt.tick_params(axis='x', labelsize=22)\n",
" plt.title('Predicted Performance Capacity Over the Next 7 Days', size=30)\n",
"\n",
" ax_1.figure.axes[-1].set_ylabel('Performance\\nCapacity', size=20, labelpad=50, rotation = 0)\n",
" cax = plt.gcf().axes[-1]\n",
" cax.tick_params(labelsize=22)\n",
" ax_1.set(xlabel=None)\n",
" ax_1.set(ylabel=None)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Not Enough Data\n"
]
}
],
"source": [
"heatmap_df, x_labels = prep_data_for_heatmap(sleep_cycle_df,avg_cycle_starts_per_day,\n",
" hrly_perf_capacity, calculation_time=7)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
"def plot_weekly_predicted_performance(heatmap_df, x_labels, calculation_time:int): \n",
" \n",
" if len(sleep_cycle_df) < calculation_time:\n",
" \n",
" print('Not Enough Data')\n",
" \n",
" else:\n",
" \n",
" #Plotting Heatmap \n",
" plt.figure(figsize=(22.5,17.5))\n",
"\n",
" ax_1 = sns.heatmap(heatmap_df, cmap = 'RdYlGn', linewidths=0.01, linecolor='black', annot=False,\n",
" cbar_kws={'ticks': [0,10,20,30,40,50,60,70,80,90,100],\n",
" 'extend':'both'})\n",
"\n",
"\n",
" plt.yticks(rotation=0, size=20)\n",
"\n",
" plt.xticks(ticks = [0.5,1.5,2.5,3.5,4.5,5.5,6.5],labels = x_labels , rotation = 0, size=20)\n",
" plt.tick_params(axis='y', labelsize=22)\n",
" plt.tick_params(axis='x', labelsize=22)\n",
" plt.title('Predicted Performance Capacity Over the Next 7 Days', size=30)\n",
"\n",
" ax_1.figure.axes[-1].set_ylabel('Performance\\nCapacity', size=20, labelpad=50, rotation = 0)\n",
" cax = plt.gcf().axes[-1]\n",
" cax.tick_params(labelsize=22)\n",
" ax_1.set(xlabel=None)\n",
" ax_1.set(ylabel=None)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Not Enough Data\n"
]
}
],
"source": [
"plot_weekly_predicted_performance(heatmap_df, x_labels, calculation_time=7)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Key Report Information"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [],
"source": [
"def generate_report_key_info(sleep_cycle_df, final_daily_data_table, hrly_perf_capacity, \n",
" num_days_shown:int, days_to_calculate:int, rows_to_change:list):\n",
"\n",
" print('Sleep Data')\n",
" print('=========================================================================================================', '\\n')\n",
"\n",
" print('Sleep Chronotype')\n",
" print('-----------------', '\\n')\n",
" \n",
" if len(sleep_cycle_df) < days_to_calculate:\n",
" print('Your Sleep Chronotype is currently being determined, ready in {} days'.format(days_to_calculate - len(sleep_cycle_df)), '\\n'*2)\n",
"\n",
" else:\n",
" print('Your Sleeping Type is {}'.format(mode(list(sleep_cycle_df['Sleeping Chronotype'].values))), '\\n'*2)\n",
"\n",
" print('Sleep Performance')\n",
" print('-----------------', '\\n')\n",
" avg_sleep_perf = np.mean(sleep_data_adj_len['Daily Sleep Score'].values)\n",
" lower_bound_sp = round(avg_sleep_perf - np.std(sleep_data_adj_len['Daily Sleep Score'].values))\n",
" upper_bound_sp = round(avg_sleep_perf + np.std(sleep_data_adj_len['Daily Sleep Score'].values))\n",
" typical_range_sp = str(lower_bound_sp) + ' - ' + str(upper_bound_sp)\n",
" \n",
" \n",
" print('Todays Daily Sleep Performance = {}'.format(round(final_daily_data_table['Daily Sleep Score'].values[-1])))\n",
" print('Sleep Performance Typical Range = {}'.format(typical_range_sp), '\\n'*2)\n",
"\n",
" \n",
"\n",
" print('Sleep Consistency')\n",
" print('-----------------', '\\n')\n",
"\n",
" print('Last 7 Days Sleep Consistency = {}'.format(weekly_sleep_consistency(bed_time_list=convert_time(final_daily_data_table['BT'].values[-7:]), \n",
" wake_time_list=convert_time(final_daily_data_table['WT'].values[-7:]))), '\\n'*2)\n",
"\n",
" print('Sleep Duration')\n",
" print('--------------', '\\n')\n",
" \n",
" avg_sleep_dur = round(np.mean(sleep_data_adj_len['Sleep Duration Hrs']),1)\n",
" avg_sleep_string_form = str(avg_sleep_dur)[0] + ' Hours ' + str(int(float(str(avg_sleep_dur)[1:])*60)) + ' Mins'\n",
" \n",
" avg_sleep_dur_past7 = round(np.mean(sleep_data_adj_len['Sleep Duration Hrs'][-7:]),1)\n",
" avg_sleep_string_form_past7 = str(avg_sleep_dur_past7)[0] + ' Hours ' + str(int(float(str(avg_sleep_dur_past7)[1:])*60)) + ' Mins'\n",
" \n",
" lower_bound = avg_sleep_dur - np.std(sleep_data_adj_len['Sleep Duration Hrs'].values)\n",
" upper_bound = avg_sleep_dur + np.std(sleep_data_adj_len['Sleep Duration Hrs'].values)\n",
" lower_bound_str = str(lower_bound)[0] + ' Hours ' + str(int(float(str(lower_bound)[1:])*60)) + ' mins'\n",
" upper_bound_str = str(upper_bound)[0] + ' Hours ' + str(int(float(str(upper_bound)[1:])*60)) + ' mins'\n",
" typical_range = lower_bound_str + ' - ' + upper_bound_str\n",
"\n",
"\n",
" \n",
" print('Avg Sleep Duration (All Sleep Data) = {}'.format(avg_sleep_string_form))\n",
" print('Avg Sleep Duration(Past {} Days) = {}'.format(len(sleep_data_adj_len['Sleep Duration Hrs'][-num_days_shown:]), avg_sleep_string_form_past7), '\\n')\n",
" \n",
" print('Over Last {} Days: '.format(num_days_shown))\n",
" print('Avg Bed Time: {}'.format(avg_time(final_daily_data_table['BT'][-num_days_shown:]).time()))\n",
" print('Avg Wake Time: {}'.format(avg_time(final_daily_data_table['WT'][-num_days_shown:]).time()), '\\n'*2)\n",
" \n",
" print('All Time: '.format(num_days_shown))\n",
" print('Avg Bed Time: {}'.format(avg_time(final_daily_data_table['BT']).time()))\n",
" print('Avg Wake Time: {}'.format(avg_time(final_daily_data_table['WT']).time()), '\\n')\n",
" \n",
" print('Last Nights Sleep Duration = {}'.format(str(final_daily_data_table['Sleep Duration Hrs'].values[-1])[0] + ' Hours ' + str(int(float(str(final_daily_data_table['Sleep Duration Hrs'].values[-1])[1:])*60)) + ' mins'))\n",
" print('Typical Sleep Duration Range = {}'.format(typical_range), '\\n')\n",
" \n",
" print('Sleep Debt')\n",
" print('------------', '\\n')\n",
" accum_SD = sum(final_daily_data_table['Daily Sleep Debt'])\n",
" asd_hrs = int(str(accum_SD).split('.')[0])\n",
" asd_mins = int(float('0.'+str(sum(final_daily_data_table['Daily Sleep Debt'])).split('.')[1])*60)\n",
" sleep_need_tonight = float(8 - accum_SD)\n",
"\n",
" print('Accumulated Sleep Debt in last {} days = {} hours {} mins'.format(len(final_daily_data_table),asd_hrs,asd_mins ), '\\n')\n",
" print('Tomorrows sleep need = {}'.format(str((int(str(sleep_need_tonight).split('.')[0])))) + ' Hours ' + str(int(float('0.' + str(sleep_need_tonight).split('.')[1])*60)) + ' Mins')\n",
" print('Bed Time to eradicate all Sleep Debt Tonight = {}'.format((avg_time(final_daily_data_table['WT']) - timedelta(hours=8-asd_hrs, minutes=-asd_mins)).time()),'\\n')\n",
" \n",
" \n",
" acc_sd_week = ((56+(accum_SD))/7)- 8\n",
" sleep_need_week = float(8-acc_sd_week)\n",
" acc_sd_week_hrs = (int(str(acc_sd_week ).split('.')[0]))\n",
" acc_sd_week_mins = int(float('0.' + str(acc_sd_week ).split('.')[1])*60)\n",
"\n",
" print('Sleep Need over the Next 7 days = {}'.format(str(int(str(sleep_need_week).split('.')[0]))) + ' Hours ' + str(round(float('0.' + str(sleep_need_week).split('.')[1])*60)) + ' Mins')\n",
" print('Bed Time across next 7 Days to eradicated Sleep Debt= {} '.format((avg_time(final_daily_data_table['WT']) - timedelta(hours=8-acc_sd_week_hrs, minutes=-acc_sd_week_mins)).time()), '\\n'*2)\n",
"\n",
" print('Exercise Data')\n",
" print('========================================================================================================', '\\n')\n",
" print('Total Moderate Ex Mins over last {} days = {} '.format(num_days_shown, sum(final_daily_data_table['Moderate Ex Mins'][-num_days_shown:])))\n",
" print('Total Vigorous Ex Mins over last {} days = {}'.format(num_days_shown, sum(final_daily_data_table['Vig Ex Mins'][-num_days_shown:])), '\\n'*2)\n",
"\n",
" print('Predicted Data')\n",
" print('========================================================================================================', '\\n')\n",
" print('Peak Predicted Perforamnce = {}'.format(round(max(hrly_perf_capacity))), '\\n'*2)\n",
" \n",
" print('Missing Data')\n",
" print('========================================================================================================', '\\n')\n",
" print('Number of data entries missing from user = {}'.format(len(rows_to_change)))\n",
" \n",
" print('Dates Missing = {} '.format([i for i in final_daily_data_table['Date'].values[rows_to_change]]))\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sleep Data\n",
"========================================================================================================= \n",
"\n",
"Sleep Chronotype\n",
"----------------- \n",
"\n",
"Your Sleep Chronotype is currently being determined, ready in 10 days \n",
"\n",
"\n",
"Sleep Performance\n",
"----------------- \n",
"\n",
"Todays Daily Sleep Performance = 88\n",
"Sleep Performance Typical Range = 84 - 94 \n",
"\n",
"\n",
"Sleep Consistency\n",
"----------------- \n",
"\n",
"Last 7 Days Sleep Consistency = 77.3 \n",
"\n",
"\n",
"Sleep Duration\n",
"-------------- \n",
"\n",
"Avg Sleep Duration (All Sleep Data) = 7 Hours 36 Mins\n",
"Avg Sleep Duration(Past 4 Days) = 7 Hours 36 Mins \n",
"\n",
"Over Last 4 Days: \n",
"Avg Bed Time: 19:30:00\n",
"Avg Wake Time: 03:06:15 \n",
"\n",
"\n",
"All Time: \n",
"Avg Bed Time: 19:30:00\n",
"Avg Wake Time: 03:06:15 \n",
"\n",
"Last Nights Sleep Duration = 8 Hours 16 mins\n",
"Typical Sleep Duration Range = 7 Hours 8 mins - 8 Hours 3 mins \n",
"\n",
"Sleep Debt\n",
"------------ \n",
"\n",
"Accumulated Sleep Debt in last 4 days = -1 hours 10 mins \n",
"\n",
"Tomorrows sleep need = 9 Hours 10 Mins\n",
"Bed Time to eradicate all Sleep Debt Tonight = 18:16:15 \n",
"\n",
"Sleep Need over the Next 7 days = 8 Hours 10 Mins\n",
"Bed Time across next 7 Days to eradicated Sleep Debt= 19:16:15 \n",
"\n",
"\n",
"Exercise Data\n",
"======================================================================================================== \n",
"\n",
"Total Moderate Ex Mins over last 4 days = 6 \n",
"Total Vigorous Ex Mins over last 4 days = 0 \n",
"\n",
"\n",
"Predicted Data\n",
"======================================================================================================== \n",
"\n",
"Peak Predicted Perforamnce = 63 \n",
"\n",
"\n",
"Missing Data\n",
"======================================================================================================== \n",
"\n",
"Number of data entries missing from user = 1\n",
"Dates Missing = ['18/11/2021'] \n"
]
}
],
"source": [
"generate_report_key_info(sleep_cycle_df, final_daily_data_table, hrly_perf_capacity, \n",
" num_days_shown, days_to_calculate=14, rows_to_change=rows_to_change)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|
import logging
import tempfile
import os
import re
import sys
import tweepy
import requests
from google.cloud import texttospeech
from requests_oauthlib import OAuth1Session
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
TEXTEMALL_BASE_DOMAIN = "staging-rest.call-em-all.com"
SUBSCRIPTIONS_API_BASE_DOMAIN = "subscriptions.bugalert.org"
SCRIPT_PATH = os.path.dirname(os.path.realpath(__file__))
os.environ['PATH'] = f"{os.environ['PATH']}:{SCRIPT_PATH}"
def main():
with open('/tmp/gcp.key', 'w') as f:
f.write(os.getenv('GCP_KEY'))
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/tmp/gcp.key'
filename = sys.argv[1]
summary, category = get_summary_and_category(filename)
url = f"https://bugalert.org/{filename.replace('md', 'html')}"
if os.getenv('TWITTER_BEARER_TOKEN'):
twitter = get_twitter_client()
tweet_summary = summary[:220] if len(summary) > 220 else summary
tweet = f"{f'{tweet_summary}...'} {url} #BugAlertNotice"
twitter.create_tweet(text=tweet)
if os.getenv('TEXT_EM_ALL_ID'):
send_telephony(summary, category, url, filename)
print("Operations complete.")
def send_telephony(summary, category, url, filename):
# Dynamic import to avoid loading up ffmpeg early
# or unnecessarily.
from pydub import AudioSegment
sess = OAuth1Session(os.getenv('TEXT_EM_ALL_ID'),
client_secret=os.getenv('TEXT_EM_ALL_SECRET'),
resource_owner_key=os.getenv('TEXT_EM_ALL_TOKEN'))
audio = generate_tts(summary)
# The response's audio_content is binary.
with tempfile.NamedTemporaryFile() as out:
# Write the response to the output file.
out.write(audio.audio_content)
intro = AudioSegment.from_mp3(SCRIPT_PATH + "/notice_introduction.mp3")
notice = AudioSegment.from_mp3(out.name)
final = intro + notice
final_filename = os.path.basename(filename) + ".mp3"
final.export(final_filename, format="mp3")
if os.getenv('TEXT_EM_ALL_ID'):
sms_file_id, phone_file_id = update_contact_list(category)
msg = f"BugAlert: {summary} {url}"
broadcast = create_sms_broadcast(msg, os.path.basename(filename), sms_file_id, sess)
print(broadcast)
audio_id = upload_audio(final_filename, sess)
broadcast = create_phone_broadcast(audio_id, final_filename, phone_file_id, sess)
print(broadcast)
def update_contact_list(category):
headers = {"Origin": "https://bugalert.org"}
payload = {"category": category,
"email": "nobody@example.com"} # email field required on API validation rules
response = requests.post(f"https://{SUBSCRIPTIONS_API_BASE_DOMAIN}/listup", headers=headers, json=payload)
response.raise_for_status()
response_dict = response.json()
return response_dict.get('sms_file_id'), response_dict.get('phone_file_id')
def generate_tts(summary):
# Instantiates a client
client = texttospeech.TextToSpeechClient()
synthesis_input = texttospeech.SynthesisInput(text=f"{summary} The notice will be played once more. {summary}")
# Build the voice request, select the language code ("en-US")
voice = texttospeech.VoiceSelectionParams(
language_code="en-US",
name="en-US-Wavenet-F"
)
# Select the type of audio file you want returned
audio_config = texttospeech.AudioConfig(
audio_encoding=texttospeech.AudioEncoding.MP3
#speaking_rate=0.88
)
# Perform the text-to-speech request on the text input with the selected
# voice parameters and audio file type
response = client.synthesize_speech(
input=synthesis_input, voice=voice, audio_config=audio_config
)
return response
def get_summary_and_category(filename):
with open(filename, 'r') as f:
notice = f.read()
pattern = "Summary: (.*)"
groups = re.search(pattern, notice)
summary = groups.group(1)
pattern = "Category: (.*)"
groups = re.search(pattern, notice)
category_verbose = groups.group(1)
category_keys = {
"Software Frameworks, Libraries, and Components": "frameworks_libs_components",
"Operating Systems": "operating_systems",
"Services & System Applications": "services_system_applications",
"End-User Applications": "end_user_applications",
"Test": "test"
}
category = category_keys[category_verbose]
print(summary)
print(category)
return summary, category
def upload_audio(filename, sess):
url = f"https://{TEXTEMALL_BASE_DOMAIN}/v1/audio/{filename}"
payload={}
files=[
('File',(filename,open(filename,'rb'),'audio/mpeg'))
]
headers = {
'Accept': 'application/json',
'Content-Type': 'audio/mpeg',
}
response = sess.post(url, headers=headers, data=payload, files=files)
return response.json()['AudioID']
def create_phone_broadcast(audioid, filename, phone_file_id, sess):
url = f"https://{TEXTEMALL_BASE_DOMAIN}/v1/broadcasts"
payload={'BroadcastName': filename, 'BroadcastType': 'Announcement', 'StartDate': '', 'CallerID': '5076688567', 'Audio': {'AudioID': audioid}, 'FileUploads': [{'FileID': phone_file_id}]}
response = sess.post(url, json=payload)
return response.json()
def create_sms_broadcast(msg, filename, sms_file_id, sess):
url = f"https://{TEXTEMALL_BASE_DOMAIN}/v1/broadcasts"
payload={'BroadcastName': filename, 'BroadcastType': 'SMS', 'StartDate': '', 'TextMessage': msg, 'FileUploads': [{'FileID': sms_file_id}]}
response = sess.post(url, json=payload)
return response.json()
def get_twitter_client():
api = tweepy.Client(
bearer_token=os.getenv('TWITTER_BEARER_TOKEN'),
consumer_key=os.getenv('TWITTER_CONSUMER_KEY'),
consumer_secret=os.getenv('TWITTER_CONSUMER_SECRET'),
access_token=os.getenv('TWITTER_ACCESS_TOKEN'),
access_token_secret=os.getenv('TWITTER_ACCESS_TOKEN_SECRET')
)
return api
if __name__ == '__main__':
main()
|
#!/usr/bin/env python3
import os
import random
import torch
import numpy as np
from faker import Faker
from loguru import logger
from transformers import GPT2LMHeadModel, GPT2Tokenizer
MODEL_NAME = os.environ.get('MODEL_NAME', 'gpt2')
if MODEL_NAME.lower() == 'gpt2':
logger.debug('***** Running basic GPT2 pretrained weights *****')
WEIGHTS_DIR = MODEL_NAME # Just use the pretrained weights on hugging faces
elif MODEL_NAME.lower() == '4chan':
# The docker container will automatically download weights to this location
logger.debug('***** Running GPT2 trained on 3.5 years of 4Chan /pol posts (WARNING: HIGHLY OFFENSIVE OUTPUTS - YOU HAVE BEEN WARNED!!!) *****')
WEIGHTS_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '../weights'))
else:
raise ValueError('Only supported models are original gpt2 or 4chan model!')
MAX_LENGTH = int(10000) # Hardcoded max length to avoid infinite loop
cities = {
'Arlington': 'Tarrant County',
'Austin': 'Travis County',
'Corpus Christi': 'Nueces County',
'Dallas': 'Collin County',
'El Paso': 'El Paso County',
'Fort Worth': 'Denton County',
'Garland': 'Collin County',
'Houston': 'Fort Bend County',
'Irving': 'Dallas County',
'Laredo': 'Webb County',
'Lubbock': 'Lubbock County',
'Plano': 'Collin County',
'San Antonio': 'Bexar County'
}
gop_members = [
'Gary VanDeaver', 'Bryan Slaton', 'Cecil Bell Jr.', 'Keith Bell', 'Cole Hefner', 'Matt Schaefer', 'Jay Dean', 'Cody Harris',
'Chris Paddie', 'Travis Clardy', 'Kyle Kacal', 'Ben Leman', 'John N. Raney', 'Steve Toth', 'Will Metcalf', 'John Cyrier', 'Ernest Bailes',
'James White', 'Terry Wilson', 'Dade Phelan', 'Mayes Middleton', 'Greg Bonnen', 'Cody Vasut', 'Brooks Landgraf', 'Tom Craddick',
'Dustin Burrows', 'John Frullo', 'Phil Stephenson', 'John T. Smithee', 'Four Price', 'Ken King', 'Candy Noble', 'Stephanie Klick',
'Jeff Cason', 'Matt Krause', 'Tony Tinderholt', 'David Cook', 'Craig Goldman', 'Giovanni Capriglione', 'Charlie Geren', 'Sam Harless',
'Dan Huberty', 'Briscoe Cain', 'Dennis Paul', 'Tom Oliverson', 'Mike Schofield'
]
firstNames = ['Hannah', 'Olivia', 'Marcia', 'Sarah', 'Tara', 'Brooke', 'Wanda', 'Andrea', 'Julie']
lastNames = ['Morgan', 'Walker', 'Lewis', 'Butler', 'Jones', 'Barnes', 'Martin', 'Wright', 'Foster']
info_location = [
'A friend saw them', 'I work at the clinic', 'I know his secretary', 'He told me at the club', 'The police report', 'His wife told me'
]
# TX IPs gathered from here: https://www.xmyip.com/ip-addresses/united--states/texas
ips = [
"15.180.224.", # San Antonio
"15.155.5.", # San Antonio
"15.153.133.", # San Antonio
"12.56.225.", # Dallas
"67.10.46." # Edinburg
]
# random element from each list
def sign_up_page():
raise NotImplementedError()
def set_random_seed(seed, n_gpu):
np.random.seed(seed)
torch.manual_seed(seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(seed)
def adjust_seq_length_to_model(length, max_sequence_length):
if length < 0 and max_sequence_length > 0:
length = max_sequence_length
elif 0 < max_sequence_length < length:
length = max_sequence_length # No generation bigger than model size
elif length < 0:
length = MAX_LENGTH # avoid infinite loop
return length
def generate_text(prompt_text: str, k=50, p=0.9, seq_length=150, seed=None, temperature=1.0, num_return_sequences=1):
""" Create a synthetic text sequence using a pretrained model. """
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = 0 if device == 'cpu' else torch.cuda.device_count()
repetition_penalty = 1.0 # Primarily used for CTRL model, so hardcoding this value
stop_token = "<EOS>"
seed = seed if seed is not None else np.random.randint(0, 1000000)
set_random_seed(seed, n_gpu)
# Initialize the model and tokenizer
model_class, tokenizer_class = (GPT2LMHeadModel, GPT2Tokenizer)
tokenizer = tokenizer_class.from_pretrained(WEIGHTS_DIR)
model = model_class.from_pretrained(WEIGHTS_DIR)
model.to(device)
seq_length = adjust_seq_length_to_model(seq_length, max_sequence_length=model.config.max_position_embeddings)
encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=True, return_tensors="pt")
encoded_prompt = encoded_prompt.to(device)
if encoded_prompt.size()[-1] == 0:
input_ids = None
else:
input_ids = encoded_prompt
output_sequences = model.generate(
input_ids=input_ids,
max_length=seq_length + len(encoded_prompt[0]),
temperature=temperature,
top_k=k,
top_p=p,
repetition_penalty=repetition_penalty,
do_sample=True,
num_return_sequences=num_return_sequences,
)
# Remove the batch dimension when returning multiple sequences
if len(output_sequences.shape) > 2:
output_sequences.squeeze_()
generated_sequences = []
for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
# print("=== GENERATED SEQUENCE {} ===".format(generated_sequence_idx + 1))
generated_sequence = generated_sequence.tolist()
# Decode text
text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
# Remove all text after the stop token
text = text[: text.find(stop_token) if stop_token else None]
# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
total_sequence = (
prompt_text + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)):]
)
generated_sequences.append(total_sequence)
# print(total_sequence)
return generated_sequences
def create_anonymous_form_batch(prompt_text='Dear Gov. Abbott,', batch_size=5):
# Used for fake name generation
fake = Faker(['en_US', 'es_MX'])
text_sequences = generate_text(prompt_text, num_return_sequences=batch_size)
form_batch = []
for i in range(batch_size):
city, county = random.choice(list(cities.items()))
form_data = {
'textarea-1': text_sequences[i],
'text-1': random.choice(info_location),
'text-6': 'Dr. ' + fake.name(),
'text-2': city,
'text-3': 'Texas',
'text-4': fake.zipcode_in_state('TX'),
'text-5': county,
'hidden-1': random.choice(ips) + str(random.randint(0, 255)),
'checkbox-1[]': 'no',
}
form_batch.append(form_data)
return form_batch
def _test_form_generator():
prompt_text = f'Dear {random.choice(gop_members)},'
form_batch = create_anonymous_form_batch(prompt_text, batch_size=3)
logger.info(form_batch)
if __name__ == "__main__":
_test_form_generator()
|
# This file is part of the ISIS IBEX application.
# Copyright (C) 2012-2020 Science & Technology Facilities Council.
# All rights reserved.
#
# This program is distributed in the hope that it will be useful.
# This program and the accompanying materials are made available under the
# terms of the Eclipse Public License v1.0 which accompanies this distribution.
# EXCEPT AS EXPRESSLY SET FORTH IN THE ECLIPSE PUBLIC LICENSE V1.0, THE PROGRAM
# AND ACCOMPANYING MATERIALS ARE PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES
# OR CONDITIONS OF ANY KIND. See the Eclipse Public License v1.0 for more details.
#
# You should have received a copy of the Eclipse Public License v1.0
# along with this program; if not, you can obtain a copy from
# https://www.eclipse.org/org/documents/epl-v10.php or
# http://opensource.org/licenses/eclipse-1.0.php
import os
import sys
sys.path.insert(0, os.path.abspath(os.getcwd()))
from argparse import ArgumentParser
from BlockServerToKafka.block_server_monitor import BlockServerMonitor
from time import sleep
from os import environ
from BlockServerToKafka.kafka_producer import ProducerWrapper
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument('-d', '--data', help='Kafka topic to send PV data to', nargs=1, type=str,
default='test_bs_forwarder')
parser.add_argument('-c', '--config', help='Kafka topic to send forwarder config to', nargs=1, type=str,
default='test_bs_forwarder_config')
parser.add_argument('-b', '--broker', help='Location of the Kafka brokers (host:port)', nargs='+', type=str,
default='sakura.isis.cclrc.ac.uk:9092')
parser.add_argument('-p', '--pvprefix', help='PV Prefix of the block server', nargs=1, type=str,
default=environ["MYPVPREFIX"])
args = parser.parse_args()
KAFKA_DATA = args.data[0]
KAFKA_CONFIG = args.config[0]
KAFKA_BROKER = args.broker
PREFIX = args.pvprefix[0]
producer = ProducerWrapper(KAFKA_BROKER, KAFKA_CONFIG, KAFKA_DATA)
monitor = BlockServerMonitor(f"{PREFIX}CS:BLOCKSERVER:BLOCKNAMES", PREFIX, producer)
while True:
sleep(0.1)
|
"""file to deal with batch operations"""
import sys
import json
import xlwt
import extract_info
from fit_sheet_wrapper import FitSheetWrapper
from xlwt import Workbook
import random
# from desk import *
import requests
import pandas as pd
import numpy as np
import nltk
import json
import csv
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn import naive_bayes
from sklearn.metrics import roc_auc_score
import pandas
""" Gets the arguments from the command line input """
SENTENCES = sys.argv[1:]
#print(SENTENCES)
wb = Workbook()
ws = FitSheetWrapper(wb.add_sheet('Sheet 1'))
style = xlwt.XFStyle()
font = xlwt.Font()
font.bold = True
style.font = font
# df = pandas.read_csv('engine/desktop_train_health2.csv')
# stopset = set(stopwords.words('english'))
# vectorizer = TfidfVectorizer(use_idf=True,lowercase=True,strip_accents='ascii',stop_words=stopset)
# y = df.common
# x = vectorizer.fit_transform(df.action)
# x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=42)
# clf=naive_bayes.MultinomialNB()
# clf.fit(x_train,y_train)
i = 1
for request in SENTENCES:
result = extract_info.main(request)
print(result)
ws.write(i, 0, "Test Case Number", style=style)
ws.write(i, 1, result['case_id'])
i += 1
ws.write(i, 0, "Test Case Type", style=style)
r = random.randint(0,1)
ws.write(i, 1, "Unique" if r==1 else "General")
i += 1
ws.write(i, 0, "Test Case Description", style=style)
ws.write(i, 1, result['action'])
i += 1
if len(result["inputs"]) > 0:
ws.write_merge(
i, i + len(result["inputs"]) - 1, 0, 0, "Expected Inputs", style=style)
for inp in result["inputs"]:
ws.write(i, 1, inp[0] + " = " + inp[1])
i += 1
else:
ws.write(i, 0, "Expected Inputs", style=style)
ws.write(i, 1, "-")
i += 1
ws.write(i, 0, "Expected Resuls", style=style)
ws.write(i, 1, result['expectation'])
i += 4
wb.save('genTestCases.xls')
print(json.dumps({"code":True}))
sys.stdout.flush()
|
import bpy
from bpy.props import *
from ... events import executionCodeChanged
from ... sockets.info import isBase, toBaseDataType
from ... base_types import AnimationNode, ListTypeSelectorSocket
class GetListElementNode(bpy.types.Node, AnimationNode):
bl_idname = "an_GetListElementNode"
bl_label = "Get List Element"
bl_width_default = 180
dynamicLabelType = "HIDDEN_ONLY"
assignedType: ListTypeSelectorSocket.newProperty(default = "Float")
clampIndex: BoolProperty(name = "Clamp Index", default = False,
description = "Clamp the index between the lowest and highest possible index",
update = executionCodeChanged)
allowNegativeIndex: BoolProperty(name = "Allow Negative Index",
description = "-2 means the second last list element",
update = executionCodeChanged, default = True)
makeCopy: BoolProperty(name = "Make Copy", default = True,
description = "Output a copy of the list element to make it independed",
update = executionCodeChanged)
useIndexList: BoolProperty(name = "Use Index List", default = False,
update = AnimationNode.refresh)
def create(self):
prop = ("assignedType", "BASE")
self.newInput(ListTypeSelectorSocket(
"List", "inList", "LIST", prop))
if not self.useIndexList:
self.newInput("Integer", "Index", "index")
else:
self.newInput("Integer List", "Indices", "indices")
self.newInput(ListTypeSelectorSocket(
"Fallback", "fallback", "BASE", prop, hide = True))
if not self.useIndexList:
self.newOutput(ListTypeSelectorSocket(
"Element", "element", "BASE", prop))
else:
self.newOutput(ListTypeSelectorSocket(
"Elements", "elements", "LIST", prop))
def draw(self, layout):
row = layout.row(align = True)
row.prop(self, "clampIndex", text = "Clamp", icon = "FULLSCREEN_EXIT")
row.prop(self, "allowNegativeIndex", text = "Wrap", icon = "LOOP_FORWARDS")
row.prop(self, "useIndexList", text = "", icon = "LINENUMBERS_ON")
def drawAdvanced(self, layout):
layout.prop(self, "makeCopy")
self.invokeSelector(layout, "DATA_TYPE", "assignListDataType",
dataTypes = "LIST", text = "Change Type", icon = "TRIA_RIGHT")
def drawLabel(self):
if not self.useIndexList:
if self.inputs["Index"].isUnlinked:
return "List[{}]".format(self.inputs["Index"].value)
return "Get List Element"
def getExecutionCode(self, required):
if self.useIndexList:
yield from self.getExecutionCode_List()
else:
yield from self.getExecutionCode_Single()
def getExecutionCode_Single(self):
yield "if len(inList) != 0: element = " + self.getGetElementCode("index", "len(inList)")
yield "else: element = fallback"
if self.makeCopy:
socket = self.outputs[0]
if socket.isCopyable():
yield "element = " + socket.getCopyExpression().replace("value", "element")
def getExecutionCode_List(self):
yield "if len(inList) != 0:"
yield " length = len(inList)"
yield " elements = self.outputs[0].getDefaultValue()"
yield " for i in indices:"
yield " elements.append({})".format(self.getGetElementCode("i", "length"))
yield "else:"
fromFallbackCode = self.sockets[0].getFromValuesCode().replace("value", "[fallback]")
yield " elements = {} * len(indices)".format(fromFallbackCode)
def getGetElementCode(self, index, length):
if self.allowNegativeIndex:
if self.clampIndex:
code = "inList[min(max({index}, -{length}), {length} - 1)]"
else:
code = "inList[{index}] if -{length} <= {index} < {length} else fallback"
else:
if self.clampIndex:
code = "inList[min(max({index}, 0), {length} - 1)]"
else:
code = "inList[{index}] if 0 <= {index} < {length} else fallback"
return code.format(index = index, length = length)
def assignListDataType(self, listDataType):
self.assignType(toBaseDataType(listDataType))
def assignType(self, baseDataType):
if not isBase(baseDataType): return
if baseDataType == self.assignedType: return
self.assignedType = baseDataType
self.refresh()
|
# Generated by Django 3.0 on 2019-12-06 23:39
import django.db.models.deletion
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('blog', '0006_auto_20191207_0015'),
]
operations = [
migrations.RemoveField(
model_name='post',
name='images',
),
migrations.RemoveField(
model_name='postimage',
name='tags',
),
migrations.AddField(
model_name='post',
name='image',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='blog.PostImage'),
),
migrations.AlterField(
model_name='post',
name='published',
field=models.DateTimeField(auto_created=True),
),
]
|
import os
from flask import Flask, session, render_template, redirect, abort, url_for, request, jsonify
from flask_session import Session
from sqlalchemy import create_engine
from sqlalchemy.orm import scoped_session, sessionmaker
from . import auth_tool
app = Flask(__name__)
app.config.from_pyfile('../config.py')
credentials = os.getenv("PSQL_CREDS")
engine = create_engine(credentials + "web50")
db = scoped_session(sessionmaker(bind=engine))
from .lib import gr_search,get_reviews,dict_proxy
"""
Template Processors
"""
@app.context_processor
def display_year_c():
from time import strftime
return dict(show_year="© 2020-" + strftime("%Y"))
@app.context_processor
def inject_user():
return dict(USERNAME=session.get('username'))
@app.context_processor
def inject_referrer():
return dict(PREV_URL=request.referrer)
"""
Template filters
"""
@app.template_filter('format_time')
def format_time(s):
return s.strftime("%x %X")
Session(app)
@app.route('/')
def index():
return render_template('index.html')
@app.route('/search', methods=["GET", "POST"])
def search():
if not session.get('user_id'):
return redirect(url_for('login'))
if request.method == "POST":
result=[]
query = request.form.get("search_string")
app.logger.info(query)
if query is not None:
try:
query = int(query)
result = db.execute("select * from books where published_year = :year",{'year':query}).fetchall()
except ValueError:
query = '%' + query + '%'
app.logger.info('titled '+query)
result = db.execute("select * from books where title ilike :query or isbn like :query or author ilike :query",
{'query':query}).fetchall()
db.commit()
return render_template("search.html", books=result)
elif len(request.args) == 1:
result=[]
query = request.args.get('search_string')
if query is not None:
query = '%' + query + '%'
# function(123)
result = db.execute("select * from books where title ilike :query or isbn ilike :query or author ilike :query",
{'query': query}).fetchall()
return render_template("search.html",books=result)
else:
return render_template('search.html')
@app.route('/book/<isbn>',methods=["GET"])
def book(isbn):
if not session.get('user_id'):
return redirect(url_for('login'))
reviews={}
data = {}
if isbn is not None:
#data = get_reviews(isbn)
result = db.execute("select * from books where isbn = :isbn", {"isbn": isbn}).fetchone()
if result is not None:
gr_avg,gr_ratings = gr_search(isbn, app.config.get('GR_API_KEY'))
#gr_avg=1;gr_ratings=1
data = {'isbn': result.isbn, 'title': result.title, 'author': result.author, 'year': result.published_year,
'review_count': result.total_reviews,
# if the average score is still set at 0.0 then we return 0 as to not get a divide by zero error.
'average_score': (result.total_review_score / result.total_reviews) if result.total_reviews > 0 else 0}
reviews = db.execute("""select reviews.id,reviews.review_score,reviews.review_text,reviews.reviewed_time,users.username
from reviews right join users on reviews.user_id = users.id where isbn = :isbn""",
{"isbn": isbn})
db.commit()
result = {}
if reviews:
reviews = dict_proxy(reviews)
data['gr_avg_rating']=gr_avg
data['gr_total_ratings']=gr_ratings
#reviews=get_reviews(isbn)
else:
abort(404)
# if it's no found redirect them to 404 page.
else:
# return redirect(url_for("error_404"),code=404)
abort(404)
app.logger.info(data)
return render_template('book.html',book_info=data,reviews=reviews)
#TODO: Make it so that the selection-y bits are their own section(the book info that is). The rest of it will be just re-populated.
#they are going to leave a review. Or they're submitting their review.
@app.route('/review/',methods=["GET","POST"])
#this is in case they want to see a specific review by someone.
@app.route('/review/id/<int:review_id>',methods=["GET"])
@app.route('/review/isbn/<isbns>',methods=["GET"])
def review():
if not session.get('user_id'):
return redirect(url_for('login'))
if request.method == "GET":
isbns=request.args.get('isbn')
review_id=request.args.get('id')
result=None
reviews = None
if review_id is None and isbns is None:
return redirect(url_for('search'))
if review_id is not None:
#result=db.execute("select * from reviews where id=:id",{'id':id});
reviews = db.execute("""select reviews.isbn,reviews.review_score,reviews.review_text,reviews.reviewed_time,users.username
from reviews right join users on reviews.user_id = users.id where reviews.id = :id""",
{"id": review_id})
else:
reviews = db.execute("""select reviews.isbn,reviews.review_score,reviews.review_text,reviews.reviewed_time,users.username from reviews right join users on reviews.user_id = users.id where users.id = :id and reviews.isbn = :isbn""",{"id": session.get('user_id'),'isbn':isbns}).fetchall()
if isbns is None:
abort(500)
app.logger.info(reviews)
data = db.execute("select * from books where isbn = :isbn", {"isbn": isbns}).fetchone()
#db.commit()
data={**data}
#data=dict(zip(data.keys(),data))
data['avg_score'] = 0 if data['total_reviews'] == 0 else round(data['total_review_score']/data['total_reviews'],2)
if reviews is not None and reviews != []:
#reviews=dict(zip(reviews.keys(),reviews))
reviews=dict_proxy(reviews)[0]
reviews['id']=review_id
#if result is not None:
# result=dict_proxy(result)
app.logger.info(reviews)
return render_template('review.html',book_info=data,reviews=reviews)
if request.method == "POST":
queried=False
#update the database with the new fields.
#db.execute("update books set total_reviews = total_reviews + 1, total_review_score = total_review_score + :review_points;")
isbn=request.form.get('isbn')
review_id=request.form.get('id')
review_text = request.form.get('review_text')
review_score = request.form.get('review_score')
username=session.get('username')
query=''
if review_id is not None:
db.execute("""UPDATE books set total_review_score = total_review_score + (select (:review_score-review_score) from reviews where id = :id) where isbn = :isbn""",
{'isbn':isbn,'review_score':review_score,'id':review_id})
result=db.execute("""UPDATE reviews set(review_score,review_text) = (:review_score,:review_text) where id = :id and user_id = :user_id returning reviewed_time""",
{'id':review_id,'review_score':review_score,'review_text':review_text,'user_id':session.get('user_id')})
db.commit()
result=dict_proxy(result)[0]
reviews={'id':review_id,'username':session.get('username'),'review_score':review_score,'review_text':review_text,'reviewed_time':result['reviewed_time']}
elif isbn is not None:
user_id=session.get('user_id')
rows=db.execute("""select isbn from reviews where user_id = :user_id and isbn = :isbn""",{'user_id':user_id,'isbn':isbn}).fetchone()
if rows:
db.execute("""UPDATE books set total_review_score = total_review_score + (select (:review_score-review_score) from reviews where id = :id) where isbn = :isbn""",{'isbn':isbn,'review_score':review_score,'id':review_id})
result = db.execute("""UPDATE reviews set(review_score,review_text) = (:review_score,review_text) where user_id = :user_id and isbn = :isbn RETURNING id,reviewed_time""",
{'review_score':review_score,'review_text':review_text,'user_id':user_id,'isbn':isbn})
#db.session.commit()
else:
result =db.execute("""INSERT INTO reviews(review_score,review_text,isbn,user_id) values(:review_score,:review_text,:isbn,:user_id) RETURNING id,reviewed_time;""",
{'review_score':review_score,'review_text':review_text,'isbn':isbn,'user_id':user_id})
db.execute("""UPDATE books SET total_reviews = total_reviews + 1,
total_review_score = total_review_score + :review_score where isbn=:isbn""",{'review_score':review_score,'isbn':isbn})
#db.session.commit()
db.commit()
result=dict_proxy(result)[0]
reviews={'id':result['id'],'username':username,'review_score':review_score,'review_text':review_text,'reviewed_time':result['reviewed_time']}
else:
reviews=None
db.commit()
app.logger.info("Query "+query)
data = db.execute("select * from books where isbn = :isbn", {"isbn": isbn}).fetchone()
#db.commit()
#data=dict_proxy(data)[0]
data=dict(zip(data.keys(),data))
data['avg_score'] = 0 if data['total_reviews'] == 0 else round(data['total_review_score']/data['total_reviews'],2)
app.logger.info(reviews)
return render_template('review.html',reviews=reviews,book_info=data)
return redirect(url_for('index'))
@app.route('/gr_review/<isbns>',methods=["GET"])
def gr_review(isbns):
output={}
gr_reviews = gr_search(isbns, app.config.get('GR_API_KEY'))
output['gr_avg_rating'] = gr_reviews[0]
output['gr_ratings'] = gr_reviews[1]
return jsonify(output)
@app.route('/reviews/<isbns>',methods=["GET","POST"])
def book_info(isbns):
if request.method == "POST":
request.form.get("isbns")
result={}
if isbns is not None:
data = db.execute("select * from books where isbn = :isbn", {"isbn": isbns})
if data is None:
return jsonify({'book': False, 'reviews': False})
else:
data = dict_proxy(data)[0]
if data['total_reviews'] != 0:
reviews=get_reviews(isbns)
else:
reviews=[]
#gr_search(isbns,app.config.get('GR_API_KEY'))
gr_avg,gr_ratings = gr_search(isbns, app.config.get('GR_API_KEY'))
#gr_avg_rating=1
#gr_total_ratings=1
data['gr_avg_rating']=gr_avg
data['gr_total_ratings']=gr_ratings
result['book']=data;result['reviews']=reviews
return jsonify(result)
else:
return jsonify({'book': False, 'reviews': False})
@app.route('/dashboard')
def dashboard():
reviews=db.execute("select reviews.*,books.title,books.author from reviews join books on reviews.isbn = books.isbn where user_id = :user_id",{'user_id':session.get('user_id')})
reviews=dict_proxy(reviews)
return render_template('dashboard.html',reviews=reviews)
@app.route('/api/<isbn>', methods=["GET", "POST"])
def get_isbn(isbn):
if request.method == "POST":
isbn=request.form.get("isbn")
# to make sure that the query doesn't open up to SQLi also there should only ever be one result.
data = db.execute("select * from books where isbn = :isbn", {"isbn": isbn}).fetchone()
if data is not None:
output = {'isbn': data.isbn, 'title': data.title, 'author': data.author, 'year': data.published_year,
'review_count': data.total_reviews,
# if the average score is still set at 0.0 then we return 0 as to not get a divide by zero error.
'average_score': (data.total_review_score / data.total_reviews) if data.total_reviews > 0 else 0}
#if data.total_reviews == 0:
gr_reviews=gr_search(isbn,app.config.get('GR_API_KEY'))
output['gr_avg_rating'] = gr_reviews[0]
output['gr_ratings'] = gr_reviews[1]
# craft our response.
response = app.make_response(output)
# make sure it's stated that it's pure JSON.
response.mimetype = "application/json"
# return that data.
return response
# if it's no found redirect them to 404 page.
else:
# return redirect(url_for("error_404"),code=404)
abort(404)
@app.route('/404')
def error_404():
return "Page not found"
@app.route('/test')
def test():
return str(current_app.config['PEPPER'])
@app.route('/login', methods=["GET", "POST"])
def login():
# see what type of request it is.
if request.method == "GET":
# if it's a get request just return the form like normal.
return render_template('login.html')
else:
# get the static pepper that's used for the entire thing and never ever changes.
pepper = app.config['PEPPER']
# get the username from the form.
username = request.form.get('username')
# the password.
password = request.form.get('password')
# modify the password mobilzed(e.g. first char is letter uppercase it otherwise keep it the same)
mobile_password = password[0].upper() + password[1:]
# only get a single result there should only ever be one. Also not selecting all as we dont' need all data.
result = db.execute('Select password,mobile_password,id from users where username = :username',
{'username': username}).fetchone()
db.commit()
# if it's none then they don't exist.
if result is not None:
# check their normal password.
pass_good = auth_tool.verify_password(password, pepper, result.password)
# if it's passed
if not pass_good:
# see if the mobile version of teh password works.
pass_good = auth_tool.verify_password(mobile_password, pepper, result.mobile_password)
# if that's OK then they have a good password.
if pass_good:
session['username']=username
session['user_id']=result.id
return redirect(url_for('index'))
else:
return render_template('login.html',msg="The password or username your entered is either incorrect or the user does not exist.")
else:
return render_template("login.html", msg="The password or username your entered is either incorrect or the user does not exist.")
@app.route('/register', methods=["GET", "POST"])
def register():
if request.method == "POST":
pepper = app.config['PEPPER']
username = request.form.get('username')
if username is None:
return render_template('register.html', msg="No username specified")
if db.execute('select id from users where username=:username', {'username': username}).fetchone() is not None:
return render_template('register.html', msg="Sorry someone is already registered with that username")
else:
password = request.form.get('password')
password_confirm = request.form.get('password_confirm')
if password is None :
return render_template("register.html", msg="No password specified.")
elif password_confirm is None:
return render_template("register.html", msg="You didn't specify a verification password.")
elif password_confirm != password:
return render_template("register.html", msg="Your verification password didn't match your password.")
mobile_password = password[0].upper() + password[1:]
password = auth_tool.hash_password(password, pepper)
mobile_password = auth_tool.hash_password(mobile_password, pepper)
db.execute(
'INSERT INTO USERS(username,password,mobile_password) VALUES (:username,:password,:mobile_password)',
{'username': username, 'password': password, 'mobile_password': mobile_password})
db.commit()
return redirect(url_for("index"))
else:
return render_template('register.html')
@app.route('/logout')
def logout():
session.clear()
return redirect(url_for('index'))
#pass
@app.route('/browse')
def browse():
books1 = db.execute("SELECT * from books where total_reviews >= 1 order by title").fetchall()
return render_template('books.html', books=books1)
@app.route('/logged_in')
def logged_in():
if 'username' in session:
return "logged in as %s" % session['username']
return 'you are not logged in'
if __name__ == '__main__':
app.run() |
from sqlalchemy import func as sql_func, Date
from cebulany.models import db
def get_year_month_col(column: Date):
database_type = db.engine.dialect.name
if database_type == 'postgresql':
return sql_func.to_char(column, 'YYYY-MM')
if database_type == 'sqlite':
return sql_func.strftime('%Y-%m', column)
raise AttributeError(f'Unknown database type: {database_type}')
def get_year_col(column: Date):
database_type = db.engine.dialect.name
if database_type == 'postgresql':
return sql_func.to_char(column, 'YYYY')
if database_type == 'sqlite':
return sql_func.strftime('%Y', column)
raise AttributeError(f'Unknown database type: {database_type}')
|
from numpy import *
# Download datafile
import urllib
urllib.urlretrieve('http://www.ai-geostats.org/fileadmin/Documents/Data/walker_01.dat',filename='walker_01.dat')
# Whhether to thin dataset; definitely thin it if you're running this example on your laptop!
thin = False
l = file('walker_01.dat').read().splitlines()[8:-1]
a = array([fromstring(line,sep='\t') for line in l])
if thin:
a=a[::5]
ident,x,y,v,u,t=a.T
mesh = vstack((x,y)).T |
"""A setup script to demonstrate build using bcrypt"""
#
# Run the build process by running the command 'python setup.py build'
#
# If everything works well you should find a subdirectory in the build
# subdirectory that contains the files needed to run the script without Python
from cx_Freeze import setup, Executable
setup(name="test_bcrypt",
version="0.2",
description="cx_Freeze script to test bcrypt",
executables=[Executable("test_bcrypt.py")],
options={"build_exe": {"excludes": ["tkinter"],
"zip_include_packages": ["*"],
"zip_exclude_packages": []}})
|
from __future__ import unicode_literals
from django.core.management.base import BaseCommand
from ...utils import update_sentry_404s
class Command(BaseCommand):
def handle(self, *args, **kwargs):
update_sentry_404s()
|
def arithmatic_mean(list):
length_of_list = len(list)
summation = sum(list)
return summation / length_of_list
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
mean = arithmatic_mean(x)
print('Mean of', x,'is',mean) |
"""Cycles"""
from .rankinebasic import RankineBasic
|
# Title: 전깃줄
# Link: https://www.acmicpc.net/problem/2565
import sys
from bisect import bisect_left
sys.setrecursionlimit(10 ** 6)
read_list_int = lambda: list(map(int, sys.stdin.readline().strip().split(' ')))
read_single_int = lambda: int(sys.stdin.readline().strip())
def solution(n: int, lines: list):
lines = sorted(lines, key=lambda l: l[0])
bs = list(map(lambda l: l[1], lines))
d = [0]
for b in bs:
i = bisect_left(d, b)
if len(d) == i:
d.append(b)
else:
d[i] = b
return n - (len(d)-1)
def main():
n = read_single_int()
lines = []
for _ in range(n):
lines.append(read_list_int())
print(solution(n, lines))
if __name__ == '__main__':
main() |
#============================================
__author__ = "Sachin Mehta"
__maintainer__ = "Sachin Mehta"
#============================================
import torch
from torch import nn
from nn_layers.cnn_utils import CBR, CB
class Shuffle(nn.Module):
'''
This class implements Channel Shuffling
'''
def __init__(self, groups):
'''
:param groups: # of groups for shuffling
'''
super().__init__()
self.groups = groups
def forward(self, x):
batchsize, num_channels, height, width = x.data.size()
channels_per_group = num_channels // self.groups
x = x.view(batchsize, self.groups, channels_per_group, height, width)
x = torch.transpose(x, 1, 2).contiguous()
x = x.view(batchsize, -1, height, width)
return x
class DWSepConv(nn.Module):
'''
This class implements the volume-wise seperable convolutions
'''
def __init__(self, channel_in, channel_out, kernel_size=3, stride=1, dilation=1):
super().__init__()
self.dwise_layer = nn.Sequential(
CB(channel_in, channel_in, kernel_size, stride=stride, dilation=dilation, groups=channel_in),
CBR(channel_in, channel_out, 1, 1, groups=1)
)
self.channel_in = channel_in
self.channel_out = channel_out
self.ksize=kernel_size
self.dilation = dilation
def forward(self, x):
return self.dwise_layer(x)
def __repr__(self):
s = '{name}(in_channels={channel_in}, out_channels={channel_out}, kernel_size={ksize}, dilation={dilation})'
return s.format(name=self.__class__.__name__, **self.__dict__)
class StridedDWise(nn.Module):
def __init__(self, channel_in, kernel_size=3, dilation=1):
super().__init__()
self.pool_layer = CBR(channel_in, channel_in, 3, stride=2, groups=channel_in)
self.dwise_layer = DWSepConv(channel_in, channel_in, kernel_size=kernel_size, dilation=dilation)
self.channel_in = channel_in
self.channel_out = 2*channel_in
self.ksize = kernel_size
def forward(self, x):
x = self.pool_layer(x)
return torch.cat([x, self.dwise_layer(x)], 1)
def __repr__(self):
s = '{name}(in_channels={channel_in}, out_channels={channel_out}, kernel_size={ksize})'
return s.format(name=self.__class__.__name__, **self.__dict__)
|
class Phase(object):
"""The Phase class represents a phase a task may be in.
It has no function other than to act as an anchor in the task graph.
All phases are instantiated in common.phases
"""
def __init__(self, name, description):
# The name of the phase
self.name = name
# The description of the phase (currently not used anywhere)
self.description = description
def pos(self):
"""Gets the position of the phase
:return: The positional index of the phase in relation to the other phases
:rtype: int
"""
from bootstrapvz.common.phases import order
return next(i for i, phase in enumerate(order) if phase is self)
def __cmp__(self, other):
"""Compares the phase order in relation to the other phases
:return int:
"""
return self.pos() - other.pos()
def __str__(self):
"""
:return: String representation of the phase
:rtype: str
"""
return self.name
|
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import json
import warnings
import pulumi
import pulumi.runtime
from .. import utilities, tables
class TargetPool(pulumi.CustomResource):
backup_pool: pulumi.Output[str]
"""
URL to the backup target pool. Must also set
failover\_ratio.
"""
description: pulumi.Output[str]
"""
Textual description field.
"""
failover_ratio: pulumi.Output[float]
"""
Ratio (0 to 1) of failed nodes before using the
backup pool (which must also be set).
"""
health_checks: pulumi.Output[str]
"""
List of zero or one health check name or self_link. Only
legacy `google_compute_http_health_check` is supported.
"""
instances: pulumi.Output[list]
"""
List of instances in the pool. They can be given as
URLs, or in the form of "zone/name". Note that the instances need not exist
at the time of target pool creation, so there is no need to use the
Terraform interpolators to create a dependency on the instances from the
target pool.
"""
name: pulumi.Output[str]
"""
A unique name for the resource, required by GCE. Changing
this forces a new resource to be created.
"""
project: pulumi.Output[str]
"""
The ID of the project in which the resource belongs. If it
is not provided, the provider project is used.
"""
region: pulumi.Output[str]
"""
Where the target pool resides. Defaults to project
region.
"""
self_link: pulumi.Output[str]
"""
The URI of the created resource.
"""
session_affinity: pulumi.Output[str]
"""
How to distribute load. Options are "NONE" (no
affinity). "CLIENT\_IP" (hash of the source/dest addresses / ports), and
"CLIENT\_IP\_PROTO" also includes the protocol (default "NONE").
"""
def __init__(__self__, resource_name, opts=None, backup_pool=None, description=None, failover_ratio=None, health_checks=None, instances=None, name=None, project=None, region=None, session_affinity=None, __name__=None, __opts__=None):
"""
Manages a Target Pool within GCE. This is a collection of instances used as
target of a network load balancer (Forwarding Rule). For more information see
[the official
documentation](https://cloud.google.com/compute/docs/load-balancing/network/target-pools)
and [API](https://cloud.google.com/compute/docs/reference/latest/targetPools).
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] backup_pool: URL to the backup target pool. Must also set
failover\_ratio.
:param pulumi.Input[str] description: Textual description field.
:param pulumi.Input[float] failover_ratio: Ratio (0 to 1) of failed nodes before using the
backup pool (which must also be set).
:param pulumi.Input[str] health_checks: List of zero or one health check name or self_link. Only
legacy `google_compute_http_health_check` is supported.
:param pulumi.Input[list] instances: List of instances in the pool. They can be given as
URLs, or in the form of "zone/name". Note that the instances need not exist
at the time of target pool creation, so there is no need to use the
Terraform interpolators to create a dependency on the instances from the
target pool.
:param pulumi.Input[str] name: A unique name for the resource, required by GCE. Changing
this forces a new resource to be created.
:param pulumi.Input[str] project: The ID of the project in which the resource belongs. If it
is not provided, the provider project is used.
:param pulumi.Input[str] region: Where the target pool resides. Defaults to project
region.
:param pulumi.Input[str] session_affinity: How to distribute load. Options are "NONE" (no
affinity). "CLIENT\_IP" (hash of the source/dest addresses / ports), and
"CLIENT\_IP\_PROTO" also includes the protocol (default "NONE").
"""
if __name__ is not None:
warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning)
resource_name = __name__
if __opts__ is not None:
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if not resource_name:
raise TypeError('Missing resource name argument (for URN creation)')
if not isinstance(resource_name, str):
raise TypeError('Expected resource name to be a string')
if opts and not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
__props__ = dict()
__props__['backup_pool'] = backup_pool
__props__['description'] = description
__props__['failover_ratio'] = failover_ratio
__props__['health_checks'] = health_checks
__props__['instances'] = instances
__props__['name'] = name
__props__['project'] = project
__props__['region'] = region
__props__['session_affinity'] = session_affinity
__props__['self_link'] = None
super(TargetPool, __self__).__init__(
'gcp:compute/targetPool:TargetPool',
resource_name,
__props__,
opts)
def translate_output_property(self, prop):
return tables._CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
def translate_input_property(self, prop):
return tables._SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop
|
import datetime
from django.test import Client, TestCase
from streamer.factories import AnimeRoomFactory, AnimeUserFactory
import pytest
from http import HTTPStatus
class TestIndexView(TestCase):
def setUp(self) -> None:
self.client = Client()
self.endpoint = "/"
@pytest.mark.django_db
def test_ok_200(self):
response = self.client.get(self.endpoint)
assert response.status_code == HTTPStatus.OK
class TestUsageView(TestCase):
def setUp(self) -> None:
self.client = Client()
self.endpoint = "/usage"
@pytest.mark.django_db
def test_ok_200(self):
response = self.client.get(self.endpoint)
assert response.status_code == HTTPStatus.OK
class TestAnimeRobbyView(TestCase):
def setUp(self) -> None:
self.client = Client()
self.anime_room = AnimeRoomFactory()
self.endpoint = "/anime-store/lobby/{}"
@pytest.mark.django_db
def test_ok_200(self):
response = self.client.get(self.endpoint.format(str(self.anime_room.room_id)))
assert response.status_code == HTTPStatus.OK
@pytest.mark.django_db
def test_ok_404(self):
response = self.client.get(self.endpoint.format("hello"))
assert response.status_code == HTTPStatus.NOT_FOUND
@pytest.mark.django_db
def test_ok_404(self):
self.anime_room.delete()
response = self.client.get(self.endpoint.format(str(self.anime_room.room_id)))
assert response.status_code == HTTPStatus.NOT_FOUND
|
# Copyright (c) 2015 Cloudbase Solutions Srl
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import re
from neutron.common import constants
from neutron.extensions import portbindings
from neutron.plugins.common import constants as p_constants
from neutron.plugins.ml2.drivers import mech_agent
class VBoxMechanismDriver(mech_agent.SimpleAgentMechanismDriverBase):
"""Attach to networks using VBox L2 agent.
The VBoxMechanismDriver integrates the ml2 plugin with the
VBox L2 agent. Port binding with this driver requires the VBox
agent to be running on the port's host, and that agent to have
connectivity to at least one segment of the port's network.
"""
def __init__(self):
super(VBoxMechanismDriver, self).__init__(
constants.AGENT_TYPE_VBOX,
portbindings.VIF_TYPE_BRIDGE,
{portbindings.CAP_PORT_FILTER: False})
def get_allowed_network_types(self, agent=None):
return [p_constants.TYPE_LOCAL, p_constants.TYPE_FLAT]
def get_mappings(self, agent):
return agent['configurations'].get('network_mappings', {})
def physnet_in_mappings(self, physnet, mappings):
return any(re.match(pattern, physnet) for pattern in mappings)
|
from stackformation.aws.stacks import BaseStack
from stackformation.aws.stacks import TemplateComponent
from stackformation.aws import Ami
import logging
from colorama import Fore, Style, Back # noqa
from troposphere import apigateway, awslambda
from troposphere import ( # noqa
FindInMap, GetAtt, Join,
Parameter, Output, Ref,
Select, Tags, Template,
GetAZs, Export, Base64
)
import json
from stackformation.utils import md5str
class SwaggerJsonTemplate(TemplateComponent):
def __init__(self, template):
self.name = 'SwaggerTemplate'
self.template = template
def render(self):
return self.template
class SwaggerApiStack(BaseStack):
def __init__(self, stack_name):
super(SwaggerApiStack, self).__init__("SwaggerApiStack", 600)
self.stack_name = stack_name
self.lambda_funcs = []
self.ep_type = 'REGIONAL'
self.template_str = ''
self.stages = []
def add_stage(self, stage):
self.stages.append(stage)
def add_swagger(self, template):
self.template_str = template
self.add_template_component('Swagger', SwaggerJsonTemplate(template))
def add_lambda_func(self, func):
self.lambda_funcs.append(func)
def before_deploy(self, context, parameters):
swag_key = '{}Swagger'.format(self.stack_name)
if context.check_var(swag_key):
swag_data = context.get_var(swag_key)
n = 4096
swag_list = [swag_data[i:i + n]
for i in range(0, len(swag_data), n)]
for k, v in enumerate(swag_list):
varname = "{}{}".format(swag_key, k)
context.add_vars({varname: v})
def build_template(self):
t = self._init_template()
swag_data = []
for i in range(0, 4):
swag_data.append(
Ref(t.add_parameter(Parameter(
'{}Swagger{}'.format(self.stack_name, i),
Type='String',
Default=' ',
Description='Swagger Data #{}'.format(i)
)))
)
api = t.add_resource(apigateway.RestApi(
'{}RestApi'.format(self.stack_name),
Body=Join('', swag_data),
EndpointConfiguration=apigateway.EndpointConfiguration(
Types=[self.ep_type]
)
))
if len(self.stages) <= 0:
self.add_stage('prod')
for stage in self.stages:
deployment = t.add_resource(
apigateway.Deployment(
'{}{}{}Deployment'.format(
self.stack_name, md5str(
self.template_str), stage), RestApiId=Ref(api), StageName=md5str(
self.template_str + stage), ))
stage_res = t.add_resource(apigateway.Stage(
'{}{}Stage'.format(self.stack_name, stage),
StageName=stage,
Description='{}{}'.format(self.stack_name, stage),
RestApiId=Ref(api),
DeploymentId=Ref(deployment),
))
for func in self.lambda_funcs:
func_param = t.add_parameter(Parameter(
func.output_func_arn(),
Type='String',
Description='Function to grant invoke access to'
))
t.add_resource(awslambda.Permission(
'SwagFuncPerm{}'.format(func.output_func_name()),
SourceArn=Join("", [
'arn:aws:execute-api:',
Ref('AWS::Region'),
':',
Ref('AWS::AccountId'),
':',
Ref(api),
"/*/*/*"
]),
FunctionName=Ref(func_param),
Action='lambda:invokeFunction',
Principal='apigateway.amazonaws.com',
DependsOn="{}RestApi".format(self.stack_name)
))
t.add_output([
Output(
'ApiId'.format(self.stack_name),
Description='Root id for API',
Value=Ref(api)
),
Output(
'ApiUrl'.format(self.stack_name),
Value=Join('', [
Ref(api),
'.execute-api.',
Ref('AWS::Region'),
'.amazonaws.com'
])
)
])
return t
def output_id(self):
return "{}ApiId".format(self.get_stack_name())
def output_url(self):
return "{}ApiUrl".format(self.get_stack_name())
class CustomDomainStack(BaseStack):
def __init__(self, name, apigw):
super(CustomDomainStack, self).__init__("APIGWDomain", 700)
self.stack_name = name
self.api_stack = apigw
self.domain_name = None
self.region_ssl_arn = None
self.stage = None
self.base_path = None
def add_stage(self, stage):
self.stage = stage
def add_domain(self, domain):
self.domain_name = domain
def add_base_path(self, path):
self.base_path = path
def add_region_ssl_arn(self, arn):
self.region_ssl_arn = arn
def build_template(self):
t = self._init_template()
api_param = t.add_parameter(Parameter(
'{}ApiId'.format(self.api_stack.get_stack_name()),
Type='String'
))
domain = t.add_resource(apigateway.DomainName(
'{}Domain'.format(self.stack_name),
DomainName=self.domain_name,
EndpointConfiguration=apigateway.EndpointConfiguration(
Types=[self.api_stack.ep_type]
)
))
# if self.certificate_arn is not None:
# domain.CertificateArn = self.certificate_arn
if self.region_ssl_arn is not None:
domain.RegionalCertificateArn = self.region_ssl_arn
mapping = t.add_resource(apigateway.BasePathMapping(
'{}Mapping'.format(self.stack_name),
DomainName=Ref(domain),
RestApiId=Ref(api_param)
))
if self.base_path is not None:
mapping.BasePath = self.base_path
if self.stage is not None:
mapping.Stage = self.stage
t.add_output([
Output(
'DomainId',
Value=Ref(domain),
Description="API Gateway Custom Domain ID"
)
])
return t
|
# Script to replace text in a designated field in a resource and post the resource back to API.
# Requirements:
# - ASFunctions.py
# - A csv of format repo,asid
# - sheetFeeder (optional, for reporting purposes)
import ASFunctions as asf
import json
from pprint import pprint
import re
import csv
from sheetFeeder import dataSheet
def main():
asf.setServer('Test')
# Google sheet used for reporting changes.
the_report_sheet=dataSheet('1wNO0t2j5G9U0hUmb7E-jLd4T5skTs1aRxN7HrlyZwEI','resources!A:Z')
id_file = 'resource_replacements.csv'
output_folder = 'output/resource_replacements'
# Read a list of repo and object ids (csv)
the_ids = []
ids = open(id_file)
for row in csv.reader(ids):
the_ids.append([row[0],row[1]])
ids.close()
# Search/replace patterns
the_search_pattern = 'NCC'
the_replace_pattern = 'NNC'
the_before_afters = []
the_heads = ['repo', 'asid','before', 'after']
the_before_afters.append(the_heads)
for an_obj in the_ids:
out_path = output_folder + '/' + an_obj[0] + '_' + an_obj[1] + '_old.json'
# read from API
x = asf.getResource(an_obj[0],an_obj[1])
# Save copy of existing object
print('Saving data to ' + out_path + '....')
f = open(out_path, "w+")
f.write(x)
f.close()
x = json.loads(x)
the_old_field_data = x['user_defined']['string_2']
y = x
y['user_defined']['string_2'] = re.sub(the_search_pattern, the_replace_pattern, x['user_defined']['string_2'])
if y['user_defined']['string_2'] == the_old_field_data:
the_new_field_data = "[no change]"
else:
the_new_field_data = y['user_defined']['string_2']
the_before_afters.append([an_obj[0], an_obj[1], '{string_2} ' + the_old_field_data, '{string_2} ' + the_new_field_data ])
# convert dict back to json for posting.
z = json.dumps(y)
# Post the fixed object back to API.
post = asf.postResource(an_obj[0], an_obj[1], z)
print(post)
# Report changes to Google Sheet
print('Writing before/after info to sheet...')
the_report_sheet.clear()
the_report_sheet.appendData(the_before_afters)
if __name__ == '__main__':
main()
|
try:
from setuptools import setup, find_packages
except ImportError:
from distutils.core import setup
def find_packages(exclude=None):
"""
Just stub this. If you're packaging, you need setuptools. If
you're installing, not so much.
"""
return
required = [
'emds',
'requests',
'pyyaml',
]
scripts = [
'bin/emdu_console',
]
setup(
name='emdu',
version='0.1',
description='EVE Market Data Uploader',
long_description=open('README.rst').read(),
author='Greg Taylor',
author_email='gtaylor@gc-taylor.com',
url='https://github.com/gtaylor/EVE-Market-Data-Uploader',
packages=find_packages(exclude=["tests"]),
scripts=scripts,
package_data={'': ['LICENSE']},
include_package_data=True,
install_requires=required,
license='BSD',
classifiers=(
'Development Status :: 4 - Beta',
'Intended Audience :: Developers',
'Natural Language :: English',
'License :: OSI Approved :: BSD License',
'Programming Language :: Python',
'Programming Language :: Python :: 2.7',
),
)
|
"""
In-memory treq returns stubbed responses.
"""
from functools import partial
from inspect import getmembers, isfunction
from mock import ANY
from six import text_type, binary_type
from twisted.trial.unittest import TestCase
from twisted.web.client import ResponseFailed
from twisted.web.error import SchemeNotSupported
from twisted.web.resource import Resource
from twisted.web.server import NOT_DONE_YET
from twisted.python.compat import _PY3
import treq
from treq.testing import (
HasHeaders,
RequestSequence,
StringStubbingResource,
StubTreq
)
class _StaticTestResource(Resource):
"""Resource that always returns 418 "I'm a teapot"""
isLeaf = True
def render(self, request):
request.setResponseCode(418)
request.setHeader(b"x-teapot", b"teapot!")
return b"I'm a teapot"
class _NonResponsiveTestResource(Resource):
"""Resource that returns NOT_DONE_YET and never finishes the request"""
isLeaf = True
def render(self, request):
return NOT_DONE_YET
class _EventuallyResponsiveTestResource(Resource):
"""
Resource that returns NOT_DONE_YET and stores the request so that something
else can finish the response later.
"""
isLeaf = True
def render(self, request):
self.stored_request = request
return NOT_DONE_YET
class StubbingTests(TestCase):
"""
Tests for :class:`StubTreq`.
"""
def test_stubtreq_provides_all_functions_in_treq_all(self):
"""
Every single function and attribute exposed by :obj:`treq.__all__` is
provided by :obj:`StubTreq`.
"""
treq_things = [(name, obj) for name, obj in getmembers(treq)
if name in treq.__all__]
stub = StubTreq(_StaticTestResource())
api_things = [(name, obj) for name, obj in treq_things
if obj.__module__ == "treq.api"]
content_things = [(name, obj) for name, obj in treq_things
if obj.__module__ == "treq.content"]
# sanity checks - this test should fail if treq exposes a new API
# without changes being made to StubTreq and this test.
msg = ("At the time this test was written, StubTreq only knew about "
"treq exposing functions from treq.api and treq.content. If "
"this has changed, StubTreq will need to be updated, as will "
"this test.")
self.assertTrue(all(isfunction(obj) for name, obj in treq_things), msg)
self.assertEqual(set(treq_things), set(api_things + content_things),
msg)
for name, obj in api_things:
self.assertTrue(
isfunction(getattr(stub, name, None)),
"StubTreq.{0} should be a function.".format(name))
for name, obj in content_things:
self.assertIs(
getattr(stub, name, None), obj,
"StubTreq.{0} should just expose treq.{0}".format(name))
def test_providing_resource_to_stub_treq(self):
"""
The resource provided to StubTreq responds to every request no
matter what the URI or parameters or data.
"""
verbs = ('GET', 'PUT', 'HEAD', 'PATCH', 'DELETE', 'POST')
urls = (
'http://supports-http.com',
'https://supports-https.com',
'http://this/has/a/path/and/invalid/domain/name',
'https://supports-https.com:8080',
'http://supports-http.com:8080',
)
params = (None, {}, {b'page': [1]})
headers = (None, {}, {b'x-random-header': [b'value', b'value2']})
data = (None, b"", b'some data', b'{"some": "json"}')
stub = StubTreq(_StaticTestResource())
combos = (
(verb, {"url": url, "params": p, "headers": h, "data": d})
for verb in verbs
for url in urls
for p in params
for h in headers
for d in data
)
for combo in combos:
verb, kwargs = combo
deferreds = (stub.request(verb, **kwargs),
getattr(stub, verb.lower())(**kwargs))
for d in deferreds:
resp = self.successResultOf(d)
self.assertEqual(418, resp.code)
self.assertEqual([b'teapot!'],
resp.headers.getRawHeaders(b'x-teapot'))
self.assertEqual(b"" if verb == "HEAD" else b"I'm a teapot",
self.successResultOf(stub.content(resp)))
def test_handles_invalid_schemes(self):
"""
Invalid URLs errback with a :obj:`SchemeNotSupported` failure, and does
so even after a successful request.
"""
stub = StubTreq(_StaticTestResource())
self.failureResultOf(stub.get(""), SchemeNotSupported)
self.successResultOf(stub.get("http://url.com"))
self.failureResultOf(stub.get(""), SchemeNotSupported)
def test_files_are_rejected(self):
"""
StubTreq does not handle files yet - it should reject requests which
attempt to pass files.
"""
stub = StubTreq(_StaticTestResource())
self.assertRaises(
AssertionError, stub.request,
'method', 'http://url', files=b'some file')
def test_passing_in_strange_data_is_rejected(self):
"""
StubTreq rejects data that isn't list/dictionary/tuple/bytes/unicode.
"""
stub = StubTreq(_StaticTestResource())
self.assertRaises(
AssertionError, stub.request, 'method', 'http://url',
data=object())
self.successResultOf(stub.request('method', 'http://url', data={}))
self.successResultOf(stub.request('method', 'http://url', data=[]))
self.successResultOf(stub.request('method', 'http://url', data=()))
self.successResultOf(
stub.request('method', 'http://url', data=binary_type(b"")))
self.successResultOf(
stub.request('method', 'http://url', data=text_type("")))
def test_handles_failing_asynchronous_requests(self):
"""
Handle a resource returning NOT_DONE_YET and then canceling the
request.
"""
stub = StubTreq(_NonResponsiveTestResource())
d = stub.request('method', 'http://url', data=b"1234")
self.assertNoResult(d)
d.cancel()
self.failureResultOf(d, ResponseFailed)
def test_handles_successful_asynchronous_requests(self):
"""
Handle a resource returning NOT_DONE_YET and then later finishing the
response.
"""
rsrc = _EventuallyResponsiveTestResource()
stub = StubTreq(rsrc)
d = stub.request('method', 'http://example.com/', data=b"1234")
self.assertNoResult(d)
rsrc.stored_request.finish()
stub.flush()
resp = self.successResultOf(d)
self.assertEqual(resp.code, 200)
def test_handles_successful_asynchronous_requests_with_response_data(self):
"""
Handle a resource returning NOT_DONE_YET and then sending some data in
the response.
"""
rsrc = _EventuallyResponsiveTestResource()
stub = StubTreq(rsrc)
d = stub.request('method', 'http://example.com/', data=b"1234")
self.assertNoResult(d)
chunks = []
rsrc.stored_request.write(b'spam ')
rsrc.stored_request.write(b'eggs')
stub.flush()
resp = self.successResultOf(d)
d = stub.collect(resp, chunks.append)
self.assertNoResult(d)
self.assertEqual(b''.join(chunks), b'spam eggs')
rsrc.stored_request.finish()
stub.flush()
self.successResultOf(d)
def test_handles_successful_asynchronous_requests_with_streaming(self):
"""
Handle a resource returning NOT_DONE_YET and then streaming data back
gradually over time.
"""
rsrc = _EventuallyResponsiveTestResource()
stub = StubTreq(rsrc)
d = stub.request('method', 'http://example.com/', data="1234")
self.assertNoResult(d)
chunks = []
rsrc.stored_request.write(b'spam ')
rsrc.stored_request.write(b'eggs')
stub.flush()
resp = self.successResultOf(d)
d = stub.collect(resp, chunks.append)
self.assertNoResult(d)
self.assertEqual(b''.join(chunks), b'spam eggs')
del chunks[:]
rsrc.stored_request.write(b'eggs\r\nspam\r\n')
stub.flush()
self.assertNoResult(d)
self.assertEqual(b''.join(chunks), b'eggs\r\nspam\r\n')
rsrc.stored_request.finish()
stub.flush()
self.successResultOf(d)
class HasHeadersTests(TestCase):
"""
Tests for :obj:`HasHeaders`.
"""
def test_equality_and_strict_subsets_succeed(self):
"""
The :obj:`HasHeaders` returns True if both sets of headers are
equivalent, or the first is a strict subset of the second.
"""
self.assertEqual(HasHeaders({'one': ['two', 'three']}),
{'one': ['two', 'three']},
"Equivalent headers do not match.")
self.assertEqual(HasHeaders({'one': ['two', 'three']}),
{'one': ['two', 'three', 'four'],
'ten': ['six']},
"Strict subset headers do not match")
def test_partial_or_zero_intersection_subsets_fail(self):
"""
The :obj:`HasHeaders` returns False if both sets of headers overlap
but the first is not a strict subset of the second. It also returns
False if there is no overlap.
"""
self.assertNotEqual(HasHeaders({'one': ['two', 'three']}),
{'one': ['three', 'four']},
"Partial value overlap matches")
self.assertNotEqual(HasHeaders({'one': ['two', 'three']}),
{'one': ['two']},
"Missing value matches")
self.assertNotEqual(HasHeaders({'one': ['two', 'three']}),
{'ten': ['six']},
"Complete inequality matches")
def test_case_insensitive_keys(self):
"""
The :obj:`HasHeaders` equality function ignores the case of the header
keys.
"""
self.assertEqual(HasHeaders({b'A': [b'1'], b'b': [b'2']}),
{b'a': [b'1'], b'B': [b'2']})
def test_case_sensitive_values(self):
"""
The :obj:`HasHeaders` equality function does care about the case of
the header value.
"""
self.assertNotEqual(HasHeaders({b'a': [b'a']}), {b'a': [b'A']})
def test_bytes_encoded_forms(self):
"""
The :obj:`HasHeaders` equality function compares the bytes-encoded
forms of both sets of headers.
"""
self.assertEqual(HasHeaders({b'a': [b'a']}), {u'a': [u'a']})
self.assertEqual(HasHeaders({u'b': [u'b']}), {b'b': [b'b']})
def test_repr(self):
"""
:obj:`HasHeaders` returns a nice string repr.
"""
if _PY3:
reprOutput = "HasHeaders({b'a': [b'b']})"
else:
reprOutput = "HasHeaders({'a': ['b']})"
self.assertEqual(reprOutput, repr(HasHeaders({b'A': [b'b']})))
class StringStubbingTests(TestCase):
"""
Tests for :obj:`StringStubbingResource`.
"""
def _get_response_for(self, expected_args, response):
"""
Make a :obj:`IStringResponseStubs` that checks the expected args and
returns the given response.
"""
method, url, params, headers, data = expected_args
def get_response_for(_method, _url, _params, _headers, _data):
self.assertEqual((method, url, params, data),
(_method, _url, _params, _data))
self.assertEqual(HasHeaders(headers), _headers)
return response
return get_response_for
def test_interacts_successfully_with_istub(self):
"""
The :obj:`IStringResponseStubs` is passed the correct parameters with
which to evaluate the response, and the response is returned.
"""
resource = StringStubbingResource(self._get_response_for(
(b'DELETE', 'http://what/a/thing', {b'page': [b'1']},
{b'x-header': [b'eh']}, b'datastr'),
(418, {b'x-response': b'responseheader'}, b'response body')))
stub = StubTreq(resource)
d = stub.delete('http://what/a/thing', headers={b'x-header': b'eh'},
params={b'page': b'1'}, data=b'datastr')
resp = self.successResultOf(d)
self.assertEqual(418, resp.code)
self.assertEqual([b'responseheader'],
resp.headers.getRawHeaders(b'x-response'))
self.assertEqual(b'response body',
self.successResultOf(stub.content(resp)))
class RequestSequenceTests(TestCase):
"""
Tests for :obj:`RequestSequence`.
"""
def setUp(self):
"""
Set up a way to report failures asynchronously.
"""
self.async_failures = []
def test_mismatched_request_causes_failure(self):
"""
If a request is made that is not expected as the next request,
causes a failure.
"""
sequence = RequestSequence(
[((b'get', 'https://anything/', {b'1': [b'2']},
HasHeaders({b'1': [b'1']}), b'what'),
(418, {}, b'body')),
((b'get', 'http://anything', {},
HasHeaders({b'2': [b'1']}), b'what'),
(202, {}, b'deleted'))],
async_failure_reporter=self.async_failures.append)
stub = StubTreq(StringStubbingResource(sequence))
get = partial(stub.get, 'https://anything?1=2', data=b'what',
headers={b'1': b'1'})
resp = self.successResultOf(get())
self.assertEqual(418, resp.code)
self.assertEqual(b'body', self.successResultOf(stub.content(resp)))
self.assertEqual([], self.async_failures)
resp = self.successResultOf(get())
self.assertEqual(500, resp.code)
self.assertEqual(1, len(self.async_failures))
self.assertIn("Expected the next request to be",
self.async_failures[0])
self.assertFalse(sequence.consumed())
def test_unexpected_number_of_request_causes_failure(self):
"""
If there are no more expected requests, making a request causes a
failure.
"""
sequence = RequestSequence(
[],
async_failure_reporter=self.async_failures.append)
stub = StubTreq(StringStubbingResource(sequence))
d = stub.get('https://anything', data=b'what', headers={b'1': b'1'})
resp = self.successResultOf(d)
self.assertEqual(500, resp.code)
self.assertEqual(b'StubbingError',
self.successResultOf(resp.content()))
self.assertEqual(1, len(self.async_failures))
self.assertIn("No more requests expected, but request",
self.async_failures[0])
# the expected requests have all been made
self.assertTrue(sequence.consumed())
def test_works_with_mock_any(self):
"""
:obj:`mock.ANY` can be used with the request parameters.
"""
sequence = RequestSequence(
[((ANY, ANY, ANY, ANY, ANY), (418, {}, b'body'))],
async_failure_reporter=self.async_failures.append)
stub = StubTreq(StringStubbingResource(sequence))
with sequence.consume(sync_failure_reporter=self.fail):
d = stub.get('https://anything', data=b'what',
headers={b'1': b'1'})
resp = self.successResultOf(d)
self.assertEqual(418, resp.code)
self.assertEqual(b'body', self.successResultOf(stub.content(resp)))
self.assertEqual([], self.async_failures)
# the expected requests have all been made
self.assertTrue(sequence.consumed())
def test_consume_context_manager_fails_on_remaining_requests(self):
"""
If the `consume` context manager is used, if there are any remaining
expecting requests, the test case will be failed.
"""
sequence = RequestSequence(
[((ANY, ANY, ANY, ANY, ANY), (418, {}, b'body'))] * 2,
async_failure_reporter=self.async_failures.append)
stub = StubTreq(StringStubbingResource(sequence))
consume_failures = []
with sequence.consume(sync_failure_reporter=consume_failures.append):
self.successResultOf(stub.get('https://anything', data=b'what',
headers={b'1': b'1'}))
self.assertEqual(1, len(consume_failures))
self.assertIn(
"Not all expected requests were made. Still expecting:",
consume_failures[0])
self.assertIn(
"{0}(url={0}, params={0}, headers={0}, data={0})".format(
repr(ANY)),
consume_failures[0])
# no asynchronous failures (mismatches, etc.)
self.assertEqual([], self.async_failures)
def test_async_failures_logged(self):
"""
When no `async_failure_reporter` is passed async failures are logged by
default.
"""
sequence = RequestSequence([])
stub = StubTreq(StringStubbingResource(sequence))
with sequence.consume(self.fail):
self.successResultOf(stub.get('https://example.com'))
[failure] = self.flushLoggedErrors()
self.assertIsInstance(failure.value, AssertionError)
|
from bs4 import Comment, NavigableString, Tag
from prismriver.plugin.common import Plugin
from prismriver.struct import Song
class UtaTenPlugin(Plugin):
ID = 'utaten'
RANK = 9
def __init__(self, config):
super(UtaTenPlugin, self).__init__('UtaTen', config)
def search_song(self, artist, title):
link = 'http://utaten.com/lyric/{}/{}/'.format(
self.prepare_url_parameter(artist),
self.prepare_url_parameter(title))
page = self.download_webpage_text(link)
if page:
soup = self.prepare_soup(page)
main_pane = soup.find('main')
title_pane = main_pane.find('div', {'class': 'contentBox__title contentBox__title--lyricTitle'})
if not title_pane:
# song not found, redirected to main page
return None
title_pane_parts = title_pane.h1.contents
song_title = title_pane_parts[0].strip()[1:-1]
song_artist = title_pane_parts[3].text.strip()
lyric_pane = main_pane.find('div', {'class': 'lyricBody'})
lyric_pane = lyric_pane.find('div', {'class': 'medium'})
lyric = self.parse_verse_block(lyric_pane)
return Song(song_artist, song_title, self.sanitize_lyrics([lyric]))
# todo: handle furigana
def parse_verse_block(self, verse_block, tags_to_skip=None):
lyric = ''
for elem in verse_block.childGenerator():
if isinstance(elem, Comment):
pass
elif isinstance(elem, NavigableString):
lyric += elem.strip()
elif isinstance(elem, Tag):
if elem.name == 'span':
lyric += elem.find('span', {'class': 'rb'}, recursive=False).text
else:
lyric += '\n'
return lyric.strip()
|
#
# (c) Copyright 2016 Hewlett Packard Enterprise Development LP
# (c) Copyright 2017 SUSE LLC
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
#
import argparse
import os
import pipes
import re
import select
import shlex
import shutil
import socket
import subprocess
import sys
import time
import yaml
import paramiko
# Unbuffer output
sys.stdout = os.fdopen(sys.stdout.fileno(), 'w', 0)
sys.stderr = os.fdopen(sys.stderr.fileno(), 'w', 0)
NEWLINE = re.compile("\r?\n")
class TestPlanAction(object):
def __init__(self, ssh_config="astack-ssh-config",
deployer_node="server1"):
self.cfg = paramiko.SSHConfig()
self.cfg.parse(open(ssh_config))
self.ssh_config = ssh_config
self.deployer_node = deployer_node
self.deployer_user = self.config(self.deployer_node)["user"]
self.client = self.connect(deployer_node)
self.name = None
self.log_filename = None
self.log_prefix = None
self.log = None
self._first_write = True
self.filename = None
self.testdata = None
def config(self, server):
return self.cfg.lookup(server)
def connect(self, node):
client = paramiko.SSHClient()
client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
hostinfo = self.cfg.lookup(node)
client.connect(
hostinfo["hostname"],
username=hostinfo["user"],
key_filename=hostinfo["identityfile"])
return client
def get_scratchdir(self):
return os.path.expanduser(
"/home/%s/scratch/ansible/next/ardana/ansible" % self.deployer_user)
def get_testdir(self):
return os.path.expanduser("/home/%s/ardana-ci-tests" % self.deployer_user)
def set_loginfo(self, name, filename, prefix=None):
if self.log:
self.log.close()
self.name = name
if "/" in filename:
raise Exception(
"We don't support filenames with directory location")
# Jenkins integration, make sure that we work with the jenkins
# log publishers
if os.environ.get("WORKSPACE", None):
self.log_filename = os.path.join(os.environ["WORKSPACE"], filename)
else:
self.log_filename = filename
self.log = open(filename, "w")
self.log_prefix = prefix
def log_data(self, data):
if self.log_prefix:
prefix = "\n%s: " % self.log_prefix
if self._first_write:
data = "%s: %s" % (self.log_prefix, data)
data = NEWLINE.sub(prefix, data)
sys.stdout.write(data)
if self.log:
self.log.write(data)
self._first_write = False
def _run_cmd_locally(self, shell_cmd, cwd=None, env={}):
self.log_data("Running '%s'\n" % (shell_cmd))
env["HOME"] = os.environ["HOME"]
env["PYTHONUNBUFFERED"] = "1"
for key, val in os.environ.items():
if key.startswith("ARDANA"):
env[key] = val
devbin = os.path.dirname(__file__) + "/../"
testdir = os.path.dirname(self.filename)
env["PATH"] = "%s:%s:%s" % (
testdir,
devbin,
"/usr/local/bin:/usr/bin:/bin:")
self.log_data(" ENV: %s\n" % env)
if not cwd:
cwd = testdir
cmd = shlex.split(shell_cmd)
p = subprocess.Popen(
cmd,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
cwd=cwd,
env=env)
while True:
line = p.stdout.readline()
if not line and p.poll() is not None:
break
self.log_data(line)
p.communicate()[0]
status = p.returncode
self.log_data("'%s' exited with local status: %d\n" % (cmd, status))
if status != 0:
raise Exception(
"'%s' exited with local status: %d" % (cmd, status))
def run_locally(self, shell_cmd):
if isinstance(shell_cmd, dict):
env = shell_cmd.get("env", {})
cwd = shell_cmd.get("chdir", None)
self._run_cmd_locally(shell_cmd["cmd"], cwd, env)
else:
self._run_cmd_locally(shell_cmd)
def run_on_deployer(self, cmd):
transport = self.client.get_transport()
transport.set_keepalive(1)
channel = transport.open_session()
channel.get_pty()
channel.setblocking(0)
channel.exec_command(cmd)
while True:
rl, wl, xl = select.select([channel], [], [])
if len(rl) > 0:
data = channel.recv(1024)
if not data:
break
self.log_data(data)
status = channel.recv_exit_status()
self.log_data("'%s' exited with status: %d" % (cmd, status))
channel.close()
if status != 0:
raise Exception("'%s' exited with status %d" % (cmd, status))
def run_executable_on_deployer(self, executable, cwd=None):
cwd = cwd or self.get_scratchdir()
cmd = "env PATH=%s:$PATH bash -c \"cd %s ; %s\"" % (
self.get_testdir(), pipes.quote(cwd), executable)
self.log_data("Running '%s'\n" % (cmd))
self.run_on_deployer(cmd)
def run_playbook(self, playbook):
self.run_on_deployer(
"cd %s ; ansible-playbook -i %s/hosts/verb_hosts %s/%s" % (
self.get_scratchdir(), self.get_scratchdir(),
self.get_scratchdir(),
playbook))
# Run tempest tests
def run_tempest_region(self, region, filters, tempest_cwd):
ftp = self.client.open_sftp()
fp = ftp.file(os.path.join(tempest_cwd, "tempest.filter"), "w")
fp.write("\n".join(filters))
fp.close()
self.run_executable_on_deployer(
"sudo -u tempest /opt/stack/tempest/bin/ardana-tempest.sh"
" --config tempest_%s.conf"
" --run-filter %s" % (
region,
pipes.quote(os.path.join(tempest_cwd, "tempest.filter"))
),
cwd=tempest_cwd
)
for filename in ["tempest_%s.log" % region, "testrepository.subunit"]:
fp = ftp.file(os.path.join(tempest_cwd, filename))
with open("%s-%s" % (
self.log_filename, filename), "w") as local_fp:
shutil.copyfileobj(fp, local_fp)
fp.close()
ftp.close()
def run_tempest(self, tempest):
tempest_cwd = os.path.join(
"/tmp", "%s-%s" % (self.name, time.strftime('%Y%m%dT%H%M%SZ')))
ftp = self.client.open_sftp()
ftp.mkdir(tempest_cwd)
remote_cwd = ftp.open(tempest_cwd)
remote_cwd.chmod(0o777)
remote_cwd.close()
ftp.close()
if isinstance(tempest, dict):
for region, filters in tempest.items():
self.run_tempest_region(region, filters, tempest_cwd)
else:
self.run_tempest_region("region1", tempest, tempest_cwd)
# VM Operations that run locally
def run_virsh_command(self, cmd, vms):
if type(vms) != list:
vms = [vms]
for vm in vms:
vm_name = "project-vagrant_%s" % vm
self.log_data(
"VM operation: %s virsh instance: '%s'" % (
cmd, vm_name))
status = os.system("virsh %s %s" % (cmd, vm_name))
if status != 0:
raise Exception("Failed to %s '%s'" % (cmd, vm))
# sleep to allow virsh to do its thing.
time.sleep(1)
def run_virsh_reboot(self, vms):
self.run_virsh_command("reboot", vms)
self._wait_for_vms_to_start(vms)
def run_virsh_shutdown(self, vms):
self.run_virsh_command("shutdown", vms)
def run_virsh_start(self, vms):
if type(vms) != list:
vms = [vms]
for vm in vms:
vm_name = "minimal-vagrant_%s" % vm
# Sytem is not running then we start it.
status = os.system(
'virsh list --all | grep -qE "%s[ ]+running"' % vm_name)
if status != 0:
self.run_virsh_command("start", vm)
else:
self.log_data("VM operation: %s is already running\n" % vm)
self._wait_for_vms_to_start(vms)
def _wait_for_vms_to_start(self, vms, max_retry=60):
sleep_time = 3
if type(vms) != list:
vms = [vms]
for vm in vms:
self.log_data(
"VM operation: waiting for %s to come back...\n" % vm)
for i in range(0, max_retry):
try:
client = self.connect(vm)
except (paramiko.BadHostKeyException,
paramiko.AuthenticationException,
paramiko.SSHException,
paramiko.ssh_exception.NoValidConnectionsError,
socket.error):
if i >= max_retry - 1:
self.log_data(
"VM operation: Timed out waiting for %s\n" % vm)
raise
time.sleep(sleep_time)
else:
self.log_data(
"VM operation: %s available after: %d\n" %
(vm, i * sleep_time))
# If we update the deployer_node then we should also use
# the new client object.
if vm == self.deployer_node:
self.client = client
break
else:
raise Exception("Failed to connect to %s after reboot" % vm)
def load(self, filename):
self.filename = filename
self.testdata = yaml.load(open(filename))
def main(ssh_config, deployer_node, filename):
actions = TestPlanAction(ssh_config, deployer_node)
count = 0
actions.load(filename)
for testdata in actions.testdata:
count += 1
name = testdata["name"]
if testdata.get("logfile", None):
logfilename = testdata["logfile"]
else:
logfilename = "testsuite-%s.log" % name.replace(" ", "_")
actions.set_loginfo(
name, logfilename, testdata.get("prefix", "part%d" % count))
actions.log_data("Start running *** %s ***\n\n" % name)
vms = testdata.get("vms", [])
if vms:
actions.log_data("VM operations\n")
for vm in vms:
reboot_vms = vm.get("reboot", [])
actions.run_virsh_reboot(reboot_vms)
shutdown_vms = vm.get("shutdown", [])
actions.run_virsh_shutdown(shutdown_vms)
start_vms = vm.get("start", [])
actions.run_virsh_start(start_vms)
playbooks = testdata.get("playbooks", [])
actions.log_data("Run playbooks: %s\n" % (",".join(playbooks)))
for playbook in playbooks:
actions.run_playbook(playbook)
execs = testdata.get("exec", [])
actions.log_data("Run executables: %s\n" % (",".join(execs)))
for cmd in execs:
actions.run_executable_on_deployer(cmd)
tests = testdata.get("tests", [])
actions.log_data("Run tests: %s\n" % (",".join(tests)))
for testcmd in tests:
actions.run_executable_on_deployer(testcmd)
localexecs = testdata.get("local", [])
actions.log_data("Run local commands\n")
for testcmd in localexecs:
actions.run_locally(testcmd)
tempests = testdata.get("tempest", None)
actions.log_data("Running tempest tests")
if tempests:
actions.run_tempest(tempests)
sys.stdout.write("\n")
if actions.log:
actions.log.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="XX")
parser.add_argument("--ssh-config", type=str, action="store",
help="SSH configuration")
parser.add_argument("--deployer-node", type=str, action="store",
help="",
default="server1")
parser.add_argument("test_plan", nargs="+",
help="")
args = parser.parse_args()
for test_plan in args.test_plan:
main(args.ssh_config, args.deployer_node, test_plan)
|
import matplotlib.pyplot as plt
import argparse
import pathlib
import numpy as np
import typing
colors = ["blue", "green", "cyan", "red", "yellow", "magenta", "peru", "azure", "slateblue", "plum"]
def plot_bbox(bbox_XYXY, label):
xmin, ymin, xmax, ymax = bbox_XYXY
plt.plot(
[xmin, xmin, xmax, xmax, xmin],
[ymin, ymax, ymax, ymin, ymin],
color=colors[label],
label=str(label))
def read_labels(label_path: pathlib.Path) -> typing.Tuple[np.ndarray]:
assert label_path.is_file(), f"Did not find file: {label_path}"
labels = []
BBOXES_XYXY = []
with open(label_path, "r") as fp:
for line in list(fp.readlines())[1:]:
label, xmin, ymin, xmax, ymax = [int(_) for _ in line.split(",")]
labels.append(label)
BBOXES_XYXY.append([xmin, ymin, xmax, ymax])
boxes = np.array(BBOXES_XYXY)
if len(boxes) == 0:
boxes = np.zeros((0, 4))
return np.array(labels), boxes
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("directory")
args = parser.parse_args()
base_path = pathlib.Path(args.directory)
image_dir = base_path.joinpath("images")
label_dir = base_path.joinpath("labels")
impaths = image_dir.glob("*.png")
for impath in impaths:
label_path = label_dir.joinpath(
f"{impath.stem}.txt"
)
labels, bboxes_XYXY = read_labels(label_path)
im = plt.imread(str(impath))
plt.imshow(im, cmap="gray")
for bbox, label in zip(bboxes_XYXY, labels):
plot_bbox(bbox, label)
plt.savefig("example_image.png")
plt.show()
|
feira = ('Pão', 2, 'Sorda', 4, 'Queijo', 12.75)
print('_' * 40)
print(' '*10, 'FEIRA NA PADARIA', ' '*10)
print('-' * 40)
for pos in range(0, len(feira)):
if pos % 2 == 0:
print(f'{feira[pos]:.<30}', end='')
else:
print(f'R${feira[pos]}')
print('_' * 40)
|
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A command line utility that creates custom Cloud Monitoring metrics
for tracking GPU and GPU memory utilization."""
from google.cloud import monitoring_v3
from absl import app
from absl import flags
from absl import logging
GPU_UTILIZATION_METRIC_NAME = 'gce/gpu/utilization'
GPU_UTILIZATION_METRIC_DESC = 'GPU utilization'
GPU_MEMORY_UTILIZATION_METRIC_NAME = 'gce/gpu/memory_utilization'
GPU_MEMORY_UTILIZATION_METRIC_DESC = 'GPU memory utilization'
FLAGS = flags.FLAGS
flags.DEFINE_string('project_id', None, 'GCP Project ID')
flags.mark_flag_as_required('project_id')
def create_gpu_metrics(project_id):
"Creates metrics that monitor GPU utilization."
def _create_metric_descriptor(name, description):
"Creates a GAUGE/INT64 metric descriptor."
descriptor = monitoring_v3.types.MetricDescriptor()
descriptor.type = f'custom.googleapis.com/{name}'
descriptor.metric_kind = monitoring_v3.enums.MetricDescriptor.MetricKind.GAUGE
descriptor.value_type = monitoring_v3.enums.MetricDescriptor.ValueType.INT64
descriptor.description = description
descriptor = client.create_metric_descriptor(project_name, descriptor)
print(descriptor)
client = monitoring_v3.MetricServiceClient()
project_name = client.project_path(project_id)
_create_metric_descriptor(GPU_UTILIZATION_METRIC_NAME,
GPU_UTILIZATION_METRIC_DESC)
_create_metric_descriptor(GPU_MEMORY_UTILIZATION_METRIC_NAME,
GPU_MEMORY_UTILIZATION_METRIC_DESC)
def main(argv):
del argv
create_gpu_metrics(FLAGS.project_id)
if __name__ == '__main__':
app.run(main) |
from random import sample
WORD_FILE_NAME = "/usr/share/dict/words"
def city_names(number=None):
with open(WORD_FILE_NAME, 'r') as word_file:
words = word_file.read().splitlines()
cities = [word[:-4] for word in words if (
len(word) > 4
and word.endswith('city')
and word.isalpha()
and word.islower()
)]
city_count = max(0, min(
1 if number is None else int(number),
len(cities)
))
return sorted(sample(cities, city_count))
if __name__ == '__main__':
import sys
arguments = sys.argv[1:]
if len(arguments) > 0:
argument = arguments[0]
else:
argument = None
for city in city_names(argument):
print(city)
|
'''
Dictionary store
=================
Use a Python dictionary as a store.
'''
__all__ = ('DictStore', )
try:
import cPickle as pickle
except ImportError:
import pickle
from os.path import exists
from kivy.compat import iteritems
from kivy.storage import AbstractStore
class DictStore(AbstractStore):
'''Store implementation using a pickled `dict`.
See the :mod:`kivy.storage` module documentation for more information.
'''
def __init__(self, filename, data=None, **kwargs):
if isinstance(filename, dict):
# backward compatibility, first argument was a dict.
self.filename = None
self._data = filename
else:
self.filename = filename
self._data = data or {}
self._is_changed = True
super(DictStore, self).__init__(**kwargs)
def store_load(self):
if self.filename is None:
return
if not exists(self.filename):
return
with open(self.filename, 'rb') as fd:
data = fd.read()
if data:
self._data = pickle.loads(data)
def store_sync(self):
if self.filename is None:
return
if not self._is_changed:
return
with open(self.filename, 'wb') as fd:
pickle.dump(self._data, fd)
self._is_changed = False
def store_exists(self, key):
return key in self._data
def store_get(self, key):
return self._data[key]
def store_put(self, key, value):
self._data[key] = value
self._is_changed = True
return True
def store_delete(self, key):
del self._data[key]
self._is_changed = True
return True
def store_find(self, filters):
for key, values in iteritems(self._data):
found = True
for fkey, fvalue in iteritems(filters):
if fkey not in values:
found = False
break
if values[fkey] != fvalue:
found = False
break
if found:
yield key, values
def store_count(self):
return len(self._data)
def store_keys(self):
return list(self._data.keys())
|
"""Well-known (and otherwise) constants used by JupyterLite"""
import shutil
from pathlib import Path
#: a locale for reproducible file sorting
C_LOCALE = "C"
#: the encoding for pretty much every file written and read by jupyterlite
UTF8 = dict(encoding="utf-8")
JSON_FMT = dict(sort_keys=True, indent=2)
ROOT = Path(__file__).parent
#: all of the archives
ALL_APP_ARCHIVES = sorted(ROOT.glob("jupyterlite-*.tgz"))
#: the extension point for addons, including core
ADDON_ENTRYPOINT = "jupyterlite.addon.v0"
### other parties' well-known paths
#: a predictably-serveable HTML file
INDEX_HTML = "index.html"
#: settings overrides. used JupyterLab build system, usually goes in
#: $PREFIX/share/jupyter/lab/
OVERRIDES_JSON = "overrides.json"
#: the canonical location within an env (or archive) for labextensions
SHARE_LABEXTENSIONS = "share/jupyter/labextensions"
#: the canonical location of labextension metadata
PACKAGE_JSON = "package.json"
#: the generally-used listing of pip requirements
REQUIREMENTS_TXT = "requirements.txt"
#: output equivalent to `sha256sum *` for providing a local bill-of-data
SHA256SUMS = "SHA256SUMS"
#: a script DOM ID on most jupyter pages
JUPYTER_CONFIG_DATA = "jupyter-config-data"
FEDERATED_EXTENSIONS = "federated_extensions"
DISABLED_EXTENSIONS = "disabledExtensions"
SETTINGS_OVERRIDES = "settingsOverrides"
#: the top-level key for lite plugin settings
LITE_PLUGIN_SETTINGS = "litePluginSettings"
#: the plugin id for the pyolite kernel
PYOLITE_PLUGIN_ID = "@jupyterlite/pyolite-kernel-extension:kernel"
### jupyterlite "well-known" paths
#: our schema
JUPYTERLITE_SCHEMA = "jupyterlite.schema.v0.json"
#: our configuration file
JUPYTERLITE_JSON = "jupyter-lite.json"
#: our configuration file
JUPYTERLITE_IPYNB = "jupyter-lite.ipynb"
JUPYTERLITE_METADATA = "jupyter-lite"
JUPYTER_LITE_CONFIG = "jupyter_lite_config.json"
#: Needs a better canonical location
DEFAULT_OUTPUT_DIR = "_output"
#: commonly-used filename for response fixtures, e.g. settings
ALL_JSON = "all.json"
### Environment Variables
#: a canonical environment variable for triggering reproducible builds
SOURCE_DATE_EPOCH = "SOURCE_DATE_EPOCH"
#: this is arrived at by inspection
NPM_SOURCE_DATE_EPOCH = 499162500
#: the only kind of noarch wheel piplite understands
NOARCH_WHL = "py3-none-any.whl"
#: the only kind of binary wheel piplite understands
WASM_WHL = "emscripten_wasm32.whl"
#: the only kinds of wheels piplite understands
ALL_WHL = [NOARCH_WHL, WASM_WHL]
### URLs
#: the Jupyter API route for Contents API
API_CONTENTS = "api/contents"
API_TRANSLATIONS = "api/translations"
LAB_EXTENSIONS = "extensions"
#: our doit task-based plugin system
HOOKS = [
"status",
"init",
"build",
"check",
"serve",
"archive",
]
#: the name of the previous hook
HOOK_PARENTS = dict(
build="init",
check="build",
serve="build",
archive="build",
)
#: the lifecycle stages inside a hook
PHASES = ["pre_", "", "post_"]
#: extensions to be considered sourcemaps
SOURCEMAPS = [".js.map", ".mjs.map"]
SOURCEMAP_IGNORE_PATTERNS = shutil.ignore_patterns(*[f"*{p}" for p in SOURCEMAPS])
|
# Mainly copied from NeoPixel strandtest example. Author: Tony DiCola (tony@tonydicola.com)
# See: https://github.com/jgarff/rpi_ws281x
import threading
import time
from neopixel import *
# Led Driver for Pawn Shy to animate / set the Pawn LED states.
class LedDriver():
def __init__(self):
# LED strip configuration:
LED_COUNT = 5 # Number of LED pixels.
LED_PIN = 18 # GPIO pin connected to the pixels (18 uses PWM!).
LED_FREQ_HZ = 800000 # LED signal frequency in hertz (usually 800khz)
LED_DMA = 5 # DMA channel to use for generating signal (try 5)
LED_BRIGHTNESS = 255 # Set to 0 for darkest and 255 for brightest
LED_INVERT = False # True to invert the signal (when using NPN transistor level shift)
LED_CHANNEL = 0 # set to '1' for GPIOs 13, 19, 41, 45 or 53
LED_STRIP = ws.WS2811_STRIP_GRB # Strip type and colour ordering
# Create NeoPixel object with appropriate configuration.
self.strip = Adafruit_NeoPixel(LED_COUNT, LED_PIN, LED_FREQ_HZ, LED_DMA, LED_INVERT, LED_BRIGHTNESS, LED_CHANNEL, LED_STRIP)
#self.bad_color = Color(255, 0, 0)
self.bad_color = Color(128, 0, 128) # Purple
#self.good_color = Color(0, 0, 0) # Off
self.good_color = Color(255, 255, 0) # Minion Yellow
# Intialize the library (must be called once before other functions).
self.strip.begin()
# Idle
self.display_mode = 0
self.pwn_count = 0
self.running = False
self.counter = 0
self.result_shown = False
# Animate the LEDs whilst we wait for a user input
def animate_whilst_not_busy(self):
self.counter = 0
self.display_mode = 0
# Animate the LEDs whilst the HIBP result is being retreived
def animate_whilst_hibp_lookup(self):
self.counter = 0
self.display_mode = 1
# show the result from an email lookup. count should be the
# number of times the email address was pwned
def show_result_email_count(self, count):
self.counter = 0
self.display_mode = 2
self.pwn_count = count
self.result_shown = False
# show the results from a domain lookup. count should be the number
# of accounts compromised
def show_result_web_count(self, count):
self.counter = 0
self.display_mode = 3
self.pwn_count = count
self.result_shown = False
# Start a background thread to show the status
def start(self):
self.display_mode = 0
self.running = True
# start a thread to run the LED animations in the background.
thread = threading.Thread(target=self.run_animations, args=())
thread.daemon = True # Daemonize thread
thread.start()
def stop(self):
self.running = False
# Thred target to show status.
def run_animations(self):
while self.running:
self.counter = self.counter + 1;
if self.display_mode == 0:
self._show_idle()
elif self.display_mode == 1:
self._show_hibp_lookup()
elif self.display_mode == 2:
self._show_email_result()
elif self.display_mode == 3:
self._show_web_result()
def _show_idle(self):
#self.theaterChaseRainbow(self.strip, 20, 1)
for i in range(self.strip.numPixels()):
self.strip.setPixelColor(i, self.wheel((int(i * 256 / self.strip.numPixels()) + self.counter) & 255))
self.strip.show()
time.sleep(20 / 1000.0)
if self.counter > 255:
self.counter = 0
def _show_hibp_lookup(self):
#self.rainbowCycle(self.strip, 2, 1)
"""Draw rainbow that uniformly distributes itself across all pixels."""
for i in range(self.strip.numPixels()):
self.strip.setPixelColor(i, self.wheel((int(i * 256 / self.strip.numPixels()) + self.counter) & 255))
self.strip.show()
time.sleep(2 / 1000.0)
if self.counter > 255:
self.counter = 0
def _show_email_result(self):
if self.result_shown:
time.sleep(0.01)
return;
# Count 5+ all leds.
# Count 4, LED1 Off
# Count 3, LED1,2 Off
# Count 2, LED1,2,3 Off
# Count 1, LED1,2,3,4 Off
# Count 0, LED1,2,3,4,5 Off
for i in range(self.strip.numPixels()):
self.strip.setPixelColor(i, self.good_color)
self.strip.show()
self.animate_results(self.strip, 0)
self.strip.setPixelColor(0, self.bad_color if self.pwn_count>=5 else self.good_color)
if self.pwn_count < 5:
self.animate_results(self.strip, 1)
self.strip.setPixelColor(1, self.bad_color if self.pwn_count>=4 else self.good_color)
if self.pwn_count < 4:
self.animate_results(self.strip, 2)
self.strip.setPixelColor(2, self.bad_color if self.pwn_count>=3 else self.good_color)
if self.pwn_count < 3:
self.animate_results(self.strip, 3)
self.strip.setPixelColor(3, self.bad_color if self.pwn_count>=2 else self.good_color)
if self.pwn_count < 2:
self.animate_results(self.strip, 4)
self.strip.setPixelColor(4, self.bad_color if self.pwn_count>=1 else self.good_color)
self.strip.show()
self.result_shown = True
def _show_web_result(self):
if self.result_shown:
time.sleep(0.01)
return;
for i in range(self.strip.numPixels()):
self.strip.setPixelColor(i, self.good_color)
self.animate_results(self.strip, 0)
self.strip.setPixelColor(0, self.good_color if self.pwn_count < 100000000 else self.bad_color)
if self.pwn_count < 100000000:
self.animate_results(self.strip, 1)
self.strip.setPixelColor(1, self.good_color if self.pwn_count < 10000000 else self.bad_color)
if self.pwn_count < 10000000:
self.animate_results(self.strip, 2)
self.strip.setPixelColor(2, self.good_color if self.pwn_count < 1000000 else self.bad_color)
if self.pwn_count < 1000000:
self.animate_results(self.strip, 3)
self.strip.setPixelColor(3, self.good_color if self.pwn_count < 100000 else self.bad_color)
if self.pwn_count < 100000:
self.animate_results(self.strip, 4)
self.strip.setPixelColor(4, self.good_color if self.pwn_count < 1 else self.bad_color)
self.strip.show()
self.result_shown = True
def animate_results(self, strip, start, wait_ms=1, iterations=10):
# Animate pawns starting at start
# so giving a count down.
for j in range(0, 256 * iterations, 2):
for i in range(start, strip.numPixels()):
strip.setPixelColor(i, self.wheel((int(i * 256 / strip.numPixels()) + j) & 255))
strip.show()
time.sleep(wait_ms / 1000.0)
# Define functions which animate LEDs in various ways.
def colorWipe(self, strip, color, wait_ms=50):
"""Wipe color across display a pixel at a time."""
for i in range(strip.numPixels()):
strip.setPixelColor(i, color)
strip.show()
time.sleep(wait_ms / 1000.0)
def theaterChase(self, strip, color, wait_ms=50, iterations=10):
"""Movie theater light style chaser animation."""
for j in range(iterations):
for q in range(3):
for i in range(0, strip.numPixels(), 3):
strip.setPixelColor(i + q, color)
strip.show()
time.sleep(wait_ms / 1000.0)
for i in range(0, strip.numPixels(), 3):
strip.setPixelColor(i + q, 0)
def wheel(self, pos):
"""Generate rainbow colors across 0-255 positions."""
if pos < 85:
return Color(pos * 3, 255 - pos * 3, 0)
elif pos < 170:
pos -= 85
return Color(255 - pos * 3, 0, pos * 3)
else:
pos -= 170
return Color(0, pos * 3, 255 - pos * 3)
def rainbow(self, strip, wait_ms=20, iterations=1):
"""Draw rainbow that fades across all pixels at once."""
for j in range(256 * iterations):
for i in range(strip.numPixels()):
strip.setPixelColor(i, self.wheel((i + j) & 255))
strip.show()
time.sleep(wait_ms / 1000.0)
def rainbowCycle(self, strip, wait_ms=20, iterations=5):
"""Draw rainbow that uniformly distributes itself across all pixels."""
for j in range(256 * iterations):
for i in range(strip.numPixels()):
strip.setPixelColor(i, self.wheel((int(i * 256 / strip.numPixels()) + j) & 255))
strip.show()
time.sleep(wait_ms / 1000.0)
def theaterChaseRainbow(self, strip, wait_ms=50):
"""Rainbow movie theater light style chaser animation."""
for j in range(256):
for q in range(3):
for i in range(0, strip.numPixels(), 3):
strip.setPixelColor(i + q, self.wheel((i + j) % 255))
strip.show()
time.sleep(wait_ms / 1000.0)
for i in range(0, strip.numPixels(), 3):
strip.setPixelColor(i + q, 0)
# Main program logic follows:
if __name__ == '__main__':
driver = LedDriver()
print("Not busy animation")
driver.animate_whilst_not_busy()
driver.start()
time.sleep(10.0)
print("HIBP Lookup animation")
driver.animate_whilst_hibp_lookup()
time.sleep(10.0)
print("Email count result")
driver.show_result_email_count(2)
time.sleep(20.0)
print("Web result")
driver.show_result_web_count(23567)
#time.sleep(10.0)
print("That's it, I'm out of here....")
driver.stop()
|
import json
import time
import hashlib
from random import randrange
from flask import Flask, request, make_response, render_template
from flask_restful import Resource, Api
from sort_db import database
app = Flask(__name__)
api = Api(app)
class SortAPIHelp(Resource):
def get(self):
return {
"Hello" : "This is Sort API using RESTful",
"Method - GET" : {
"/users" : "return users in db",
"/time/<user>" : "return user data(json)",
"/score/<user>" : "return user data(json)"
},
"Method - PUT" : {
"/time/<user>" : "set user data with flag",
"/score/<user>" : "set user data with flag"
}
}
api.add_resource(SortAPIHelp, '/')
db = database()
datas = {}
class Admin(Resource):
def get(self):
return render_template('home.html')
def put(seelf):
return { "x" : "x" }
api.add_resource(Admin, '/admin')
tokens = {}
def outGuest():
guests = db.getGuests()
if (not guests):
return
for guest in guests:
name = guest['name']
if not name in tokens:
db.delUserData(name)
def newGuest():
outGuest()
name = "Guest"
while True:
name += str(randrange(0, 10))
if not name in tokens:
break
db.newGuest(name)
return name
def timestamp():
now = time.localtime()
return "%04d-%02d-%02d %02d:%02d:%02d" % (now.tm_year, now.tm_mon, now.tm_mday, now.tm_hour, now.tm_min, now.tm_sec)
def hasToken(user):
return user in tokens
def getToken(user):
if (not db.isUser(user)):
return None
if (hasToken(user)):
return tokens[user]
key = timestamp() + user + "sortsalt"
tokens[user] = hashlib.sha1(key.encode('utf-8')).hexdigest()
return tokens[user]
def outToken(user):
if (user in tokens):
del tokens[user]
class Time(Resource):
def get(self, user):
isGuest = False
guests = db.getGuests()
for guest in guests:
if (guest['name'] == user):
isGuest = True
break
if not isGuest:
user = str(user.encode('utf-8'))
return db.getUserTable(db.getUserID(user), 'times')
def put(self, user):
if (not request.args):
return { 'return': 'require more(less) argument' }
client = request.args.getlist('client')[0]
if client != "Guest":
user = str(user.encode('utf-8'))
token = request.args.getlist('token')[0]
if not token in tokens.values():
return { 'return': 'Invalidate access' }
ret = 'failed'
data = json.loads(request.data.decode('utf-8'))
if db.setUserData(user, 'times', data['flag'], data['time']):
ret = 'accept'
return { 'return': ret }
api.add_resource(Time, '/time/<string:user>')
class Score(Resource):
def get(self):
return db.getOrderTable('scores', 'score')
def put(self):
if (not request.args.getlist('token') or not request.data):
return { 'return': 'require more(less) argument' }
token = request.args.getlist('token')[0]
if not token in tokens.values():
return { 'return': 'Invalidate access' }
db.newScoreData(json.loads(request.data.decode('utf-8')), timestamp())
return { 'return': "updated" }
api.add_resource(Score, '/score')
class Users(Resource):
def get(self):
id = request.args.getlist('id')
if (id):
return db.getUserTable(int(id[0]), 'users')
users = db.getUsers()
view = []
for user in users:
name = user['user']
view.append({ name : hasToken(name) and "Login" or "Logout"})
return view
def put(self):
if not (request.args.getlist('user') and request.args.getlist('client') and request.args.getlist('flag')):
return { 'return' : 'Invalidate access'}
client = request.args.getlist('client')[0]
user = request.args.getlist('user')[0]
if client != "Guest":
user = str(user.encode('utf-8'))
flag = request.args.getlist('flag')[0]
if flag == 'login':
if client == "Guest":
user = newGuest();
db.newUserData(user, timestamp())
return { 'return': flag, 'token': getToken(user), 'name': user }
if flag == 'logout':
print(user)
if tokens[user] == request.args.getlist('token')[0]:
outToken(user)
return { 'return': flag }
return { 'return' : 'Invalidate access'}
api.add_resource(Users, '/users')
if __name__ == "__main__":
app.run(debug=True, host='0.0.0.0', port=5009)
|
## INFOS ##
name = 'brs_utils'
descr = 'Basic utilities'
url = f'https://github.com/brsynth/{name}'
authors = 'Joan Hérisson'
corr_author = 'joan.herisson@univ-evry.fr'
###########
###########
from setuptools import (
setup,
find_packages
)
from os import path as os_path
here = os_path.dirname(os_path.realpath(__file__))
version_file = os_path.join(here, name, '_version.py')
exec(open(f"{version_file}").read()) # loads __version__
description = open(
os_path.join(
here,
'README.md'
),
encoding='utf-8'
).read()
setup(
name = name,
version = __version__,
author = authors,
author_email = corr_author,
description = descr,
long_description = description,
long_description_content_type = 'text/markdown',
url = url,
packages = find_packages(exclude=""),
# packages = [src_dir],
# package_dir = {package: package},
include_package_data = True,
test_suite = 'pytest',
license = 'MIT',
classifiers = [
'Programming Language :: Python :: 3',
'License :: OSI Approved :: MIT License',
'Operating System :: OS Independent',
],
python_requires = '>=3.7',
)
|
#!/usr/bin/env python
#-*- coding:utf-8 -*-
# Author: Donny You(youansheng@gmail.com)
# Visualize the log files.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import visdom
import time
import numpy as np
import torchvision as tv
from utils.tools.logger import Logger as Log
class VisdomHelper():
'''
Repackage the package visdom.
'''
def __init__(self, env='default', **kwargs):
self.vis = visdom.Visdom(env=env, **kwargs)
# 画的第几个数，相当于横座标
# 保存（’loss',23） 即loss的第23个点
self.global_win_dict = dict()
self.log_text = ''
def reinit(self, env='default', **kwargs):
'''
修改visdom的配置
'''
self.vis = visdom.Visdom(env=env, **kwargs)
return self
def img_many(self, d):
for k, v in d.iteritems():
self.img(k, v)
def plot_line(self, win_name, line_name, x, y):
'''
self.plot('loss',1.00)
'''
if win_name not in self.global_win_dict:
self.global_win_dict[win_name] = self.vis.line(X=np.array([x]),
Y=np.array([y]),
name=line_name,
opts={'legend': [line_name]})
else:
self.vis.updateTrace(X=np.array([x]), Y=np.array([y]),
win=self.global_win_dict[win_name], name=line_name)
def img(self, name, img_):
'''
self.img('input_img',t.Tensor(64,64))
'''
if len(img_.size()) < 3:
img_ = img_.cpu().unsqueeze(0)
self.vis.image(img_.cpu(),
win=unicode(name),
opts=dict(title=name)
)
def img_grid_many(self, d):
for k, v in d.iteritems():
self.img_grid(k, v)
def img_grid(self, name, input_3d):
'''
一个batch的图片转成一个网格图，i.e. input（36，64，64）
会变成 6*6 的网格图，每个格子大小64*64
'''
self.img(name, tv.utils.make_grid(
input_3d.cpu()[0].unsqueeze(1).clamp(max=1, min=0)))
def log(self, info, win='log_text'):
'''
self.log({'loss':1,'lr':0.0001})
'''
self.log_text += ('[{time}] {info} <br>'.format(
time=time.strftime('%m%d_%H%M%S'), \
info=info))
self.vis.text(self.log_text, win='log_text')
def __getattr__(self, name):
return getattr(self.vis, name) |
import time
from timebudget import timebudget
with timebudget('load-file'):
readme = open('README.md','rt').read()
timebudget.report()
|
# Generated by Django 2.2.4 on 2019-08-05 04:28
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('globinq', '0001_initial'),
]
operations = [
migrations.AlterField(
model_name='globin',
name='e7_portal',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='e7_portal', to='globinq.Channel'),
),
migrations.AlterField(
model_name='globin',
name='g8_channel',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='g8_channel', to='globinq.Channel'),
),
migrations.AlterField(
model_name='globin',
name='l_channel',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='l_channel', to='globinq.Channel'),
),
]
|
import sys
print(sys.version)
name = "jc"
print("my name is " + name)
|
from trpg.data_classes import *
def hook(obj):
# TODO: Hook all types
if '__type__' in obj:
obj_type = obj.pop('__type__')
if obj_type == 'scenario':
return Scenario(**obj)
elif obj_type == 'player':
return Player(**obj)
elif obj_type == 'item':
return Item(**obj)
elif obj_type == 'inventory':
return Inventory(**obj)
elif obj_type == 'option':
return Option(**obj)
elif obj_type == 'player_stats':
return PlayerStats(**obj)
elif obj_type == 'resource_stat':
return ResourceStat(**obj)
elif obj_type == 'other_stat':
return OtherStat(**obj)
elif obj_type == 'stat_settings':
return StatSettings(**obj)
elif obj_type == 'stat_setting':
return StatSetting(**obj)
return obj
|
from functools import partial
from math import sqrt
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import string
import timeit
import torch
from torch import nn
import torch.functional as F
import torch.nn.functional as F
import statistics
import re
import warnings
from math import floor
import seaborn as sns
# All the functions in this file have self-explanatory names.
# We have added comments where something is ambigous
def get_device(log = False):
CUDA_ID = None
DEVICE = None
if torch.cuda.is_available():
CUDA_ID = torch.cuda.current_device()
DEVICE = torch.device('cuda') # pylint: disable=maybe-no-member, unused-variable
if log:
print("Running On GPU")
else:
DEVICE = "cpu"
if log:
print("No GPU :(")
return DEVICE
def load_stopwords(filename):
file = open(filename, "r", encoding="utf8")
return [x[:-1] for x in file]
def remove_username(text):
at ='@'
text = ' '.join(list(map(lambda word : word if word[0] != at else ' ', text))).split()
return text
def remove_punctuations(text):
punc = string.punctuation + "…।"
text = ''.join([ch if ch not in punc else ' ' for ch in ' '.join(text)]).split()
return text
def remove_url(text):
text = re.sub('http[s]?://\S+', '', ' '.join(text)) # pylint: disable = anomalous-backslash-in-string
return text.split()
def remove_stopwords(stopwords, text):
text = ' '.join([word.strip() if word.strip() not in stopwords else ' ' for word in text]).split()
return text
def remove_single_char_and_digit(text):
single_let_n_sym = [c for c in string.ascii_letters + string.digits + '”सहगईइ॥√ﷺ》तजओप“मक¸✅उख–भॐर¶°़】•चए—©अऋब' + "৺ঁ্্যঃঅআইঈউঊঋঔকখগঘঙচছজঝঞটঠডঢণতথদধনপফবভমযরশষসক্ষড়ঢ়য়৷т’"]
hidden_char = 8294
text = ' '.join([' ' if (word in single_let_n_sym) or (len(word) == 1 and ord(word) == hidden_char) else word for word in text]).split()
return text
def preprocess(texts, stopwords, log = True):
if log:
print("Preprocessing started")
start = timeit.default_timer()
remove_stopwords_callback = partial(remove_stopwords, stopwords)
texts = list(map(remove_username, texts))
texts = list(map(remove_url, texts))
texts = list(map(remove_punctuations, texts))
texts = list(map(remove_stopwords_callback, texts))
texts = list(map(remove_single_char_and_digit, texts))
texts = list(map(lambda text : [word.lower() for word in text], texts))
stop = timeit.default_timer()
if log:
print("Preprocessing finished.\nPreprocessing Took " + str(stop - start) + " Seconds\n\n")
return texts
def build_vocab(texts, padding = '___PAD___', unknown = '___UNK___'):
return [padding] + [unknown] + list(set(word for text in texts for word in text))
def get_padding_index(V, PADDING):
return V.index(PADDING)
def word_to_one_hot(word_idx, ONE_HOT_VECTOR_SIZE):
v = [0] * ONE_HOT_VECTOR_SIZE
v[word_idx] = 1
return v
def convert_word_to_index(V): # assigns an index to each word of the vocabulary
w2idx = {}
for idx, word in enumerate(V):
w2idx[word] = idx
return w2idx
def batch_word_to_one_hot(batch, ONE_HOT_VECTOR_SIZE): # to produce batches of one hot
one_hot_batch = []
for idx_tensor in batch:
idx = idx_tensor.item()
x = word_to_one_hot(int(idx), ONE_HOT_VECTOR_SIZE)
one_hot_batch.append(x)
return torch.Tensor(one_hot_batch)
def summary_stat(dataf, sentences): # plots distributions and sumaary statistics
dataf["preprocessed_sen_len"] = [len(text) for text in sentences]
print(dataf.preprocessed_sen_len.describe())
plt.hist(list(dataf.preprocessed_sen_len), bins= 100, density = True, cumulative = True, label = 'CDF: Preprocessed Sentence Lenght', histtype='step', alpha=0.55, color='purple')
plt.show()
sns.distplot(dataf.preprocessed_sen_len, hist = True, kde = True,
bins = int(180/5), color = 'darkblue',
hist_kws={'edgecolor':'black'},
kde_kws={'linewidth': 4},
axlabel = "Preprocessed Sentence Length")
print("")
def save_model(model, filename):
torch.save(model.state_dict(), filename)
def load_model(model, filename):
model.load_state_dict(torch.load(filename), strict=False)
# Calculates the number of times each word appears in all the sentences and finally devides by the total number of words
def calc_rel_freq(V, texts, ONE_HOT_VECTOR_SIZE, word_to_index):
rel_freq = [0.] * ONE_HOT_VECTOR_SIZE
total = 0.
for word in V:
cnt = 0.
for text in texts:
cnt += text.count(word)
rel_freq[word_to_index[word]] = cnt
total += cnt
for word in V:
rel_freq[word_to_index[word]] = rel_freq[word_to_index[word]] / total
return rel_freq
def plot_embedding_losses(losses):
plt.plot(losses)
plt.xlabel("Number Of Epochs")
plt.ylabel("Training Loss")
plt.show()
print("\n\n")
def sampling_prob(word_ind, rel_freq):
rf = rel_freq[word_ind]
return (sqrt(rf / 0.001) + 1.) * (0.001 / rf)
# creates center, context pair for the given window size consider the sampling probablity
def get_target_context(sentence, word_to_index, rel_freq, window_size = 2):
sentence_to_indices = [word_to_index[word] for word in sentence]
sentence_len = len(sentence_to_indices)
for center_ind in range(sentence_len):
for context_i in range(-window_size, window_size + 1):
context_ind = center_ind + context_i
if context_ind == center_ind \
or context_ind < 0 \
or context_ind >= sentence_len:
continue
if np.random.random() < sampling_prob(sentence_to_indices[context_ind], rel_freq):
yield (sentence_to_indices[center_ind], sentence_to_indices[context_ind])
# Helper method to plot training and validation loss and accuracy
def plot_losses(train_losses, train_accs, valid_losses, valid_accs):
checkpoint = valid_losses.index(min(valid_losses))
xsteps = int(len(train_losses)/10)
fig, axs = plt.subplots(1, 2, figsize=(12,6))
axs[0].plot(train_losses, label = "Training")
axs[0].plot(valid_losses, label = "Validation")
axs[0].axvline(x = checkpoint, label = "Early Stopping Check Point", c = "red", ls = "--")
axs[0].set(xlabel='Number of epochs', ylabel='Loss', title = "Train/Validtion Loss Per Epoch")
axs[1].plot(train_accs, label = "Training")
axs[1].plot(valid_accs, label = "Validation")
axs[1].axvline(x = checkpoint, label = "Early Stopping Check Point", c = "red", ls = "--")
axs[1].set(xlabel='Number of epochs', ylabel='Accuracy', title = "Train/Validtion Accuracy Per Epoch")
fig.tight_layout()
plt.legend()
plt.show()
# Helper method to store training losses and the model to a file
def save_training_info(train_losses, train_accs, valid_losses, valid_accs, model, path):
with open(path, 'w') as f:
print(' '.join([str(ls) for ls in train_losses]), file = f)
print(' '.join([str(ls) for ls in train_accs]), file = f)
print(' '.join([str(ls) for ls in valid_losses]), file = f)
print(' '.join([str(ls) for ls in valid_accs]), file = f)
print(model, file = f)
print(path, file = f)
print("\nTraining Session Info Stored")
# Given the initial input shape of the first convolution layer, and convolution parameters,
# it calculates the output shape of the final convolution layer.
# We use this method to calculate number of neurons of the first fully connected layer
def conv_out_shape(params, h, w):
# For EncoderCNN, h: sent_len , w: hidden_weights_dim * (2 if bidirectional lstm else 1)
# For TransformerCNN: h: sent_len, w: embedding_size
conv_params = params["conv_params"]
cnt = 0
for conv in conv_params:
h = int(floor(h - conv[2][0] + (2 * conv[4]) / conv[3])) + 1
w = int(floor(w - conv[2][1] + (2 * conv[4]) / conv[3])) + 1
cnt += 1
if cnt != len(conv_params): # no max pool after the last conv layer, so it will not be devided by 2
h = int(floor(h/2))
w = int(floor(w/2))
print(f"Output shape after conv{cnt}: {(h, w)}")
return (h, w) |
class Simple1Exception(Exception):
pass
class Simple2Exception(Exception):
pass
def abc():
try:
raise Simple1Exception
except Simple1Exception as e:
raise Simple2Exception from e
abc()
|
import os
import requests
class ApiCore(object):
def __init__(self):
self.api_key = {"appid":os.environ["API_KEY"]}
self.base_url = os.environ["BASE_URL"]
self.weather_url = os.environ["WEATHER_URL"]
self.several_cities = os.environ["SERVAL_CITIES"]
self.cities_in_cycle = os.environ["CITIES_IN_CYCLE"]
self.several_city_ids = os.environ["SEVERAL_CITY_IDS"]
def get(self,query,api_url):
return requests.get(f"{self.base_url}{api_url}", params={**self.api_key,**query}) |
import logging
from django.contrib import messages
from django.core.exceptions import ValidationError
from django.core.urlresolvers import reverse
from django.views.generic.edit import FormView
from ipware.ip import get_ip
from knownly import plans
from knownly.billing.errors import PaymentProviderError
from knownly.billing.forms import SubscriptionPlanForm
from knownly.billing.services import CustomerBillingService
from knownly.plans.services import CustomerSubscriptionService
logger = logging.getLogger(__name__)
class PlansView(FormView):
form_class = SubscriptionPlanForm
template_name = 'billing/plans.html'
mode = 'plans'
FREE_PLAN_SUCCSS_MESSAGE = 'Thanks for joining Knownly!'
PAYMENT_SUCCESS_MESSAGE = 'Thanks for joining Knownly!'
def get_success_url(self):
return reverse('console')
def get_context_data(self, **kwargs):
context_data = super(PlansView, self).get_context_data(**kwargs)
context_data['mode'] = self.mode
return context_data
def form_valid(self, form):
# 3 parts to this form: the plan, billing info, and billing period
selected_plan = form.cleaned_data['knownly_plan']
if selected_plan in [plans.LITE, plans.PREMIUM]:
billing_details = form.cleaned_data
billing_details['ip_address'] = get_ip(self.request)
# Update the customer's billing details
try:
cust_billing_service = \
CustomerBillingService(self.request.user)
cust_billing_service.update_billing_details(billing_details)
cust_billing_service.update_subscription(
selected_plan, billing_details['period'])
except PaymentProviderError:
logger.exception('Payment Provider error encountered '
'while creating customer: %s',
self.request.user)
form.add_error('__all__',
ValidationError('There was a problem '
'problem validating your '
'credit card.'))
return super(PlansView, self).form_invalid(form)
except Exception:
logger.exception('Payment Provider error encountered while '
'creating customer: %s', self.request.user)
form.add_error('__all__',
ValidationError('There was a problem updating '
'your subscription. We will '
'look into it and be in '
'contact with you.'))
return super(PlansView, self).form_invalid(form)
cust_subs_service = CustomerSubscriptionService(self.request.user)
if cust_subs_service.has_current_subscription():
subscription = cust_subs_service.get_current_subscription()
if subscription.current_plan != plans.FREE \
and selected_plan != subscription.current_plan:
logger.exception('We haven\'t built support for changing a '
'paid plan yet. Attempted by %s',
self.request.user)
form.add_error('__all__',
ValidationError('Please contact us '
'(info@knownly.net) to arrange '
'a change in your plan.'))
return super(PlansView, self).form_invalid(form)
try:
cust_subs_service.create_or_update_subscription(
plan=selected_plan, reason='Customer selected plan')
if selected_plan == plans.FREE:
messages.add_message(self.request, messages.SUCCESS,
self.FREE_PLAN_SUCCSS_MESSAGE)
else:
messages.add_message(self.request, messages.SUCCESS,
self.PAYMENT_SUCCESS_MESSAGE)
except PaymentProviderError:
logger.exception('Payment Provider error encountered while '
'updating subscription for customer: %s',
self.request.user)
form.add_error('__all__',
ValidationError('There was a problem updating your '
'subscription. We will look into '
'it and be in contact with you.'))
return super(PlansView, self).form_invalid(form)
except Exception:
logger.exception('Payment Provider error encountered while '
'updating subscription for customer: %s',
self.request.user)
form.add_error('__all__',
ValidationError('There was a problem updating your '
'subscription. We will look into '
'it and be in contact with you.'))
return super(PlansView, self).form_invalid(form)
return super(PlansView, self).form_valid(form)
def form_invalid(self, form):
logger.debug(form.errors)
return super(PlansView, self).form_invalid(form)
|
def l():print('\n'+'=-'*30 + '=')
ns = [[], []]
n = 0
for i in range(0, 7):
n = int(input('digite um numero: '))
if n % 2 == 0:
ns[0].append(n)
else:
ns[1].append(n)
l()
ns[0].sort()
ns[1].sort()
l():
print('valores pares:\n')
for i in ns[0]:
print(' ', i, end='')
l()
print('\nvalores impares:\n')
for i in ns[1]:
print(' ', i, end='')
|
# coding=utf-8
import codecs
import os
import scrapy
import datetime
import csv
from scrapy import Request
from selenium import webdriver
import sys
reload(sys)
sys.setdefaultencoding('utf8')
class IdeaSpider(scrapy.Spider):
name = "idea"
allowed_domains = ["www.writepop.com"]
def __init__(self, *args, **kwargs):
super(IdeaSpider, self).__init__(*args, **kwargs)
self.start_urls = ["http://www.writepop.com/category/1001-story-ideas"]
option = webdriver.ChromeOptions()
option.add_argument("--user-agent=Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Ubuntu Chromium/59.0.3071.109 Chrome/59.0.3071.109 Safari/537.36")
self.browser = webdriver.Chrome(options = option, executable_path = "C:\\Program Files (x86)\\Google\\Chrome\\Application\\chromedriver.exe")
self.item_list = []
self.count = 0
self.file = None
if not os.path.exists("ideas"):
os.mkdir("ideas")
def destroy_browser(self):
self.browser.quit()
def parse(self, response):
result_list = response.css("#post-476 .entry ul>li")
for item in result_list[:-1]:
# print item.css("a::attr(href)").extract()[0]
yield Request(item.css("a::attr(href)").extract()[0], callback = self.parse_ideas)
def parse_ideas(self, response):
def get_tag_name(html):
return html[1:html.find(">")]
import re
pattern = re.compile("[^a-zA-Z0-9.…(!)]")
pattern_2 = re.compile(" +")
category = re.sub(pattern = pattern, string = response.css(".entry h2::text").extract()[0].replace("/", "or"), repl = " ")
category = re.sub(pattern = pattern_2, string = category, repl = " ").lstrip().rstrip()
# print category
if not os.path.exists("ideas/%s" % category):
os.mkdir("ideas/%s" % category)
contents = response.css(".entry>*")
for item in contents:
if get_tag_name(item.extract()) == "h3" and len(item.xpath("./text()").extract()) != 0:
if self.file is not None and not self.file.closed:
self.file.close()
filename = re.sub(pattern = pattern, string = item.xpath("./text()").extract()[0], repl = " ")
filename = re.sub(pattern = pattern_2, string = filename, repl = " ").lstrip().rstrip()
self.file = open("ideas/%s/%s.txt" % (category, filename), "wb")
elif get_tag_name(item.extract()) == "ul":
unfinish = True
while unfinish:
try:
if item.css("ul") is not None:
contents = item.css("ul").css("li::text").extract()
else:
contents = item.css("li::text").extract()
print category, len(contents), type(contents)
self.count += len(contents)
unfinish = False
except IOError:
unfinish = True
continue
for content in contents:
self.file.write(content + "\n\n")
def closed(self, reason):
self.destroy_browser()
print self.count
print reason |
"""
Models for interfacing asynchronous programs with pretty CLI output.
"""
|
import unittest
from basketball_reference_scraper.drafts import get_draft_class
class TestDraft(unittest.TestCase):
def test_should_get_class(self):
df = get_draft_class(2003)
self.assertEqual(len(df), 58)
row = df.iloc[0]
self.assertEqual('LeBron James', row['PLAYER'])
def test_should_get_old_class(self):
df = get_draft_class(1987)
self.assertEqual(len(df), 161)
def test_should_handle_forfeit(self):
df = get_draft_class(2002)
self.assertEqual(len(df), 57)
row = df.iloc[27]
self.assertEqual('28', row['PICK'])
row = df.iloc[28]
self.assertEqual('30', row['PICK']) |
#!/usr/bin/env python
import os
from cli import parser
from __init__ import MusicFile
ACOUSTID_API_KEY = 'cSpUJKpD'
def main():
args = parser.parse_args()
if args.current_dir:
files = [x for x in os.listdir(os.getcwd()) if x.endswith('.mp3')]
else:
files = args.files
for file in files:
print "[META] Proccesing file: %s..." % file
mf = MusicFile(file)
mf.lookup(ACOUSTID_API_KEY)
print "[META] Found details: %s - %s" % (mf.artist, mf.song_name)
if not mf.album:
print "[META] Could not find album for the song"
print "[META] Copying to: %s" % args.out_dir
mf.copy_to(args.out_dir)
print "[META] Tagging mp3"
mf.tag()
print "[META] Done."
if __name__ == '__main__':
main()
|
import re
import six
from lxml import etree
from sbedecoder.message import SBEMessage, TypeMessageField, EnumMessageField, SetMessageField, CompositeMessageField, \
SBERepeatingGroupContainer
def convert_to_underscore(name):
name = name.strip('@').strip('#')
s1 = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', name)
return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s1).lower()
class SBESchema(object):
def __init__(self, include_message_size_header=False, use_description_as_message_name=False):
self.messages = []
self.include_message_size_header = include_message_size_header
self.use_description_as_message_name = use_description_as_message_name
self.initial_types = {
"char": {"children": [], "description": "char", "name": "char", "primitive_type": "char", "type": "type"},
"int": {"children": [], "description": "int", "name": "int", "primitive_type": "int32", "type": "type"},
"int8": {"children": [], "description": "int8", "name": "int8", "primitive_type": "int8", "type": "type"},
"int16": {"children": [], "description": "int16", "name": "int16", "primitive_type": "int16", "type": "type"},
"int32": {"children": [], "description": "int32", "name": "int32", "primitive_type": "int32", "type": "type"},
"int64": {"children": [], "description": "int64", "name": "int64", "primitive_type": "int64", "type": "type"},
"uint8": {"children": [], "description": "uint8", "name": "uint8", "primitive_type": "uint8", "type": "type"},
"uint16": {"children": [], "description": "uint16", "name": "uint16", "primitive_type": "uint16", "type": "type"},
"uint32": {"children": [], "description": "uint32", "name": "uint32", "primitive_type": "uint32", "type": "type"},
"uint64": {"children": [], "description": "uint64", "name": "uint64", "primitive_type": "uint64", "type": "type"},
"float": {"children": [], "description": "float", "name": "float", "primitive_type": "float", "type": "type"},
"double": {"children": [], "description": "double", "name": "double", "primitive_type": "double", "type": "type"}
}
self.type_map = {}
self.message_map = {}
self.primitive_type_map = {
'char': ('c', 1),
'int': ('i', 4),
'int8': ('b', 1),
'int16': ('h', 2),
'int32': ('i', 4),
'int64': ('q', 8),
'uint8': ('B', 1),
'uint16': ('H', 2),
'uint32': ('I', 4),
'uint64': ('Q', 8),
'float': ('f', 4),
'double': ('d', 8),
}
@staticmethod
def _build_type_definition(type_definition):
type_configuration = dict((convert_to_underscore(x[0]), x[1]) for x in type_definition.items())
type_configuration['type'] = type_definition.tag
if type_definition.text:
type_configuration['text'] = type_definition.text.strip()
children_types = []
for child in type_definition.getchildren():
child_configuration = dict((convert_to_underscore(x[0]), x[1]) for x in child.items())
child_configuration['type'] = child.tag
if child.text:
child_configuration['text'] = child.text.strip()
children_types.append(child_configuration)
type_configuration['children'] = children_types
return type_configuration
def _parse_types(self, xml_file, types_tag='types'):
type_map = self.initial_types
with open(xml_file, 'rb') as input_schema_file:
xml_context = etree.iterparse(input_schema_file, tag=types_tag, remove_comments=True)
for action, elem in xml_context:
# Now parse all the children under the types tag
for type_def in elem.getchildren():
new_type = self._build_type_definition(type_def)
type_map[new_type['name']] = new_type
return type_map
@staticmethod
def _parse_messages(xml_file, message_tag='message'):
messages = []
with open(xml_file, 'rb') as input_schema_file:
xml_context = etree.iterparse(input_schema_file)
for action, elem in xml_context:
local_name = etree.QName(elem.tag).localname
if local_name == message_tag:
message_definition = dict((convert_to_underscore(x[0]), x[1]) for x in elem.items())
SBESchema._parse_message_elements(elem, message_definition)
messages.append(message_definition)
return messages
@staticmethod
def _parse_message_elements(elements, definition):
fields = []
groups = []
for child in elements.getchildren():
if child.tag == 'field':
field = dict((convert_to_underscore(x[0]), x[1]) for x in child.items())
field['converted_name'] = convert_to_underscore(field['name'])
fields.append(field)
elif child.tag == 'group':
group = dict((convert_to_underscore(x[0]), x[1]) for x in child.items())
SBESchema._parse_message_elements(child, group)
group['converted_name'] = convert_to_underscore(group['name'])
groups.append(group)
definition['fields'] = fields
definition['groups'] = groups
def _build_message_field(self, field_definition, offset, header_size=10, endian='<', add_header_size=True):
field_original_name = field_definition['name']
field_name = convert_to_underscore(field_original_name)
field_id = field_definition['id']
field_description = field_definition.get('description', '')
field_type = self.type_map[field_definition['type']]
field_type_type = field_type['type']
field_semantic_type = field_definition.get('semantic_type', None)
field_since_version = int(field_definition.get('since_version','0'))
message_field = None
if field_type_type == 'type':
is_string_type = field_type['primitive_type'] == 'char' and 'length' in field_type and int(
field_type['length']) > 1
field_offset = offset
if field_definition.get('offset', None) is not None:
field_offset = int(field_definition.get('offset', None))
if add_header_size:
field_offset += header_size
primitive_type_fmt, primitive_type_size = self.primitive_type_map[field_type['primitive_type']]
field_length = field_type.get('length', None)
if field_length is not None:
field_length = int(field_length)
if is_string_type:
unpack_fmt = '%ds' % field_length # unpack as string (which may be null-terminated if shorter)
else:
unpack_fmt = '%s%s%s' % (endian, str(field_length), primitive_type_fmt)
else:
# Field length is just the primitive type length
field_length = primitive_type_size
unpack_fmt = '%s%s' % (endian, primitive_type_fmt)
constant = None
optional = False
if 'presence' in field_type:
if field_type['presence'] == 'constant':
constant_prim_type = field_type['primitive_type']
if constant_prim_type == 'char':
constant = str(field_type['text'])
else:
constant = int(field_type['text'])
elif field_type['presence'] == 'optional':
optional = True
null_value = None
if 'null_value' in field_type:
null_value = int(field_type['null_value'])
message_field = TypeMessageField(name=field_name, original_name=field_original_name,
id=field_id, description=field_description,
unpack_fmt=unpack_fmt, field_offset=field_offset,
field_length=field_length, null_value=null_value,
constant=constant, optional=optional,
is_string_type=is_string_type,
semantic_type=field_semantic_type,
since_version=field_since_version)
elif field_type_type == 'enum':
encoding_type = field_type['encoding_type']
encoding_type_type = self.type_map[encoding_type]
primitive_type_fmt, primitive_type_size = self.primitive_type_map[encoding_type_type['primitive_type']]
field_offset = offset
if field_definition.get('offset', None) is not None:
field_offset = int(field_definition.get('offset', None))
if add_header_size:
field_offset += header_size
unpack_fmt = endian
field_length = field_type.get('length', None)
if field_length is not None:
field_length = int(field_length)
for i in range(field_length):
unpack_fmt += primitive_type_fmt
else:
# Field length is just the primitive type length
field_length = primitive_type_size
unpack_fmt += primitive_type_fmt
enum_values = field_type['children']
message_field = EnumMessageField(name=field_name,
original_name=field_original_name,
id=field_id,
description=field_description,
unpack_fmt=unpack_fmt,
field_offset=field_offset,
enum_values=enum_values,
field_length=field_length,
semantic_type=field_semantic_type,
since_version=field_since_version)
elif field_type_type == 'set':
encoding_type = field_type['encoding_type']
encoding_type_type = self.type_map[encoding_type]
primitive_type_fmt, primitive_type_size = self.primitive_type_map[encoding_type_type['primitive_type']]
field_offset = offset
if field_definition.get('offset', None) is not None:
field_offset = int(field_definition.get('offset', None))
if add_header_size:
field_offset += header_size
unpack_fmt = endian
field_length = field_type.get('length', None)
if field_length is not None:
field_length = int(field_length)
for i in range(field_length):
unpack_fmt += primitive_type_fmt
else:
# Field length is just the primitive type length
field_length = primitive_type_size
unpack_fmt += primitive_type_fmt
choice_values = field_type['children']
message_field = SetMessageField(name=field_name, original_name=field_original_name,
id=field_id, description=field_description, unpack_fmt=unpack_fmt,
field_offset=field_offset, choices=choice_values, field_length=field_length,
semantic_type=field_semantic_type, since_version=field_since_version)
elif field_type_type == 'composite':
composite_parts = []
field_offset = offset
if field_definition.get('offset', None) is not None:
field_offset = int(field_definition.get('offset', None))
if add_header_size:
field_offset += header_size
float_composite = False
field_length = 0
for child in field_type['children']:
primitive_type_fmt, primitive_type_size = self.primitive_type_map[child['primitive_type']]
unpack_fmt = endian + primitive_type_fmt
child_since_version = int(child.get('since_version', '0'))
constant = None
optional = False
if 'presence' in child:
if child['presence'] == 'constant':
constant_prim_type = child['primitive_type']
if constant_prim_type == 'char':
constant = str(child['text'])
else:
constant = int(child['text'])
elif child['presence'] == 'optional':
optional = True
null_value = None
if 'null_value' in child:
null_value = int(child['null_value'])
# If a 'mantissa' field exists, assume we are working with a floating point value
if child['name'] == 'mantissa':
float_composite = True
composite_field = TypeMessageField(name=child['name'], original_name=child['name'],
description=child.get('description', ''),
unpack_fmt=unpack_fmt, field_offset=field_offset,
field_length=primitive_type_size,
null_value=null_value, constant=constant,
optional=optional, semantic_type=field_semantic_type,
since_version=child_since_version)
field_offset += primitive_type_size
field_length += primitive_type_size
composite_parts.append(composite_field)
message_field = CompositeMessageField(name=field_name, original_name=field_original_name,
id=field_id, description=field_description,
field_offset=field_offset, field_length=field_length,
parts=composite_parts,
float_value=float_composite, semantic_type=field_semantic_type,
since_version=field_since_version)
return message_field
def get_message_type(self, template_id):
return self.message_map.get(template_id, None)
def _determine_field_length(self, field):
field_type = field.get('primitive_type', field['type'])
if field_type in self.primitive_type_map:
return self.primitive_type_map[field_type][1] #second value is byte size
else:
field_def = self.type_map[field['type']]
if 'encoding_type' in field_def and field_def['encoding_type'] in self.primitive_type_map:
return self.primitive_type_map[field_def['encoding_type']][1]
#otherwise it's a regular composite field
block_length = 0
for child_field in field_def['children']:
block_length += self._determine_field_length(child_field)
return block_length
def _determine_block_length(self, message):
if 'block_length' in message:
return int(message['block_length'])
# block length was not defined but we should be able to calculate it by adding
# length of types up until we hit the first var field or repeating group
block_length = 0
for field in message['fields']:
block_length += self._determine_field_length(field)
return block_length
def _construct_header(self, message):
field_offset = 0
# All messages start with a message size field
message_id = int(message['id'])
schema_block_length = self._determine_block_length(message)
type_name = message['description'] if self.use_description_as_message_name else message['name']
message_type = type(type_name, (SBEMessage,), {'message_id': message_id,
'schema_block_length': schema_block_length})
self.message_map[message_id] = message_type
setattr(message_type, 'fields', [])
# If messages start with a message size field
if self.include_message_size_header:
message_size_field = TypeMessageField(name='message_size', original_name='message_size',
description="Header Message Size",
unpack_fmt='<H', field_offset=field_offset, field_length=2)
field_offset += message_size_field.field_length
message_type.fields.append(message_size_field)
setattr(message_type, 'message_size', message_size_field)
# Now grab the messageHeader type, it has to exist and populate the remaining header fields
message_header_type = self.type_map['messageHeader']
for header_field_type in message_header_type.get('children', []):
primitive_type_fmt, primitive_type_size = self.primitive_type_map[header_field_type['primitive_type']]
message_header_field = TypeMessageField(name=convert_to_underscore(header_field_type['name']),
original_name=header_field_type['name'],
description='Header ' + header_field_type['name'],
unpack_fmt=primitive_type_fmt,
field_offset=field_offset,
field_length=primitive_type_size)
field_offset += message_header_field.field_length
message_type.fields.append(message_header_field)
setattr(message_type, message_header_field.name, message_header_field)
setattr(message_type, 'header_size', field_offset)
return field_offset
def _add_fields(self, field_offset, entity, entity_type, endian, add_header_size=True, header_size=10):
# Now run through the remaining types and update the fields
for field_type in entity.get('fields', []):
field_type['offset'] = None
field = self._build_message_field(field_type, field_offset, header_size=header_size, endian=endian,
add_header_size=add_header_size)
field_offset += field.field_length
entity_type.fields.append(field)
# make it an attribute too
setattr(entity_type, field.name, field)
def _add_groups(self, entity, entity_type, endian):
# Now figure out the message groups
repeating_groups = []
for group_type in entity.get('groups', []):
group_name = convert_to_underscore(group_type['name'])
group_original_name = group_type['name']
group_since_version = int(group_type.get('since_version','0'))
dimension_type = self.type_map[group_type['dimension_type']]
# There are two fields we care about, block_length and num_in_group
block_length_field = None
num_in_group_field = None
block_field_offset = 0
for child in dimension_type['children']:
if child['name'] == 'blockLength':
primitive_type = child['primitive_type']
primitive_type_fmt, primitive_type_size = self.primitive_type_map[primitive_type]
block_length_field = TypeMessageField(name=convert_to_underscore(child['name']),
original_name=child['name'],
description=child['name'],
unpack_fmt=endian + primitive_type_fmt,
field_offset=block_field_offset,
field_length=primitive_type_size,
semantic_type=child.get('semantic_type'))
block_field_offset += primitive_type_size
elif child['name'] == 'numInGroup':
primitive_type = child['primitive_type']
if 'offset' in child:
block_field_offset = int(child['offset'])
primitive_type_fmt, primitive_type_size = self.primitive_type_map[primitive_type]
num_in_group_field = TypeMessageField(name=convert_to_underscore(child['name']),
original_name=child['name'],
description=child['name'],
unpack_fmt=endian + primitive_type_fmt,
field_offset=block_field_offset,
field_length=primitive_type_size,
semantic_type=child.get('semantic_type'))
block_field_offset += primitive_type_size
group_field_offset = 0
repeating_group = SBERepeatingGroupContainer(name=group_name, original_name=group_original_name,
id=int(group_type['id']),
block_length_field=block_length_field,
num_in_group_field=num_in_group_field,
dimension_size=block_field_offset,
since_version=group_since_version)
self._add_fields(group_field_offset, group_type, repeating_group, endian, add_header_size=False)
repeating_groups.append(repeating_group)
setattr(entity_type, repeating_group.name, repeating_group)
# handle nested groups
self._add_groups(group_type, repeating_group, endian)
setattr(entity_type, 'groups', repeating_groups)
def _construct_body(self, message, field_offset, endian):
message_id = int(message['id'])
message_type = self.get_message_type(message_id)
self._add_fields(field_offset, message, message_type, endian, add_header_size=True,
header_size=message_type.header_size)
self._add_groups(message, message_type, endian)
def parse(self, xml_file, message_tag="message", types_tag="types", endian='<'):
self.type_map = self._parse_types(xml_file, types_tag=types_tag)
self.messages = self._parse_messages(xml_file, message_tag=message_tag)
# Now construct each message with its expected field types
for message in self.messages:
field_offset = self._construct_header(message)
self._construct_body(message, field_offset, endian)
def load(self, messages):
self.messages = messages
self.message_map = dict((m.message_id, m) for m in messages)
class MDPSchema(SBESchema):
def __init__(self):
super(MDPSchema, self).__init__(include_message_size_header=True, use_description_as_message_name=True)
|
import numpy as np
from eval_instance_segmentation_coco import eval_instance_segmentation_coco
# with open('data/fake.pkl', 'rb') as f:
# bboxes, masks, labels, scores, keys = pickle.load(f)
#
# with open('data/fake_gt.pkl', 'rb') as f:
# sizes, gt_bboxes, gt_masks, gt_labels, gt_crowdeds, gt_areas = pickle.load(f)
pred = np.load('data/coco_instance_segm_result_val2014_fakesegm100.npz')
bboxes = pred['bboxes']
masks = pred['masks']
labels = pred['labels']
scores = pred['scores']
gt = np.load('data/coco_instance_segm_dataset_val2014_fakesegm100.npz')
sizes = gt['sizes']
gt_bboxes = gt['bboxes']
gt_masks = gt['masks']
gt_labels = gt['labels']
gt_crowdeds = gt['crowdeds']
gt_areas = gt['areas']
results = eval_instance_segmentation_coco(
sizes, bboxes, masks, labels, scores, gt_bboxes, gt_masks, gt_labels, gt_crowdeds, gt_areas)
keys = ['ap/iou=0.50:0.95/area=all/maxDets=100',
'ap/iou=0.50/area=all/maxDets=100',
'ap/iou=0.75/area=all/maxDets=100',
'ap/iou=0.50:0.95/area=small/maxDets=100',
'ap/iou=0.50:0.95/area=medium/maxDets=100',
'ap/iou=0.50:0.95/area=large/maxDets=100',
'ar/iou=0.50:0.95/area=all/maxDets=1',
'ar/iou=0.50:0.95/area=all/maxDets=10',
'ar/iou=0.50:0.95/area=all/maxDets=100',
'ar/iou=0.50:0.95/area=small/maxDets=100',
'ar/iou=0.50:0.95/area=medium/maxDets=100',
'ar/iou=0.50:0.95/area=large/maxDets=100',
]
for key in keys:
print('m' + key, results['m' + key])
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from lck.django.common import nested_commit_on_success
from ralph.util import plugin
from ralph.discovery.models import IPAddress
from ralph.discovery.http import get_http_family
@nested_commit_on_success
def run_http(ip):
family = get_http_family(ip)
ip_address, created = IPAddress.concurrent_get_or_create(address=ip)
ip_address.http_family = family
ip_address.save(update_last_seen=True)
return family
@plugin.register(chain='discovery', requires=['ping'], priority=201)
def http(**kwargs):
ip = str(kwargs['ip'])
try:
name = run_http(ip)
except Exception as e:
if hasattr(e, 'code') and hasattr(e, 'reason'):
message = 'Error %s: %s (%s)' % (e.code, e.reason)
else:
message = 'Error: %s' % unicode(e)
return True, message, kwargs
kwargs['http_family'] = name
return True, name, kwargs
|
import logging
from qcodes.instrument.base import Instrument
from qcodes.utils import validators as vals
class IQ_mixer(Instrument):
'''
Passive object designed to hold paramters for the mixer.
'''
def __init__(self, name, **kw):
logging.info(__name__ + ': Initializing instrument')
super().__init__(name, **kw)
# Qubit parameters
self.add_parameter('QI_amp_ratio',
set_cmd=self._set_QI_amp_ratio,
get_cmd=self._get_QI_amp_ratio,
vals=vals.Numbers(0, 2))
self.add_parameter('IQ_phase_skewness', unit='deg',
get_cmd=self._get_IQ_phase_skewness,
set_cmd=self._set_IQ_phase_skewness,
vals=vals.Numbers(0, 360))
self.IQ_phase_skewness.set(0)
self.QI_amp_ratio.set(1)
def _get_QI_amp_ratio(self):
return self._QI_amp_ratio
def _set_QI_amp_ratio(self, QI_amp_ratio):
self._QI_amp_ratio = QI_amp_ratio
def _get_IQ_phase_skewness(self):
return self._IQ_phase_skewness
def _set_IQ_phase_skewness(self, IQ_phase_skewness):
self._IQ_phase_skewness = IQ_phase_skewness
|
import rename
import rss
import transfer
urls = rss.part_a()
rss.part_b(urls)
rename.main()
transfer.main() |
import pytest
from lumido import Cronjob
@pytest.mark.parametrize('mins, hours, result', [(30, 1, (130, 'tomorrow')), (45, '*', (1645, 'today')),
('*', '*', (1610, 'today')), ('*', 19, (1900, 'today'))])
def test_next(mins, hours, result):
cron = Cronjob(mins, hours)
assert cron._next(1610) == result
|
# Copyright 2018 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Parses VCF files (version 4.x) and converts them to Variant objects.
The 4.2 spec is available at https://samtools.github.io/hts-specs/VCFv4.2.pdf.
"""
from __future__ import absolute_import
import logging
import os
import tempfile
from collections import namedtuple
try:
from nucleus.io.python import vcf_reader as nucleus_vcf_reader
from nucleus.protos import variants_pb2
except ImportError:
logging.warning('Nucleus is not installed. Cannot use the Nucleus parser.')
import vcf
from apache_beam.coders import coders
from apache_beam.io import filesystems
from apache_beam.io import textio
from gcp_variant_transforms.beam_io import bgzf
# Stores data about failed VCF record reads. `line` is the text line that
# caused the failed read and `file_name` is the name of the file that the read
# failed in.
MalformedVcfRecord = namedtuple('MalformedVcfRecord',
['file_name', 'line', 'error'])
FIELD_COUNT_ALTERNATE_ALLELE = 'A' # Indicates one value for each alternate
# allele.
MISSING_FIELD_VALUE = '.' # Indicates field is missing in VCF record.
PASS_FILTER = 'PASS' # Indicates that all filters have been passed.
END_INFO_KEY = 'END' # The info key that explicitly specifies end of a record.
GENOTYPE_FORMAT_KEY = 'GT' # The genotype format key in a call.
PHASESET_FORMAT_KEY = 'PS' # The phaseset format key.
DEFAULT_PHASESET_VALUE = '*' # Default phaseset value if call is phased, but
# no 'PS' is present.
MISSING_GENOTYPE_VALUE = -1 # Genotype to use when '.' is used in GT field.
FILE_FORMAT_HEADER_TEMPLATE = '##fileformat=VCFv{VERSION}'
class Variant(object):
"""A class to store info about a genomic variant.
Each object corresponds to a single record in a VCF file.
"""
def __init__(self,
reference_name=None, # type: str
start=None, # type: int
end=None, # type: int
reference_bases=None, # type: str
alternate_bases=None, # type: List[str]
names=None, # type: List[str]
quality=None, # type: float
filters=None, # type: List[str]
info=None, # type: Dict[str, Any]
calls=None # type: List[VariantCall]
):
# type: (...) -> None
"""Initialize the ``Variant`` object.
Args:
reference_name: The reference on which this variant occurs (such as
`chr20` or `X`).
start: The position at which this variant occurs (0-based). Corresponds to
the first base of the string of reference bases.
end: The end position (0-based) of this variant. Corresponds to the first
base after the last base in the reference allele.
reference_bases: The reference bases for this variant.
alternate_bases: The bases that appear instead of the reference bases.
names: Names for the variant, for example a RefSNP ID.
quality: Phred-scaled quality score (-10log10 prob(call is wrong)).
Higher values imply better quality.
filters: A list of filters (normally quality filters) this variant has
failed. `PASS` indicates this variant has passed all filters.
info: A map of additional variant information. The key is specified
in the VCF record and the value can be any type .
calls: The variant calls for this variant. Each one represents the
determination of genotype with respect to this variant.
"""
self.reference_name = reference_name
self.start = start
self.end = end
self.reference_bases = reference_bases
self.alternate_bases = alternate_bases or []
self.names = names or []
self.quality = quality
self.filters = filters or []
self.info = info or {}
self.calls = calls or []
def __eq__(self, other):
return (isinstance(other, Variant) and
vars(self) == vars(other))
def __repr__(self):
return ', '.join(
[str(s) for s in [self.reference_name,
self.start,
self.end,
self.reference_bases,
self.alternate_bases,
self.names,
self.quality,
self.filters,
self.info,
self.calls]])
def __lt__(self, other):
if not isinstance(other, Variant):
return NotImplemented
# Elements should first be sorted by reference_name, start, end.
# Ordering of other members is not important, but must be
# deterministic.
if self.reference_name != other.reference_name:
return self.reference_name < other.reference_name
elif self.start != other.start:
return self.start < other.start
elif self.end != other.end:
return self.end < other.end
self_vars = vars(self)
other_vars = vars(other)
for key in sorted(self_vars):
if self_vars[key] != other_vars[key]:
return self_vars[key] < other_vars[key]
return False
def __le__(self, other):
if not isinstance(other, Variant):
return NotImplemented
return self < other or self == other
def __ne__(self, other):
return not self == other
def __gt__(self, other):
if not isinstance(other, Variant):
return NotImplemented
return other < self
def __ge__(self, other):
if not isinstance(other, Variant):
return NotImplemented
return other <= self
class VariantCall(object):
"""A class to store info about a variant call.
A call represents the determination of genotype with respect to a particular
variant. It may include associated information such as quality and phasing.
"""
def __init__(self, name=None, genotype=None, phaseset=None, info=None):
# type: (str, List[int], str, Dict[str, Any]) -> None
"""Initialize the :class:`VariantCall` object.
Args:
name: The name of the call.
genotype: The genotype of this variant call as specified by the VCF
schema. The values are either `0` representing the reference, or a
1-based index into alternate bases. Ordering is only important if
`phaseset` is present. If a genotype is not called (that is, a `.` is
present in the GT string), -1 is used.
phaseset: If this field is present, this variant call's genotype ordering
implies the phase of the bases and is consistent with any other variant
calls in the same reference sequence which have the same phaseset value.
If the genotype data was phased but no phase set was specified, this
field will be set to `*`.
info: A map of additional variant call information. The key is specified
in the VCF record and the type of the value is specified by the VCF
header FORMAT.
"""
self.name = name
self.genotype = genotype or []
self.phaseset = phaseset
self.info = info or {}
def __eq__(self, other):
return ((self.name, self.genotype, self.phaseset, self.info) ==
(other.name, other.genotype, other.phaseset, other.info))
def __lt__(self, other):
if self.name != other.name:
return self.name < other.name
elif self.genotype != other.genotype:
return self.genotype < other.genotype
elif self.phaseset != other.phaseset:
return self.phaseset < other.phaseset
else:
return self.info < other.info
def __le__(self, other):
return self < other or self == other
def __gt__(self, other):
return other < self
def __ge__(self, other):
return other <= self
def __ne__(self, other):
return not self == other
def __repr__(self):
return ', '.join(
[str(s) for s in [self.name, self.genotype, self.phaseset, self.info]])
class VcfParser(object):
"""Base abstract class for defining a VCF file parser.
Derived classes must implement two methods:
_init_with_header: must initialize parser with given header lines.
_get_variant: given a line of VCF file, returns a Variant object.
Objects of the derived classed will be an iterator of records:
```
record_iterator = DerivedVcfParser(...)
for record in record_iterator:
yield record
```
"""
def __init__(self,
file_name, # type: str
range_tracker, # type: range_trackers.OffsetRangeTracker
file_pattern, # type: str
compression_type, # type: str
allow_malformed_records, # type: bool
representative_header_lines=None, # type: List[str]
splittable_bgzf=False, # type: bool
**kwargs # type: **str
):
# type: (...) -> None
# If `representative_header_lines` is given, header lines in `file_name`
# are ignored; refer to _process_header_lines() logic.
self._representative_header_lines = representative_header_lines
self._file_name = file_name
self._allow_malformed_records = allow_malformed_records
if splittable_bgzf:
text_source = bgzf.BGZFBlockSource(
file_name,
range_tracker,
representative_header_lines,
compression_type,
header_processor_fns=(
lambda x: not x.strip() or x.startswith('#'),
self._process_header_lines),
**kwargs)
elif compression_type == filesystems.CompressionTypes.GZIP:
text_source = bgzf.BGZFSource(
file_pattern,
0, # min_bundle_size
compression_type,
True, # strip_trailing_newlines
coders.StrUtf8Coder(), # coder
validate=False,
header_processor_fns=(
lambda x: not x.strip() or x.startswith('#'),
self._process_header_lines),
**kwargs)
else:
text_source = textio._TextSource(
file_pattern,
0, # min_bundle_size
compression_type,
True, # strip_trailing_newlines
coders.StrUtf8Coder(), # coder
validate=False,
header_processor_fns=(
lambda x: not x.strip() or x.startswith('#'),
self._process_header_lines),
**kwargs)
self._text_lines = text_source.read_records(self._file_name,
range_tracker)
def _process_header_lines(self, header_lines):
"""Processes header lines from text source and initializes the parser.
Note: this method will be automatically called by textio._TextSource().
"""
if self._representative_header_lines:
# Replace header lines with given representative header lines.
# We need to keep the last line of the header from the file because it
# contains the sample IDs, which is unique per file.
header_lines = self._representative_header_lines + header_lines[-1:]
self._init_with_header(header_lines)
def next(self):
text_line = next(self._text_lines).strip()
while not text_line: # skip empty lines.
# This natively raises StopIteration if end of file is reached.
text_line = next(self._text_lines).strip()
record = self._get_variant(text_line)
if isinstance(record, Variant):
return record
elif isinstance(record, MalformedVcfRecord):
if self._allow_malformed_records:
return record
else:
raise ValueError('VCF record read failed in %s for line %s: %s' %
(self._file_name, text_line, str(record.error)))
else:
raise ValueError('Unrecognized record type: %s.' % str(type(record)))
def __iter__(self):
return self
def _init_with_header(self, header_lines):
# type: (List[str]) -> None
"""Initializes the parser specific settings with the given header_lines.
Note: this method will be called by _process_header_lines().
"""
raise NotImplementedError
def _get_variant(self, data_line):
# type: (str) -> Variant
"""Converts a single data_line of a VCF file into a Variant object.
In case something goes wrong it must return a MalformedVcfRecord object.
Note: this method will be called by next(), one line at a time.
"""
raise NotImplementedError
class PyVcfParser(VcfParser):
"""An Iterator for processing a single VCF file using PyVcf."""
def __init__(self,
file_name, # type: str
range_tracker, # type: range_trackers.OffsetRangeTracker
compression_type, # type: str
allow_malformed_records, # type: bool
file_pattern=None, # type: str
representative_header_lines=None, # type: List[str]
splittable_bgzf=False, # type: bool
**kwargs # type: **str
):
# type: (...) -> None
super(PyVcfParser, self).__init__(file_name,
range_tracker,
file_pattern,
compression_type,
allow_malformed_records,
representative_header_lines,
splittable_bgzf,
**kwargs)
self._header_lines = []
self._next_line_to_process = None
self._current_line = None
# This member will be properly initiated in _init_with_header().
self._vcf_reader = None
def _init_with_header(self, header_lines):
self._header_lines = header_lines
try:
self._vcf_reader = vcf.Reader(fsock=self._line_generator())
except SyntaxError as e:
raise ValueError(
'Invalid VCF header in %s: %s' % (self._file_name, str(e)))
def _get_variant(self, data_line):
# _line_generator will consume this line.
self._next_line_to_process = data_line
try:
record = next(self._vcf_reader)
return self._convert_to_variant(record, self._vcf_reader.formats)
except (LookupError, ValueError) as e:
logging.warning('VCF record read failed in %s for line %s: %s',
self._file_name, data_line, str(e))
return MalformedVcfRecord(self._file_name, data_line, str(e))
def _line_generator(self):
for header in self._header_lines:
yield header
# Continue to process the next line indefinitely. The next line is set
# inside _get_variant() and this method is indirectly called in get_variant.
while self._next_line_to_process:
self._current_line = self._next_line_to_process
self._next_line_to_process = None
# PyVCF has explicit str() calls when parsing INFO fields, which fails
# with UTF-8 decoded strings. Encode the line back to UTF-8.
yield self._current_line.encode('utf-8')
# Making sure _get_variant() assigned a new value before consuming it.
assert self._next_line_to_process is not None, (
'Internal error: A data line is requested to be processed more than '
'once. Please file a bug if you see this!')
def _convert_to_variant(
self,
record, # type: vcf.model._Record
formats # type: Dict[str, vcf.parser._Format]
):
# type: (...) -> Variant
"""Converts the PyVCF record to a :class:`Variant` object.
Args:
record: An object containing info about a variant.
formats: The PyVCF dict storing FORMAT extracted from the VCF header.
The key is the FORMAT key and the value is
:class:`~vcf.parser._Format`.
Returns:
A :class:`Variant` object from the given record.
Raises:
ValueError: if ``record`` is semantically invalid.
"""
return Variant(
reference_name=record.CHROM,
start=record.start,
end=self._get_variant_end(record),
reference_bases=(
record.REF if record.REF != MISSING_FIELD_VALUE else None),
alternate_bases=self._get_variant_alternate_bases(record),
names=record.ID.split(';') if record.ID else [],
quality=record.QUAL,
filters=[PASS_FILTER] if record.FILTER == [] else record.FILTER,
info=self._get_variant_info(record),
calls=self._get_variant_calls(record, formats))
def _get_variant_end(self, record):
if END_INFO_KEY not in record.INFO:
return record.end
end_info_value = record.INFO[END_INFO_KEY]
if isinstance(end_info_value, (int, long)):
return end_info_value
if (isinstance(end_info_value, list) and len(end_info_value) == 1 and
isinstance(end_info_value[0], (int, long))):
return end_info_value[0]
else:
raise ValueError('Invalid END INFO field in record: {}'.format(
self._current_line))
def _get_variant_alternate_bases(self, record):
# ALT fields are classes in PyVCF (e.g. Substitution), so need convert
# them to their string representations.
return [str(r) for r in record.ALT if r] if record.ALT else []
def _get_variant_info(self, record):
info = {}
for k, v in record.INFO.iteritems():
if k != END_INFO_KEY:
info[k] = v
return info
def _get_variant_calls(self, record, formats):
calls = []
for sample in record.samples:
call = VariantCall()
call.name = sample.sample
for allele in sample.gt_alleles or [MISSING_GENOTYPE_VALUE]:
if allele is None:
allele = MISSING_GENOTYPE_VALUE
call.genotype.append(int(allele))
phaseset_from_format = (
getattr(sample.data, PHASESET_FORMAT_KEY)
if PHASESET_FORMAT_KEY in sample.data._fields
else None)
# Note: Call is considered phased if it contains the 'PS' key regardless
# of whether it uses '|'.
if phaseset_from_format or sample.phased:
call.phaseset = (str(phaseset_from_format) if phaseset_from_format
else DEFAULT_PHASESET_VALUE)
for field in sample.data._fields:
# Genotype and phaseset (if present) are already included.
if field in (GENOTYPE_FORMAT_KEY, PHASESET_FORMAT_KEY):
continue
data = getattr(sample.data, field)
# Convert single values to a list for cases where the number of fields
# is unknown. This is to ensure consistent types across all records.
# Note: this is already done for INFO fields in PyVCF.
if (field in formats and
formats[field].num not in (0, 1) and
isinstance(data, (int, float, long, basestring, bool))):
data = [data]
call.info[field] = data
calls.append(call)
return calls
class NucleusParser(VcfParser):
"""An Iterator for processing a single VCF file using Nucleus."""
def __init__(self,
file_name, # type: str
range_tracker, # type: range_trackers.OffsetRangeTracker
compression_type, # type: str
allow_malformed_records, # type: bool
file_pattern=None, # type: str
representative_header_lines=None, # type: List[str]
**kwargs # type: **str
):
# type: (...) -> None
super(NucleusParser, self).__init__(file_name,
range_tracker,
file_pattern,
compression_type,
allow_malformed_records,
representative_header_lines,
**kwargs)
try:
nucleus_vcf_reader
except NameError:
raise RuntimeError(
'Nucleus is not installed. Cannot use the Nucleus parser.')
# This member will be properly initiated in _init_with_header().
self._vcf_reader = None
# These members will be properly initiated in _extract_header_fields().
self._header_infos = {}
self._header_formats = {}
def _store_to_temp_local_file(self, header_lines):
temp_file, temp_file_name = tempfile.mkstemp(text=True)
for line in header_lines:
if not line.endswith('\n'):
line += '\n'
os.write(temp_file, line)
os.close(temp_file)
return temp_file_name
def _init_with_header(self, header_lines):
# The first header line must be similar to '##fileformat=VCFv.*'.
if header_lines and not header_lines[0].startswith(
FILE_FORMAT_HEADER_TEMPLATE.format(VERSION='')):
header_lines.insert(0, FILE_FORMAT_HEADER_TEMPLATE.format(VERSION='4.0'))
try:
self._vcf_reader = nucleus_vcf_reader.VcfReader.from_file(
self._store_to_temp_local_file(header_lines),
variants_pb2.VcfReaderOptions())
except ValueError as e:
raise ValueError(
'Invalid VCF header in %s: %s' % (self._file_name, str(e)))
self._extract_header_fields()
def _extract_header_fields(self):
header = self._vcf_reader.header
for info in header.infos:
self._header_infos[info.id] = info
for format_info in header.formats:
self._header_formats[format_info.id] = format_info
def _is_info_repeated(self, info_id):
info = self._header_infos.get(info_id, None)
if not info or not info.number:
return False
else:
return self._is_repeated(info.number)
def _is_format_repeated(self, format_id):
format_info = self._header_formats.get(format_id, None)
if not format_info or not format_info.number:
return False
else:
return self._is_repeated(format_info.number)
def _is_repeated(self, number):
if number in ('0', '1'):
return False
else:
return True
def _get_variant(self, data_line):
try:
variant_proto = self._vcf_reader.from_string(data_line)
return self._convert_to_variant(variant_proto)
except ValueError as e:
logging.warning('VCF variant_proto read failed in %s for line %s: %s',
self._file_name, data_line, str(e))
return MalformedVcfRecord(self._file_name, data_line, str(e))
def _convert_to_variant(self, variant_proto):
# type: (variants_pb2.Variant) -> Variant
return Variant(
reference_name=variant_proto.reference_name,
start=variant_proto.start,
end=variant_proto.end,
reference_bases=(variant_proto.reference_bases
if variant_proto.reference_bases != MISSING_FIELD_VALUE
else None),
alternate_bases=list(variant_proto.alternate_bases),
names=variant_proto.names[0].split(';') if variant_proto.names else [],
# TODO(samanvp): ensure the default value (when missing) is set to -1.
quality=variant_proto.quality,
filters=map(str, variant_proto.filter),
info=self._get_variant_info(variant_proto),
calls=self._get_variant_calls(variant_proto))
def _get_variant_info(self, variant_proto):
info = {}
for k in variant_proto.info:
data = self._convert_list_value(variant_proto.info[k],
self._is_info_repeated(k))
# Avoid including missing flags as `false` or other fields valued as `[]`.
if not data:
continue
info[k] = data
return info
def _convert_list_value(self, list_values, is_repeated):
"""Converts an object of ListValue to python native types.
if is_repeated is set the output will be a list otherwise a single value.
"""
output_list = []
for value in list_values.values:
if value.HasField('null_value'):
output_list.append(value.null_value)
elif value.HasField('number_value'):
output_list.append(value.number_value)
elif value.HasField('int_value'):
output_list.append(value.int_value)
elif value.HasField('string_value'):
output_list.append(value.string_value)
elif value.HasField('bool_value'):
output_list.append(value.bool_value)
elif value.HasField('struct_value'):
output_list.append(value.struct_value)
elif value.HasField('list_value'):
output_list.append(self._convert_list_value(value.list_value, True))
else:
raise ValueError('ListValue object has an unexpected value: %s' % value)
if is_repeated:
return output_list
else:
if len(output_list) > 1 and not self._allow_malformed_records:
raise ValueError('a not repeated field has more than 1 value')
if not output_list:
return None
else:
# TODO(samanvp): Verify whether we reach here with len(output_list) > 1.
return output_list[0]
def _get_variant_calls(self, variant_proto):
calls = []
for call_proto in variant_proto.calls:
call = VariantCall()
call.name = call_proto.call_set_name
if not call_proto.genotype:
call.genotype.append(MISSING_GENOTYPE_VALUE)
else:
call.genotype = list(call_proto.genotype)
phaseset_from_format = (
self._convert_list_value(call_proto.info[PHASESET_FORMAT_KEY], False)
if PHASESET_FORMAT_KEY in call_proto.info else None)
# Note: Call is considered phased if it contains the 'PS' key regardless
# of whether it uses '|'.
if phaseset_from_format or call_proto.is_phased:
call.phaseset = (str(phaseset_from_format) if phaseset_from_format
else DEFAULT_PHASESET_VALUE)
for k in call_proto.info:
# Genotype and phaseset (if present) are already included.
if k in (GENOTYPE_FORMAT_KEY, PHASESET_FORMAT_KEY):
continue
data = self._convert_list_value(call_proto.info[k],
self._is_format_repeated(k))
call.info[k] = data
calls.append(call)
return calls
|
from pxtrade.assets import Stock
def test_stock_str():
stock = Stock("QQQ AU", 2.55, currency_code="AUD")
stock_str = str(stock)
assert stock_str == "Stock('QQQ AU', 2.55, currency_code='AUD')"
|
"""AoC 2018 Day 20: A Regular Map"""
import re
from collections import OrderedDict
import numpy as np
# Part 1
def reduce_path(path):
new_path = ''
# Find the innermost parenthesis group
innermost = re.compile(r'(\([NSEW|]*?\))')
previous_start = 0
for m in innermost.finditer(path):
new_path += path[previous_start:m.start()]
# [1:-1] is to remove the parenthesis
branches = m.group()[1:-1].split('|')
if '' in branches:
longuest_branch = ''
else:
longuest_branch = max(branches, key=len)
new_path += longuest_branch
previous_start = m.end()
new_path += path[previous_start:]
if previous_start == 0:
return path
else:
return reduce_path(new_path)
a = reduce_path('ENNWSWW(NEWS|)SSSEEN(WNSE|)EE(SWEN|)NNN')
b = reduce_path('ESSWWN(E|NNENN(EESS(WNSE|)SSS|WWWSSSSE(SW|NNNE)))')
c = reduce_path('WSSEESWWWNW(S|NENNEEEENN(ESSSSW(NWSW|SSEN)|WSWWN(E|WWS(E|S'
'S))))')
assert len(a) == 18
assert len(b) == 23
assert len(c) == 31
with open('day20_input.txt') as f:
day = f.readline()[1:-2]
reduced = reduce_path(day)
print(f'Solution for part 1: {len(reduced)}')
# Part 2
def find_rooms(path, start_length=0):
rooms = OrderedDict()
directions = {'N': np.array([0, 1]),
'S': np.array([0, -1]),
'E': np.array([1, 0]),
'W': np.array([-1, 0])}
lengths_stack = [0]
previous_start = [0]
pos = np.array([0, 0])
for direction in path:
if pos in 'NSEW':
lengths_stack[-1] += 1
pos = pos + directions[direction]
if pos not in rooms.keys():
rooms[pos] = lengths_stack[-1]
elif pos == '(':
# branch, store starting point for the branch
room_before_branch = rooms.items()[-1]
previous_start.append(room_before_branch[0])
lengths_stack.append(room_before_branch[1])
elif pos == '|':
# Get back to the starting point of the branch
previous_start.pop()
lengths_stack
elif pos == ')':
pass
|
from datetime import datetime
from lights.base.history import History
from time import time
import numpy as np
class Learner:
"""The base class for a Solver.
Not intended for end-users, but for development only.
It should be sklearn-learn compliant
Parameters
----------
verbose : `bool`, default=True
If `True`, we verbose things, otherwise the solver does not
print anything (but records information in history anyway)
print_every : `int`, default=10
Print history information when ``n_iter`` (iteration number) is
a multiple of ``print_every``
"""
def __init__(self, verbose=True, print_every=10):
self.verbose = verbose
self.print_every = print_every
self.history = History()
def _start_solve(self):
# Reset history
self.history.clear()
self.time_start = Learner._get_now()
self._numeric_time_start = time()
if self.verbose:
print("Launching the solver " + self.__class__.__name__ + "...")
def _end_solve(self):
self.time_end = self._get_now()
t = time()
self.time_elapsed = t - self._numeric_time_start
if self.verbose:
print("Done solving using " + self.__class__.__name__ + " in "
+ "%.2e seconds" % self.time_elapsed)
@staticmethod
def _get_now():
return str(datetime.now()).replace(" ", "_").replace(":", "-")
def get_history(self, key=None):
"""Return history of the solver
Parameters
----------
key : `str`, default=None
if None all history is returned as a dict
if str then history of the required key is given
Returns
-------
output : `dict` or `list`
if key is None or key is not in history then output is
dict containing history of all keys
if key is not None and key is in history, then output is a list
containing history for the given key
"""
val = self.history.values.get(key, None)
if val is None:
return self.history.values
else:
return val
def get_history_keys(self):
"""Return names of the elements stored in history
Returns
-------
output : `list`
list containing names of history keys
"""
return self.history.values.keys()
def block_diag(l_arr):
"""Create a block diagonal matrix from provided list of arrays.
Parameters
----------
l_arr : list of arrays, up to 2-D
Input arrays.
Returns
-------
out : ndarray
Array with input arrays on the diagonal.
"""
shapes = np.array([a.shape for a in l_arr])
out_dtype = np.find_common_type([arr.dtype for arr in l_arr], [])
out = np.zeros(np.sum(shapes, axis=0), dtype=out_dtype)
r, c = 0, 0
for i, (rr, cc) in enumerate(shapes):
out[r:r + rr, c:c + cc] = l_arr[i]
r += rr
c += cc
return out
def normalize(X):
"""Normalize X to have mean 0 and std 1
Parameters
----------
X : `np.ndarray`, shape=(n, d)
A time-independent features matrix
Returns
-------
X_norm : `np.ndarray`, shape=(n, d)
The corresponding normilized matrix with mean 0 and std 1
"""
mean = X.mean(axis=0)
std = X.std(axis=0)
X_norm = (X - mean) / std
return X_norm
def from_ts_to_design_features(Y_il, fixed_effect_time_order):
"""Extracts the design features from a given longitudinal trajectory
Parameters
----------
Y_il : `pandas.Series`
A longitudinal trajectory
fixed_effect_time_order : `int`
Order of fixed effect features
Returns
-------
U_il : `np.array`
The corresponding fixed-effect design features
V_il : `np.array`
The corresponding random-effect design features
Y_il : `np.array`
The corresponding outcomes
n_il : `list`
The corresponding number of measurements
"""
times_il = Y_il.index.values
y_il = Y_il.values.reshape(-1, 1)
n_il = len(times_il)
U_il = np.ones(n_il)
for t in range(1, fixed_effect_time_order + 1):
U_il = np.c_[U_il, times_il ** t]
# linear time-varying features
V_il = np.ones(n_il)
V_il = np.c_[V_il, times_il]
return U_il, V_il, y_il, n_il
def extract_features(Y, fixed_effect_time_order):
"""Extract the design features from longitudinal data
Parameters
----------
Y : `pandas.DataFrame`, shape=(n_samples, n_long_features)
The longitudinal data. Each element of the dataframe is a
pandas.Series
fixed_effect_time_order : `int`
Order of the higher time monomial considered for the representations of
the time-varying features corresponding to the fixed effect. The
dimension of the corresponding design matrix is then equal to
fixed_effect_time_order + 1
Returns
-------
U : `np.array`, shape=(N_total, q)
Matrix that concatenates the fixed-effect design features of the
longitudinal data of all subjects, with N_total the total number
of longitudinal measurements for all subjects (N_total = sum(N))
V : `np.array`, shape=(N_total, n_samples x r)
Bloc-diagonal matrix with the random-effect design features of
the longitudinal data of all subjects
y : `np.array`, shape=(N_total,)
Vector that concatenates all longitudinal outcomes for all
subjects
N : `list`, shape=(n_samples,)
List with the number of longitudinal measurements for each subject
U_L : `list` of L `np.array`
Fixed-effect design features arranged by l-th order
V_L : `list` of L `np.array`
Random-effect design features arranged by l-th order
y_L : `list` of L `np.array`
Longitudinal outcomes arranged by l-th order
N_L : `list` of L `list`
Number of longitudinal measurements arranged by l-th order
"""
n_samples, n_long_features = Y.shape
U, V, y, N = [], [], [], []
U_L = [[] for _ in range(n_long_features)]
V_L = [[] for _ in range(n_long_features)]
y_L = [[] for _ in range(n_long_features)]
N_L = [[] for _ in range(n_long_features)]
for i in range(n_samples):
Y_i = Y.iloc[i]
U_i, V_i, y_i, N_i = [], [], np.array([]), []
for l in range(n_long_features):
U_il, V_il, y_il, N_il = from_ts_to_design_features(
Y_i[l], fixed_effect_time_order)
U_i.append(U_il)
V_i.append(V_il)
y_i = np.append(y_i, y_il)
N_i.append(N_il)
U_L[l].append(U_il)
V_L[l].append(V_il)
y_L[l].append(y_il)
N_L[l].append(N_il)
U.append(block_diag(U_i))
V.append(block_diag(V_i))
y.append(y_i.reshape(-1, 1))
N.append(N_i)
for l in range(n_long_features):
U_L[l] = np.concatenate(tuple(U_L[l]))
V_L[l] = block_diag(V_L[l])
y_L[l] = np.concatenate(tuple(y_L[l]))
return (U, V, y, N), (U_L, V_L, y_L, N_L)
def logistic_grad(z):
"""Overflow proof computation of 1 / (1 + exp(-z)))
"""
idx_pos = np.where(z >= 0.)
idx_neg = np.where(z < 0.)
res = np.empty(z.shape)
res[idx_pos] = 1. / (1. + np.exp(-z[idx_pos]))
res[idx_neg] = 1 - 1. / (1. + np.exp(z[idx_neg]))
return res
def logistic_loss(z):
"""Overflow proof computation of log(1 + exp(-z))
"""
idx_pos = np.where(z >= 0.)
idx_neg = np.where(z < 0.)
res = np.empty(z.shape)
res[idx_pos] = np.log(1. + np.exp(-z[idx_pos]))
z_neg = z[idx_neg]
res[idx_neg] = -z_neg + np.log(1. + np.exp(z_neg))
return res
def get_times_infos(T, T_u):
"""Get censored times indicators
Parameters
----------
T : `np.ndarray`, shape=(n_samples,)
Censored times of the event of interest
T_u : `np.ndarray`, shape=(J,)
The J unique training censored times of the event of interest
Returns
-------
J : `int`
Number of unique training censored times of the event of interest
indicator_1 : `np.ndarray`, shape=(n_samples, J)
The indicator matrix for comparing event times (T == T_u)
indicator_2 : `np.ndarray`, shape=(n_samples, J)
The indicator matrix for comparing event times (T <= T_u)
"""
J = T_u.shape[0]
indicator_1 = (T.reshape(-1, 1) == T_u).astype(np.ushort)
indicator_2 = (T.reshape(-1, 1) >= T_u).astype(np.ushort)
return J, indicator_1, indicator_2
def get_ext_from_vect(v):
"""Get extension on positive and negative parts of a vector
Parameters
----------
v : `np.ndarray`, shape=(dim,)
A vector
Returns
-------
v_ext : `np.ndarray`, shape=(2 * dim,)
The extended vector decomposed on positive and negative parts
"""
v_ext = np.concatenate((v, -v)).reshape(-1, 1)
v_ext[v_ext < 0] = 0
return v_ext
def get_xi_from_xi_ext(xi_ext, fit_intercept):
"""Get the time-independent coefficient vector from its extension on
positive and negative parts
Parameters
----------
xi_ext : `np.ndarray`, shape=(2*n_time_indep_features,)
The time-independent coefficient vector decomposed on positive and
negative parts
fit_intercept : `bool`
If `True`, include an intercept in the model for the time independent
features
Returns
-------
xi_0 : `float`
The intercept term
xi : `np.ndarray`, shape=(n_time_indep_features,)
The time-independent coefficient vector
"""
dim = len(xi_ext) // 2
xi = xi_ext[:dim] - xi_ext[dim:]
if fit_intercept:
xi_0 = xi[0]
xi = xi[1:]
else:
xi_0 = 0
return xi_0, xi
def get_vect_from_ext(v_ext):
"""Get the time-independent coefficient vector from its extension on
positive and negative parts
Parameters
----------
v_ext : `np.ndarray`, shape=(2*dim,)
The extended vector decomposed on positive and negative parts
Returns
-------
v : `np.ndarray`, shape=(dim,)
The returned vector
"""
dim = len(v_ext) // 2
v = (v_ext[:dim] - v_ext[dim:]).flatten()
return v
def clean_xi_ext(xi_ext, fit_intercept):
"""Removes potential intercept coefficients in the time-independent
coefficient vector decomposed on positive and negative parts
Parameters
----------
xi_ext : `np.ndarray`, shape=(2*n_time_indep_features,)
The time-independent coefficient vector decomposed on positive and
negative parts
fit_intercept : `bool`
If `True`, include an intercept in the model for the time independent
features
Returns
-------
xi_ext : `np.ndarray`, shape=(2*n_time_indep_features,)
The time-independent coefficient vector decomposed on positive and
negative parts without potential intercept coefficients
"""
if fit_intercept:
n_time_indep_features = len(xi_ext) // 2
xi_ext = np.delete(xi_ext, [0, n_time_indep_features + 1])
return xi_ext
|
#!/usr/bin/python
#Description: Download media items from an rss feed using youtube-dl
import feedparser
import sys
import subprocess
url = sys.argv[1]
tag = sys.argv[2]
#number = 10000
request = "%s%s" % (url, tag)
feed = feedparser.parse(request)
for entry in feed['entries']:
dl_link = entry.get('link', '')
dl_command = "youtube-dl %s" % (dl_link)
subprocess.call(dl_command, shell=True)
|
#!/usr/bin/env python
"""
Usage: testPartMesh.py infile
"""
import fvm
import fvm.fvmparallel as fvmparallel
import sys, time
from numpy import *
from mpi4py import MPI
from FluentCase import FluentCase
numIterations = 10
def usage():
print __doc__
sys.exit(1)
if len(sys.argv) < 2:
usage()
casfile = sys.argv[1]
reader = FluentCase(casfile)
reader.read()
fluent_meshes = reader.getMeshList()
nmesh = MPI.COMM_WORLD.Get_size()
#print "nmesh = ", nmesh
#npart = fvmparallel.IntVector(1,nmesh) #total of distributed meshes
#etype = fvmparallel.IntVector(1,1) #triangle
npart = [nmesh]
etype = [2]
part_mesh = fvmparallel.PartMesh( fluent_meshes, npart, etype );
part_mesh.setWeightType(0);
part_mesh.setNumFlag(0);
#actions
part_mesh.partition()
part_mesh.mesh()
part_mesh.mesh_debug()
part_mesh.debug_print()
#meshes = part_mesh.meshList()
|
from helpers import customs_exception
from utils.number_vehicles.stops.manager import stops_manager_cvrp
from utils.number_vehicles.stops.objects import stops_cvrptw
from utils.number_vehicles.stops import classDepot
import numpy as np
class StopsManagerCVRPTW(stops_manager_cvrp.StopsManagerCRVP):
"""
Class of manager for stops_cvrptw
"""
def __init__(self,depot = None):
super(StopsManagerCVRPTW,self).__init__(depot)
@classmethod
def from_cvrpTW_file(cls,filename):
"""
Create a stop manager filled
:param filename: the file from which we should read the stops
:return: an object of this class
"""
manager = cls()
file = open(filename, 'r') # reading only
line = file.readline()
reached_customer_section = False
while line:
words = line.strip().split("\t")
# check if the line corresponds to a stop
if reached_customer_section:
manager._create_stop(line)
# check if next one is going to be
else:
if len(words) >= 1 and words[0] == "CUSTOMER":
reached_customer_section = True
# we have to pass the line corresponding to the headers and the blank one
file.readline()
file.readline()
line = file.readline()
manager._create_depot(line)
line = file.readline()
file.close()
if not reached_customer_section:
raise customs_exception.WrongFile
return manager
def _create_stop(self,line):
"""
From the line of the file, create a stop with the corresponding
:param line: a line from the file
:return:
"""
words = line.strip().split(" ")
try:
words = [int(wo) for wo in words if wo != '']
except:
words = line.strip().split("\t")
if len(words) != 7:
raise customs_exception.WrongFile(words)
guid = self._check_guid(words[0])
self[guid] = stops_cvrptw.Stop_cvrptw(guid, words[1], words[2], words[3], words[4], words[5], words[6])
def _create_depot(self,line):
"""
From the line of the file create the corresponding depot
:param line: a line from the input
:return:
"""
words = line.strip().split(" ")
words = [wo for wo in words if wo != '']
if len(words) != 7:
words = line.strip().split("\t")
if len(words) != 7:
raise customs_exception.WrongFile(words)
assert int(words[0]) == 0, words
self.depot = classDepot.Depot(words[1], words[2], words[5])
def dump_to_file(self,file):
"""
Dump the manager stop in the right format in the file precised over there
:param file: the output file
:return: a dict matching newId to oldId
"""
file.write("CUSTOMER\n")
file.write("CUST NO.\tXCOORD.\tYCOORD.\tDEMAND\tREADY TIME\tDUE DATE\tSERVICE TIME\n")
file.write("\n")
self._dump_depot_section(file)
dict_new_old_id = self._dump_node_section(file)
return dict_new_old_id
def _dump_depot_section(self,file):
"""
Writhe the depot line in the corresponding line
:param file:
:return:
"""
depot_txt = "0 \t " + str(self.depot.x) + "\t" + str(self.depot.y) + "\t 0 \t 0 \t" + str(self.depot.due_date) + "\t 0"
file.write(depot_txt +"\n")
def _dump_node_section(self,file):
"""
Dump all the nodes to the right files
:param file:
:return: a dict matching file id to stop ID
"""
comp = 0
dict_new_old = {}
for stopId in self.keys():
comp +=1
stop = self[stopId]
text_stop = str(comp) + "\t" + str(stop.x) + "\t" + str(stop.y) + "\t" + str(stop.demand) + "\t" + str(stop.beginTW) +\
"\t" + str(stop.endTW) + "\t" + str(stop.service_time)
file.write(text_stop + "\n")
dict_new_old[comp] = stopId
return dict_new_old
def get_all_stops_end_in(self,start_time,end_time):
"""
Go through all stops whose end tw ends in [start_time, end_time[
:param start_time: begining of interval
:param end_time: end of interval
:return: a list of stop Ids
"""
list_stop_id = []
for stopId in self.keys():
stop = self[stopId]
if start_time<= stop.endTW< end_time:
list_stop_id.append(stopId)
return list_stop_id
def get_list_overlapping_stop(self,stop,threshold):
"""
Go through all the stops determine the number of overlapping stop (i.e sharing at least the theshold)
of the tw
:param stop: the considered stop
:param threshold: the amount of time the tw has to overlapp to count it as overlapping
:return: the list of stop id sharing the tw
"""
list_overlap = []
for otherId in self.keys():
other = self[otherId]
if stop.endTW - other.beginTW >= threshold and other.endTW - stop.beginTW >= threshold:
list_overlap.append(otherId)
return list_overlap
def get_avg_number_overlapping_tw_stops(self,threshold):
"""
Go through all the stops, and for each one, determine the number of overlapping stop (i.e sharing at least the theshold)
of the tw
:param threshold: the amount of time the tw has to overlapp to count it as overlapping
:return: the avergage number of such stops
"""
list_overlap = []
for stopId in self.keys():
stop = self[stopId]
list_overlap.append(len(self.get_list_overlapping_stop(stop,threshold)))
return np.mean(list_overlap)
|
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