id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
13,516 | from minichain import show, prompt, OpenAI, Python, GradioConf
import gradio as gr
def math_prompt(model, question
):
"Prompt to call GPT with a Jinja template"
return model(dict(question=question))
def python(model, code):
"Prompt to call Python interpreter"
code = "\n".join(code.strip(... | Chain them together |
13,517 | import trio
from minichain import TemplatePrompt, show_log, start_chain
The provided code snippet includes necessary dependencies for implementing the `chunk` function. Write a Python function `def chunk(f, width=4000, overlap=800)` to solve the following problem:
Split a documents into 4800 character overlapping chun... | Split a documents into 4800 character overlapping chunks |
13,518 | from dataclasses import dataclass, replace
from typing import Optional
from minichain import prompt, show, OpenAI, Google, transform
class State:
question: str
history: str = ""
next_query: Optional[str] = None
final_answer: Optional[str] = None
template_file = "selfask.pmpt.tpl")
def self_ask(... | null |
13,519 | from minichain import prompt, show, OpenAI, transform
from dataclasses import dataclass, is_dataclass, fields
from typing import List, Type, Dict, Any, get_origin, get_args
from enum import Enum
from jinja2 import select_autoescape, FileSystemLoader, Environment
import json
def type_to_prompt(out: type) -> str:
tmp... | null |
13,520 | from minichain import prompt, show, OpenAI, transform
from dataclasses import dataclass, is_dataclass, fields
from typing import List, Type, Dict, Any, get_origin, get_args
from enum import Enum
from jinja2 import select_autoescape, FileSystemLoader, Environment
import json
class Player:
player: str
stats: List... | null |
13,521 | import datasets
import numpy as np
from minichain import prompt, show, HuggingFaceEmbed, OpenAI, transform
def embed(model, inp):
return model(inp)
def get_neighbors(embedding, k=1):
res = gatsby.get_nearest_examples("embeddings", np.array(embedding), k)
return res.examples["passages"]
def ask(model, query,... | null |
13,522 | import pandas as pd
from minichain import prompt, Mock, show, OpenAI, GradioConf
import minichain
import json
import gradio as gr
import requests
names = {
'3-pointer percentage': 'FG3_PCT',
'3-pointers attempted': 'FG3A',
'3-pointers made': 'FG3M',
'Assists': 'AST',
'Blocks': 'BLK',
'Field goal... | null |
13,523 | import pandas as pd
from minichain import prompt, Mock, show, OpenAI, GradioConf
import minichain
import json
import gradio as gr
import requests
import os
def make_html(out):
return "<table><tr><td>" + out.replace("\t", "</td><td>").replace("\n", "</td></tr><tr><td>") + "</td></td></table>" | null |
13,524 | import pandas as pd
from minichain import prompt, Mock, show, OpenAI, GradioConf
import minichain
import json
import gradio as gr
import requests
def extract(model, passage, typ):
return model(dict(player_keys=names.items(), examples=examples, passage=passage, type=typ))
import os
def run(query):
return extrac... | null |
13,525 | from minichain import Id, prompt, OpenAI, show, transform, Mock, Break
from gradio_tools.tools import StableDiffusionTool, ImageCaptioningTool, ImageToMusicTool
def agent(model, query, history):
return model(dict(tools=[(str(tool.__class__.__name__), tool.description)
for tool in tools]... | null |
13,526 | import datasets
import numpy as np
from minichain import prompt, transform, show, OpenAIEmbed, OpenAI
from manifest import Manifest
def embed(model, inp):
return model(inp)
def get_neighbors(inp, k):
res = olympics.get_nearest_examples("embeddings", np.array(inp), k)
return res.examples["content"]
def get_r... | null |
13,527 | from minichain import prompt, show, GradioConf, OpenAI, Python
import gradio as gr
def pal_prompt(model, question):
def python(model, inp):
def pal(question):
return python(pal_prompt(question)) | null |
13,528 | from minichain import show, prompt, OpenAI, GradioConf
import gradio as gr
from gradio_tools.tools import StableDiffusionTool, ImageCaptioningTool
def picture(model, query):
return model(query)
gradio_conf=GradioConf(
block_output= lambda: gr.Image(),
block_input= lambda: gr.Textbox(... | null |
13,529 | from minichain import show, prompt, OpenAI, Bash
def cli_prompt(model, query):
def bash_run(model, x):
def bash(query):
return bash_run(cli_prompt(query)) | null |
13,530 | from minichain import prompt, transform, show, OpenAI
import json
def ner_extract(model, kwargs):
return model(kwargs)
def to_json(chat_output):
return json.loads(chat_output)
def team_describe(model, inp):
query = "Can you describe these basketball teams? " + \
" ".join([i["E"] for i in inp if i["T... | null |
13,543 | from minichain import prompt, show, GradioConf, OpenAI, Python
import gradio as gr
def pal_prompt(model, question):
return model(dict(question=question))
gradio_conf=GradioConf(block_input = lambda: gr.Code(language="python")))
def python(model, inp):
return model(inp + "\nprint(solution())")
def pal(q... | null |
13,545 | from minichain import show, prompt, OpenAI, Bash
def cli_prompt(model, query):
return model(dict(question=query))
def bash_run(model, x):
x = "\n".join(x.strip().split("\n")[1:-1])
return model(x)
def bash(query):
return bash_run(cli_prompt(query)) | null |
13,547 | import os
import subprocess
import time
from dataclasses import dataclass
from types import TracebackType
from typing import TYPE_CHECKING, Any, Dict, Iterator, List, Optional, Tuple
from eliot import start_action, to_file
def set_minichain_log(name: str) -> None:
to_file(open(f"{name}.log", "w")) | null |
13,548 | from dataclasses import asdict, dataclass
from itertools import count
from typing import (
Any,
Callable,
Generic,
Iterable,
Iterator,
List,
Optional,
TypeVar,
Union,
)
from jinja2 import Environment, FileSystemLoader, Template
from .backend import Backend, MinichainContext, PromptSn... | null |
13,549 | import inspect
import os
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Set, Tuple, Union
import gradio as gr
from gradio.blocks import Block
from minichain import start_chain
from .backend import MinichainContext
from .base import Prompt
CSS = """
#clean div.form {border: 0px}
#... | Constructs a gradio component to show a prompt chain. Args: prompt: A prompt or prompt chain to display. examples: A list of example inputs, either string or tuples of fields subprompts: The `Prompt` objects to display. fields: The names of the field input to the prompt. initial_state: For stateful prompts, the initial... |
13,550 | import os
import sys
from datetime import date
import subprocess
def git_authors():
result = subprocess.run(
["git", "shortlog", "--summary", "HEAD"],
stdout = subprocess.PIPE,
check = True)
names = [
line.strip().split("\t")[1]
for line in result.stdout.decode("ut... | null |
13,551 | import os
import sys
from datetime import date
import subprocess
def prose_list(items):
if not items:
return ""
if len(items) == 1:
return items[0]
elif len(items) == 2:
return " and ".join(items)
else:
return ", ".join([*items[0:-1], "and " + items[-1]]) | null |
13,552 | import logging
import hashlib
import collections.abc as abc
import os
import shutil
import sys
import errno
def _make_dir_recursively(dir_):
try:
os.makedirs(dir_)
except OSError as ex:
from errno import EEXIST
if ex.errno != EEXIST:
raise | null |
13,553 | import logging
import hashlib
import collections.abc as abc
import os
import shutil
import sys
import errno
def update_checksum(checksum, obj):
if isinstance(obj, str):
checksum.update(obj.encode("utf8"))
else:
checksum.update(obj) | null |
13,554 | import argparse
from augur.io import open_file, read_metadata
from Bio import SeqIO
import csv
import sys
def parse_args():
parser = argparse.ArgumentParser(
description="""
Custom script to combine metadata files from different origins.
In the case where metadata files specify different va... | null |
13,555 | import argparse
import json
import re
from numpy import linspace
from math import floor
The provided code snippet includes necessary dependencies for implementing the `adjust_coloring_for_epiweeks` function. Write a Python function `def adjust_coloring_for_epiweeks(dataset)` to solve the following problem:
If an auspi... | If an auspice JSON specifies a colouring with the key "epiweek" (case sensitive) then we create a categorical colorscale which evenly spaces the canonical nextstrain rainbow across the observed time window. NOTE: epiweek must be in CDC format ("YYYYMM") but this may be relaxed to include ISO format in the future. |
13,556 | import argparse
import sys
from datetime import datetime
import pandas as pd
import numpy as np
INSERT_BEFORE_THIS_COLUMN = "pango_lineage"
column_map = {
"clade": "Nextstrain_clade",
"Nextclade_pango": "Nextclade_pango",
"totalMissing": "missing_data",
"totalSubstitutions": "divergence",
"totalNonA... | Moves the new clade column after a specified column |
13,557 | import argparse
import sys
from datetime import datetime
import pandas as pd
import numpy as np
def parse_args():
parser = argparse.ArgumentParser(
description="Joins metadata file with Nextclade clade output",
)
parser.add_argument("first_file")
parser.add_argument("second_file")
parser.ad... | null |
13,558 | import argparse
import sys
from datetime import datetime
import pandas as pd
import numpy as np
def datestr_to_ordinal(x):
try:
return datetime.strptime(x,"%Y-%m-%d").toordinal()
except:
return np.nan | null |
13,559 | import argparse
import sys
from datetime import datetime
import pandas as pd
import numpy as np
def isfloat(value):
try:
float(value)
return True
except ValueError:
return False | null |
13,560 | import argparse
from augur.io import open_file, read_sequences, write_sequences
import hashlib
from pathlib import Path
import re
import sys
from utils import stream_tar_file_contents
The provided code snippet includes necessary dependencies for implementing the `rename_sequences` function. Write a Python function `de... | Rename the given sequences' ids by replacing the given patterns with the empty string. |
13,561 | import argparse
from augur.io import open_file, read_sequences, write_sequences
import hashlib
from pathlib import Path
import re
import sys
from utils import stream_tar_file_contents
class DuplicateSequenceError(ValueError):
pass
The provided code snippet includes necessary dependencies for implementing the `drop... | Identify and drop duplicate sequences from the given iterator. Parameters ---------- sequences : Iterator Yields ------ Bio.SeqIO.Seq : Unique sequence records Raises ------ DuplicateSequenceError : If `error_on_duplicates` is True and any duplicate records are found, raises an exception with a comma-delimited list of ... |
13,562 | from io import TextIOWrapper
import lzma
from pathlib import Path
import sys
import tarfile
import tempfile
EXTENSION_BY_FILETYPE = {
"metadata": ".tsv",
"sequences": ".fasta",
}
The provided code snippet includes necessary dependencies for implementing the `extract_tar_file_contents` function. Write a Python ... | Try to extract the contents of a given file type (e.g., metadata or sequences) from the given tar filename. Parameters ---------- filename : str or Path-like Path to the tar archive to search for the given file type. filetype : str Type of file to search for in the given tar archive based on the associated file extensi... |
13,563 | from io import TextIOWrapper
import lzma
from pathlib import Path
import sys
import tarfile
import tempfile
EXTENSION_BY_FILETYPE = {
"metadata": ".tsv",
"sequences": ".fasta",
}
The provided code snippet includes necessary dependencies for implementing the `stream_tar_file_contents` function. Write a Python f... | Try to extract the contents of a given file type (e.g., metadata or sequences) from the given tar filename. Parameters ---------- filename : str or Path-like Path to the tar archive to search for the given file type. filetype : str Type of file to search for in the given tar archive based on the associated file extensi... |
13,564 | import argparse
import json
from Bio import Phylo
from collections import defaultdict
def attach_labels(d, labeled_nodes):
if "children" in d:
for c in d["children"]:
if c["name"] in labeled_nodes:
if "labels" not in c["branch_attrs"]:
c["branch_attrs"]["labe... | null |
13,565 | import argparse
from datetime import datetime
from augur.io import read_metadata
import json
def get_recency(date_str, ref_date):
date_submitted = datetime.strptime(date_str, '%Y-%m-%d').toordinal()
ref_day = ref_date.toordinal()
delta_days = ref_day - date_submitted
if delta_days<=0:
return '... | null |
13,566 | import argparse
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
def isfloat(value):
try:
float(value)
return True
except ValueError:
return False | null |
13,567 | import argparse
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
def datestr_to_ordinal(x, minus_weeks=0):
try:
return (datetime.strptime(x,"%Y-%m-%d") - timedelta(weeks=minus_weeks)).toordinal()
except:
return np.nan
def earliest_clade_date(Nextstrain_clade, clad... | null |
13,568 | import argparse
from augur.io import open_file, read_sequences, write_sequences
import Bio
import Bio.SeqIO
from Bio.Seq import Seq
def mask_terminal_gaps(seq):
L = len(seq)
seq_trimmed = seq.lstrip('-')
left_gaps = L - len(seq_trimmed)
seq_trimmed = seq_trimmed.rstrip('-')
right_gaps = L - len(seq... | null |
13,569 | import argparse
import json
def update_strain_names(n): # closure
if "NODE_" not in n["name"] and args.prefix not in n["name"]:
n["name"] = args.prefix + n["name"]
if "children" in n:
for c in n["children"]:
update_strain_names(c) | null |
13,570 | import argparse
from augur.frequency_estimators import logit_transform
from augur.utils import annotate_parents_for_tree, read_node_data, read_tree, write_json
import Bio.Phylo
from collections import defaultdict
import json
import math
import numpy as np
from scipy.stats import linregress
import sys
The provided code... | Returns a dictionary of frequencies and their parameters indexed by strain name from a given auspice tip frequencies file. |
13,571 | import argparse, os, glob
from augur.io import open_file
from Bio import SeqIO, SeqFeature, Seq
from Bio.SeqIO.FastaIO import SimpleFastaParser
import numpy as np
import pandas as pd
def read_reference(fname, genemap):
try:
ref = str(SeqIO.read(fname, 'fasta').seq)
except:
with open(fname, 'r')... | null |
13,572 | import argparse, os, glob
from augur.io import open_file
from Bio import SeqIO, SeqFeature, Seq
from Bio.SeqIO.FastaIO import SimpleFastaParser
import numpy as np
import pandas as pd
def summarise_differences(ref, query, isAA):
"""
Summarise the differences between a provided reference and a query
(both of ... | null |
13,573 | import argparse
from augur.io import read_sequences
from random import shuffle
from collections import defaultdict
import Bio
import numpy as np
from Bio.SeqIO.FastaIO import SimpleFastaParser
from Bio.Seq import Seq
from Bio import AlignIO, SeqIO
from scipy import sparse
import sys
def compactify_sequences(sparse_mat... | null |
13,574 | import argparse
from augur.io import read_sequences
from random import shuffle
from collections import defaultdict
import Bio
import numpy as np
from Bio.SeqIO.FastaIO import SimpleFastaParser
from Bio.Seq import Seq
from Bio import AlignIO, SeqIO
from scipy import sparse
import sys
INITIALISATION_LENGTH = 1000000
def ... | null |
13,575 | import argparse
from augur.io import read_sequences
from random import shuffle
from collections import defaultdict
import Bio
import numpy as np
from Bio.SeqIO.FastaIO import SimpleFastaParser
from Bio.Seq import Seq
from Bio import AlignIO, SeqIO
from scipy import sparse
import sys
def calculate_distance_matrix(spars... | null |
13,576 | import argparse
from augur.io import open_file, read_metadata
import csv
import os
from pathlib import Path
import pandas as pd
import re
import shutil
import sys
from tempfile import NamedTemporaryFile
from utils import extract_tar_file_contents
The provided code snippet includes necessary dependencies for implementi... | Parse the mapping of current to new column names from the given list of renaming rules. Parameters ---------- renaming_rules : list[str] A list of strings mapping an old column name to a new one delimited by an equal symbol (e.g., "old_column=new_column"). Returns ------- dict : A mapping of new column names for each o... |
13,577 | import argparse
from augur.io import open_file, read_metadata
import csv
import os
from pathlib import Path
import pandas as pd
import re
import shutil
import sys
from tempfile import NamedTemporaryFile
from utils import extract_tar_file_contents
The provided code snippet includes necessary dependencies for implementi... | Parse location string from GISAID into the given separate geographic scales and return a dictionary of parse values by scale. Parameters ---------- location_string : str location_fields : list Returns ------- dict : dictionary of geographic fields parsed from the given string >>> location_fields = ["region", "country",... |
13,578 | import argparse
from augur.io import open_file, read_metadata
import csv
import os
from pathlib import Path
import pandas as pd
import re
import shutil
import sys
from tempfile import NamedTemporaryFile
from utils import extract_tar_file_contents
class MissingColumnException(Exception):
"""An exception caused by a ... | Get a mapping of all database ids for each strain name. Parameters ---------- metadata_file : str or Path-like or file object Path or file object for a metadata file to process. metadata_id_columns : list[str] A list of potential id columns for strain names in the metadata. database_id_columns : list[str] A list of pot... |
13,579 | import argparse
from augur.io import open_file, read_metadata
import csv
import os
from pathlib import Path
import pandas as pd
import re
import shutil
import sys
from tempfile import NamedTemporaryFile
from utils import extract_tar_file_contents
The provided code snippet includes necessary dependencies for implementi... | Filter duplicate records by the strain name in the given data frame index using the given file containing a mapping of strain names to database ids. Database ids allow us to identify duplicate records that need to be excluded. We prefer the latest record for a given strain name across all possible database ids and filt... |
13,580 | import argparse
import json
from Bio import Phylo, SeqIO
from Bio.Align import MultipleSeqAlignment
from treetime import TreeAnc
from augur.utils import load_features
def annotation_json(features, reference):
annotations = {}
for fname, feat in features.items():
annotations[fname] = {'seqid':reference.... | null |
13,581 | from os import listdir
from pathlib import Path
import argparse
from collections import defaultdict
def cut(s):
key = s.split(":")[0]
content = ":".join(s.split(":")[1:])[1:]
return (key, content)
def read_data(path):
additional_info = {}
for file in sorted(listdir(path)):
if file == '.DS... | null |
13,582 | from os import listdir
from pathlib import Path
import argparse
from collections import defaultdict
def bold(s):
return('\033[1m' + s + '\033[0m')
def read_simple_file(name):
with open(name) as myfile:
data_file = myfile.readlines()
return [l.strip() for l in data_file]
def read_dict(name):
with... | null |
13,583 | import sys
import datetime
import pandas as pd
from pathlib import Path
import os
def bold(s):
return('\033[1m' + s + '\033[0m')
def read_excel_lab_file(table_file_name):
if not os.path.exists(table_file_name):
print(bold("Missing input file: " + table_file_name))
return None
excel_table ... | null |
13,584 | import sys
import datetime
import pandas as pd
from pathlib import Path
import os
def read_metadata(filename, date_g, tweet):
uk_divisions = ["England", "Wales", "Northern Ireland", "Scotland", "United Kingdom"]
year_g = date_g[:4]
month_g = date_g[5:7]
if month_g == "12":
year_gplus = str(in... | null |
13,585 | import sys
import datetime
import pandas as pd
from pathlib import Path
import os
def collect_labs(labs, lab_dictionary, old):
lab_collection = {}
for region in labs:
if region not in lab_collection:
lab_collection[region] = {}
for country in sorted(labs[region]):
if co... | null |
13,586 | import sys
import datetime
import pandas as pd
from pathlib import Path
import os
path_to_outputs = "scripts/curate_metadata/outputs_new_sequences/"
def print_labs(lab_collection, data):
output_file = path_to_outputs + "twitter_handles_" + data + ".txt"
with open(output_file, "w") as out:
for region in... | null |
13,587 | import sys
import datetime
import pandas as pd
from pathlib import Path
import os
path_to_outputs = "scripts/curate_metadata/outputs_new_sequences/"
def generate_tweet(new_seqs_count, lab_collection, lab_collection_old, new_countries, data):
known_handles = []
for region in lab_collection_old:
for coun... | null |
13,588 | import os
import matplotlib.pyplot as plt
import pandas as pd
from pandas.plotting import register_matplotlib_converters
import random
import numpy as np
import math
import datetime
def cut(s):
key = s.split(":")[0]
content = ":".join(s.split(":")[1:])[1:]
return (key, content)
def read_data(path):
da... | null |
13,589 | import os
import matplotlib.pyplot as plt
import pandas as pd
from pandas.plotting import register_matplotlib_converters
import random
import numpy as np
import math
import datetime
def bold(s):
return('\033[1m' + s + '\033[0m')
def read_excel_lab_file(table_file_name):
excel_table = pd.read_excel(table_file_na... | null |
13,590 | import os
import matplotlib.pyplot as plt
import pandas as pd
from pandas.plotting import register_matplotlib_converters
import random
import numpy as np
import math
import datetime
def check_dates(data, today):
clade_dates = {
"19A": "2019-12-01",
"19B": "2019-12-01",
"20A": "2020-01-20",
... | null |
13,591 | import os
import matplotlib.pyplot as plt
import pandas as pd
from pandas.plotting import register_matplotlib_converters
import random
import numpy as np
import math
import datetime
def bold(s):
return('\033[1m' + s + '\033[0m')
flagged_properties = {"originating_lab": ["Synlab Haut de France"]}
path_to_outputs = "... | null |
13,592 | import os
import matplotlib.pyplot as plt
import pandas as pd
from pandas.plotting import register_matplotlib_converters
import random
import numpy as np
import math
import datetime
def plot_dates(data, path):
dates_by_country = {}
for id in data:
country = data[id]["country"]
date = datetime.... | null |
13,593 | import os
import matplotlib.pyplot as plt
import pandas as pd
from pandas.plotting import register_matplotlib_converters
import random
import numpy as np
import math
import datetime
path_to_outputs = "scripts/curate_metadata/outputs_new_sequences/"
def print_counts(data):
counts = {}
for id in data:
co... | null |
13,594 | import os
import matplotlib.pyplot as plt
import pandas as pd
from pandas.plotting import register_matplotlib_converters
import random
import numpy as np
import math
import datetime
def bold(s):
return('\033[1m' + s + '\033[0m')
def strike(s):
return('\u0336'.join(s) + '\u0336')
def read_excel_lab_file(table_fi... | null |
13,595 | import os
import matplotlib.pyplot as plt
import pandas as pd
from pandas.plotting import register_matplotlib_converters
import random
import numpy as np
import math
import datetime
def overview_with_dates(data, file_name):
data_sorted = {}
for id in data:
strain = data[id]["strain"]
submissio... | null |
13,596 | import os
import matplotlib.pyplot as plt
import pandas as pd
from pandas.plotting import register_matplotlib_converters
import random
import numpy as np
import math
import datetime
def filter_for_date_region(data, path_to_outputs, params):
(region, month) = params
special_strains = {}
for id in data:
... | null |
13,597 | import os
import matplotlib.pyplot as plt
import pandas as pd
from pandas.plotting import register_matplotlib_converters
import random
import numpy as np
import math
import datetime
path_to_outputs = "scripts/curate_metadata/outputs_new_sequences/"
def prepare_tweet(counts, total_lab_collection, lab_collection):
... | null |
13,598 | import os
import matplotlib.pyplot as plt
import pandas as pd
from pandas.plotting import register_matplotlib_converters
import random
import numpy as np
import math
import datetime
def prepare_tweet_new_format(counts, rare_labs):
links = {
"Africa": "nextstrain.org/ncov/africa",
"Asia": "nextstrai... | null |
13,599 | import os
from difflib import SequenceMatcher
from pathlib import Path
from geopy.geocoders import Nominatim
from collections import Counter
path_to_metadata = "data/"
def read_metadata(metadata_filename, data, geo_location_occurences, genbank=False):
with open(path_to_metadata + metadata_filename) as f:
... | null |
13,600 | import os
from difflib import SequenceMatcher
from pathlib import Path
from geopy.geocoders import Nominatim
from collections import Counter
def bold(s):
return('\033[1m' + s + '\033[0m')
def read_local_file(file_name):
path_file_name = path_to_config_files + file_name
with open(path_file_name) as myfile:
... | null |
13,601 | import os
from difflib import SequenceMatcher
from pathlib import Path
from geopy.geocoders import Nominatim
from collections import Counter
def bold(s):
return('\033[1m' + s + '\033[0m')
def read_local_file(file_name):
path_file_name = path_to_config_files + file_name
with open(path_file_name) as myfile:
... | null |
13,602 | import os
from difflib import SequenceMatcher
from pathlib import Path
from geopy.geocoders import Nominatim
from collections import Counter
def bold(s):
return('\033[1m' + s + '\033[0m')
def check_division_inconsistency(data):
for region in data:
for country in data[region]:
for division i... | null |
13,603 | import os
from difflib import SequenceMatcher
from pathlib import Path
from geopy.geocoders import Nominatim
from collections import Counter
def bold(s):
def read_local_file(file_name):
region_order = ["Asia", "Oceania", "Africa", "Europe", "South America", "North America"]
def check_duplicates(data, abbreviations_fil... | null |
13,604 | import os
from difflib import SequenceMatcher
from pathlib import Path
from geopy.geocoders import Nominatim
from collections import Counter
def read_latlongs(path):
with open(path + latlongs_file) as myfile:
file_content = myfile.readlines()
latlongs = {"location": {}, "division": {}, "country": {}, "r... | null |
13,605 | import os
from difflib import SequenceMatcher
from pathlib import Path
from geopy.geocoders import Nominatim
from collections import Counter
def bold(s):
return('\033[1m' + s + '\033[0m')
def print_missing_places(missing_latlongs):
### DIVISION ###
print("\n----------\n")
if missing_latlongs['division'... | null |
13,606 | import os
from difflib import SequenceMatcher
from pathlib import Path
from geopy.geocoders import Nominatim
from collections import Counter
def bold(s):
return('\033[1m' + s + '\033[0m')
def clean_string(s):
s = s.lower()
for c in replace_special_char:
s = s.replace(c, replace_special_char[c])
... | null |
13,607 | import os
from difflib import SequenceMatcher
from pathlib import Path
from geopy.geocoders import Nominatim
from collections import Counter
def find_place(geo_level, place, full_place, geolocator, region = "*"):
typed_place = full_place
redo = True
tries = 0
while redo == True:
if tries < 5:
... | null |
13,608 | import os
from difflib import SequenceMatcher
from pathlib import Path
from geopy.geocoders import Nominatim
from collections import Counter
def bold(s):
def read_ordering(path):
def read_latlongs(path):
def sort_by_coordinates(data, coordinates):
path_to_default_files = "defaults/"
path_to_output_files = "scripts/cura... | null |
13,609 | import os
from difflib import SequenceMatcher
from pathlib import Path
from geopy.geocoders import Nominatim
from collections import Counter
path_to_annotations = "../ncov-ingest/source-data/"
def read_annotations(annotationsFile, gisaid):
types = {"geography": ["location", "division", "country", "region", "divisi... | null |
13,610 | import os
from difflib import SequenceMatcher
from pathlib import Path
from geopy.geocoders import Nominatim
from collections import Counter
path_to_metadata = "data/"
def create_annotations(metadata_filename, applied_rules_geoLocation, applied_rules_manual, gisaid):
geoLocationAnnotations = {}
manualAnnotatio... | null |
13,611 | import os
from difflib import SequenceMatcher
from pathlib import Path
from geopy.geocoders import Nominatim
from collections import Counter
def bold(s):
return('\033[1m' + s + '\033[0m')
def find_conflicting_annotations(annotations, geoLocationAnnotations, manualAnnotations, gisaid):
for id in annotations["ge... | null |
13,612 | import os
from difflib import SequenceMatcher
from pathlib import Path
from geopy.geocoders import Nominatim
from collections import Counter
def bold(s):
path_to_metadata = "data/"
def special_metadata_checks(metadata_filename, annotations, gisaid):
special_annotations = {}
unknown_clades = []
with open(p... | null |
13,613 | import os
from difflib import SequenceMatcher
from pathlib import Path
from geopy.geocoders import Nominatim
from collections import Counter
path_to_output_files = "scripts/curate_metadata/output_curate_metadata/"
def write_annotations(annotations, annotationsFile):
with open(path_to_output_files + annotationsFile... | null |
13,614 | from datetime import datetime
import math
import struct
import sys
def scramble(data):
acc = 0
nacc = 0
t = 0x2953
u = 0xD9C2
v = 0x3FF1
x = 1
it = 0
while it < len(data):
t0 = t & 1
t1 = (t >> 1) & 1
u0 = u & 1
u1 = (u >> 1) & 1
v0 = v & 1
... | null |
13,615 | from datetime import datetime
import math
import struct
import sys
def flatten_dol(data):
header = struct.unpack(">64I", data[:256])
dol_min = min(a for a in header[18:36] if a)
dol_max = max(a + s for a, s in zip(header[18:36], header[36:54]))
img = bytearray(dol_max - dol_min)
for offset, addr... | null |
13,616 | from datetime import datetime
import math
import struct
import sys
def bytes_to_c_array(data):
p_list = [data[i:i + 4] for i in range(0, len(data), 4)]
return ["0x%08x" % int.from_bytes(b, byteorder='big', signed=False) for b in p_list] | null |
13,617 | from datetime import datetime
import math
import struct
import sys
def generate_header_file(elements, executable, input_file, output_file, size):
output = '#include <stdio.h>\n\n'
output += '//\n'
output += '// Command: {0} {1} {2}\n'.format(executable, input_file, output_file)
output += '//\n'
out... | null |
13,618 | from datetime import datetime
import math
import struct
import sys
The provided code snippet includes necessary dependencies for implementing the `process_scrambled_ipl` function. Write a Python function `def process_scrambled_ipl(ipl, size)` to solve the following problem:
Does additional processing to scrambled IPL ... | Does additional processing to scrambled IPL payload. Payload used by PicoBoot has to be preprocessed. Whole payload has to be aligned to 1K blocks then every bit needs to be duplicated 4 times. |
13,619 | import os
import sys
import torch
from d2l import torch as d2l
from torch import nn
import d2lutil.common as common
def dropout_layer(X, dropout):
assert 0 <= dropout <= 1
# 在本情况中，所有元素都被丢弃。
if dropout == 1:
return torch.zeros_like(X)
# 在本情况中，所有元素都被保留。
if dropout == 0:
return X
m... | null |
13,620 | import os
import sys
import torch
from torch import nn
import d2lutil.common as common
batch_size = min(10, train_labels.shape[0])
dataset = torch.utils.data.TensorDataset(train_features, train_labels)
train_iter = torch.utils.data.DataLoader(dataset, batch_size, shuffle=True)
net = nn.Linear(train_feat... | null |
13,621 | import os
import sys
import torch
from d2l import torch as d2l
from torch import nn
def init_weights(m):
if type(m) == nn.Linear:
nn.init.normal_(m.weight, std=0.01) | null |
13,622 | import hashlib
import os
import tarfile
import zipfile
import requests
import numpy as np
import pandas as pd
import torch
from torch import nn
from d2l import torch as d2l
def download(name, cache_dir=os.path.join('..', 'data')): # @save
"""下载一个DATA_HUB中的文件，返回本地文件名"""
assert name in DATA_HUB, f"{name} 不存在于 {D... | 下载并解压zip/tar文件 |
13,623 | import hashlib
import os
import tarfile
import zipfile
import requests
import numpy as np
import pandas as pd
import torch
from torch import nn
from d2l import torch as d2l
DATA_HUB = dict()
def download(name, cache_dir=os.path.join('..', 'data')): # @save
"""下载一个DATA_HUB中的文件，返回本地文件名"""
assert name in DATA_HUB... | 下载DATA_HUB中的所有文件 |
13,625 | import os
import sys
import numpy as np
import torch
from d2l import torch as d2l
import d2lutil.common as common
train_features, test_features = features[:n_train, :], features[n_train:, :]
train_labels, test_labels = labels[:n_train], labels[n_train:]
train_iter = torch.utils.data.DataLoader(dataset, batch_size, shuf... | null |
13,627 | import os
import sys
import numpy as np
import torch
from d2l import torch as d2l
import d2lutil.common as common
train_features, test_features = features[:n_train, :], features[n_train:, :]
train_labels, test_labels = labels[:n_train], labels[n_train:]
def init_params():
w = torch.randn((num_inputs, 1), requires_g... | null |
13,628 | import math
import os
import numpy as np
import torch
from d2l import torch as d2l
def normal(x, mu, sigma):
p = 1 / math.sqrt(2 * math.pi * sigma ** 2)
return p * np.exp((- 0.5 / sigma ** 2) * (x - mu) ** 2) | null |
13,629 | import sys
import os
import matplotlib.pyplot as plt
import torch
import torchvision
from torchvision import transforms
from torch.utils import data
from d2l import torch as d2l
import d2lutil.common as common
-shirt', 'trouser', 'pullover', 'dress', 'coat',
'sandal', 'shirt', 'sneaker', 'bag', 'a... | 返回Fashion-MNIST数据集的文本标签。 |
13,630 | import sys
import os
import matplotlib.pyplot as plt
import torch
import torchvision
from torchvision import transforms
from torch.utils import data
from d2l import torch as d2l
import d2lutil.common as common
d2l.use_svg_display()
(images), figsize=(12, 12))
for f, img, lbl in zip(figs, images, labels):
... | null |
13,631 | import sys
import os
import matplotlib.pyplot as plt
import torch
import torchvision
from torchvision import transforms
from torch.utils import data
from d2l import torch as d2l
import d2lutil.common as common
W = torch.normal(0, 0.01, size=(num_inputs, num_outputs), requires_grad=True)
b = torch.zeros(num_outputs, req... | null |
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