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How to use before_start method in autotest
Best Python code snippet using autotest_python
standard.py
Source:standard.py 
1"""2Standard manipulations that are found in many different genomic tools.3"""4# Built-in modules #5import sys6# Internal modules #7from gMiner.operations.genomic_manip import Manipulation as Manip8from gMiner import common9################################################################################10class merge(Manip):11 '''Merges features that are adjacent or overlapping in one stream'''12 def __init__(self):13 self.name = 'Internal merge'14 self.input_tracks = [{'type': 'track', 'name': 'X', 'kind': 'qualitative', 'fields': ['start','end', '...']}]15 self.input_constraints = []16 self.input_request = []17 self.input_special = [{'type': 'in_datatype', 'name': 'in_type'}]18 self.input_by_chrom = []19 self.output_tracks = [{'type': 'track', 'kind': 'qualitative', 'fields': {'same': 0}}]20 self.output_constraints = []21 self.output_other = []22 def chr_collapse(self, *args): return common.collapse.by_appending(*args)23 def quan(self, X):24 # Setup #25 for x in X: break26 if 'x' not in locals(): return27 # Core loop #28 for y in X:29 if y[0] == x[1]:30 x = list(x)31 l_x = x[1] - x[0]32 l_y = y[1] - y[0]33 x[2] = (l_x*x[2] + l_y*y[2]) / (l_x+l_y)34 x[1] = y[1]35 else:36 yield tuple(x)37 x = y38 # Last feature #39 yield tuple(x)40 def qual(self, X):41 # Setup #42 for x in X: break43 if 'x' not in locals(): return44 # Core loop #45 for y in X:46 if y[0] <= x[1]:47 x = list(x)48 x[1] = max(x[1], y[1])49 x[2] = x[2] + ' + ' + y[2]50 x[3] = x[3] + y[3]51 x[4] = x[4] == y[4] and x[4] or 052 else:53 yield tuple(x)54 x = y55 # Last feature #56 yield tuple(x)57#-------------------------------------------------------------------------------------------#58class filter(Manip):59 '''Computes the overlap of the first stream against the second stream60 returning only complete features from the first stream'''61 def __init__(self):62 self.name = 'Filter features'63 self.input_tracks = [{'type': 'track', 'name': 'X', 'kind': 'qualitative', 'fields': ['start','end','...']},64 {'type': 'track', 'name': 'Y', 'kind': 'qualitative', 'fields': ['start','end']}]65 self.input_constraints = ['ordered']66 self.input_request = []67 self.input_special = []68 self.input_by_chrom = []69 self.output_tracks = [{'type': 'track', 'kind': 'qualitative', 'fields': {'same': 0}}]70 self.output_constraints = []71 self.output_other = []72 def chr_collapse(self, *args): return common.collapse.by_intersection(*args)73 def __call__(self, X, Y):74 sentinel = (sys.maxint, sys.maxint)75 X = common.sentinelize(X, sentinel)76 Y = common.sentinelize(Y, sentinel)77 x = X.next()78 y = Y.next()79 if x == sentinel or y == sentinel: continue_loop = False80 else: continue_loop = True81 while continue_loop:82 open_window = y[0]83 close_window = y[1]84 # Extend the y window as long as possible #85 while True:86 if y == sentinel: break87 y = Y.next()88 if y[0] > close_window: break89 if y[1] > close_window: close_window = y[1]90 # Read features from X until overshooting the y window #91 while True:92 if x[0] >= close_window: break93 if x[1] > open_window: yield x94 x = X.next()95 if x == sentinel:96 continue_loop = False97 break98#-------------------------------------------------------------------------------------------#99class neighborhood(Manip):100 '''Given a stream of features and four integers `before_start`, `after_end`,101 `after_start` and `before_end`, this manipulation will output,102 for every feature in the input stream, one or two features103 in the neighboorhod of the orginal feature.104 * Only `before_start` and `after_end` are given::105 (start, end, ...) -> (start+before_start, end+after_end, ...)106 * Only `before_start` and `after_start` are given::107 (start, end, ...) -> (start+before_start, start+after_start, ...)108 * Only `after_end` and `before_end` are given::109 (start, end, ...) -> (end+before_end, end+after_end, ...)110 * If all four parameters are given, a pair of features is outputed::111 (start, end, ...) -> (start+before_start, start+after_start, ...)112 (end+before_end, end+after_end, ...)113 * If the boolean parameter `on_strand` is set to True,114 features on the negative strand are inverted as such::115 (start, end, ...) -> (start-after_end, start-before_end, ...)116 (end-after_start, end-before_start, ...)117 * If the inputed stream is quantitative, only `before_start` and `after_end`118 are taken into consideration and must be equal.'''119 def __init__(self):120 self.name = 'Neighborhood regions'121 self.input_tracks = [{'type': 'track', 'name': 'X', 'kind': 'any', 'fields': ['start','end', '...']}]122 self.input_constraints = []123 self.input_request = [{'type': int, 'key': 'before_start', 'name': 'before_start', 'default': None},124 {'type': int, 'key': 'after_end', 'name': 'after_end', 'default': None},125 {'type': int, 'key': 'after_start', 'name': 'after_start', 'default': None},126 {'type': int, 'key': 'before_end', 'name': 'before_end', 'default': None}]127 self.input_special = [{'type': 'in_datatype', 'name': 'in_type'}]128 self.input_by_chrom = [{'type': 'stop_val', 'name': 'stop_val'}]129 self.output_tracks = [{'type': 'track', 'kind': 'qualitative', 'fields': {'same': 0}}]130 self.output_constraints = []131 self.output_other = []132 def chr_collapse(self, *args): return common.collapse.by_appending(*args)133 def qual(self, stop_val, **kwargs):134 def generate(X, before_start=None, after_end=None, after_start=None, before_end=None, on_strand=False):135 if before_start and after_start:136 if before_start > after_start:137 raise Exception("'before_start' cannot be larger than 'after_start'")138 if before_end and after_end:139 if before_end > after_end:140 raise Exception("before_end cannot be larger than 'after_end'")141 if not on_strand:142 if before_start and after_end and after_start and before_end:143 for x in X:144 yield (x[0]+before_start, x[0]+after_start) + x[2:]145 yield (x[1]+before_end, x[1]+after_end) + x[2:]146 if before_start and after_start:147 for x in X: yield (x[0]+before_start, x[0]+after_start) + x[2:]148 if before_end and after_end:149 for x in X: yield (x[1]+before_end, x[1]+after_end) + x[2:]150 if before_start and after_end:151 for x in X: yield (x[0]+before_start, x[1]+after_end) + x[2:]152 else:153 if before_start and after_end and after_start and before_end:154 for x in X:155 yield (x[0]-after_end, x[0]-before_end) + x[2:]156 yield (x[1]-after_start, x[1]-before_start) + x[2:]157 if before_start and after_start:158 for x in X: yield (x[1]-after_start, x[1]-before_start) + x[2:]159 if after_end and before_end:160 for x in X: yield (x[0]-after_end, x[0]-before_end) + x[2:]161 if before_start and after_end:162 for x in X: yield (x[0]-after_end, x[1]-before_start) + x[2:]163 X = generate(**kwargs)164 from .basic import bounded165 for x in bounded()(X, 0, stop_val): yield x166 def quan(self, stop_val, **kwargs):167 def generate(X, before_start=None, after_end=None, after_start=None, before_end=None, on_strand=False):168 if on_strand:169 raise Exception("As the track is quantitative, you cannot specify 'on_strand=True'")170 if after_start:171 raise Exception("As the track is quantitative, you cannot specify a value for 'after_start'")172 if before_end:173 raise Exception("As the track is quantitative, you cannot specify a value for 'before_end'")174 if before_start != after_end:175 raise Exception("As the track is quantitative, 'before_start' and 'after_start' need to be equal")176 for x in X: yield (x[0]+before_start, x[1]+before_start) + x[2:]177 X = generate(**kwargs)178 from .basic import bounded179 for x in bounded()(X, 0, stop_val): yield x180#-----------------------------------#181# This code was written by the BBCF #182# http://bbcf.epfl.ch/ #183# webmaster.bbcf@epfl.ch #...
app.py
Source:app.py
1# ## Step 2 - Climate App2# Now that you have completed your initial analysis, design a Flask API based on the queries that you have just developed.3import sqlalchemy4from sqlalchemy.ext.automap import automap_base5from sqlalchemy.orm import Session6from sqlalchemy import create_engine, func, inspect, desc7from flask import Flask, jsonify8import matplotlib.pyplot as plt9import numpy as np10import pandas as pd11import datetime as dt12 13engine = create_engine("sqlite:///sqlalchemy-challenge/Resources/hawaii.sqlite")14Base = automap_base()15Base.prepare(engine, reflect=True)16Base.classes.keys()17inspector = inspect(engine)18Measurement = Base.classes.measurement19m_columns = inspector.get_columns('measurement')20Station = Base.classes.station21s_columns = inspector.get_columns('station')22M = Measurement23S = Station 24# * Use Flask to create your routes.25app = Flask(__name__)26# ### Routes27# * `/`28@app.route('/')29# * Home page.30def home_page():31 # * List all routes that are available.32 return (33 f"Welcome to my climate analysis homepage! <br/>"34 f"Available routes inclue: <br/>"35 f"/api/v1.0/precipitation <br/>"36 f"/api/v1.0/stations <br/>"37 f"/api/v1.0/tobs <br/>"38 f"/api/v1.0/<start> <br/>"39 f"/api/v1.0/<start>/<end>"40 )41@app.route("/api/v1.0/precipitation")42# * `/api/v1.0/precipitation`43def precipitation():44 session = Session(engine)45 percip = session.query(M.date, M.prcp).order_by(M.date).all()46 session.close()47 return jsonify(percip)48# * Convert the query results to a dictionary using `date` as the key and `prcp` as the value.49# * Return the JSON representation of your dictionary.50@app.route("/api/v1.0/stations")51# * `/api/v1.0/stations`52def stations():53 session = Session(engine)54 stations = session.query(S.name).all()55 session.close()56 # * Return a JSON list of stations from the dataset.57 return jsonify(stations)58 59@app.route("/api/v1.0/tobs")60 # * `/api/v1.0/tobs`61def tobs():62 session = Session(engine)63 last_12_months = (dt.date(2017, 8, 23) - dt.timedelta(days=365))64 sel = [Measurement.date, Measurement.prcp]65 last_12_percip = session.query(*sel).filter(Measurement.date >= last_12_months).group_by(Measurement.date).all()66 session.close()67 # * Return a JSON list of temperature observations (TOBS) for the previous year.68 return jsonify(last_12_percip)69 70 71@app.route('/api/v1.0/<start>', endpoint='start')72# * `/api/v1.0/<start>` 73def start(start):74 start = '2010-11-23'75 76 session = Session(engine)77 # * Return a JSON list of the minimum temperature, the average temperature, and the max temperature for a given start or start-end range.78 # * When given the start only, calculate `TMIN`, `TAVG`, and `TMAX` for all dates greater than and equal to the start date.79 sel = [func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)]80 before_start = pd.DataFrame(columns=('Date', 'Min Temp', 'Avg Temp', 'Max Temp'))81 before_start['Date'] = pd.date_range(start='2010-01-01', end=start)82 j=083 for i in before_start['Date'].astype(str):84 data = session.query(*sel).filter(func.strftime("%Y-%m-%d", Measurement.date) == i).all()85 before_start['Min Temp'][j] = data[0][0]86 before_start['Avg Temp'][j] = data[0][1]87 before_start['Max Temp'][j] = data[0][2]88 j+=189 session.close()90 results = before_start.to_json(orient='index', date_format='iso')91 return jsonify(results)92 93 94# and `/api/v1.0/<start>/<end>95@app.route('/api/v1.0/<start>/<end>', endpoint='start_end')96# * `/api/v1.0/<start>` 97def start_end(start=None, end=None):98 session = Session(engine)99 # * Return a JSON list of the minimum temperature, the average temperature, and the max temperature for a given start or start-end range.100 # * When given the start and the end date, calculate the `TMIN`, `TAVG`, and `TMAX` for dates between the start and end date inclusive.101 sel = [func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)]102 before_start = pd.DataFrame(columns=('Date', 'Min Temp', 'Avg Temp', 'Max Temp'))103 before_start['Date'] = pd.date_range(start=start, end=end)104 j=0105 for i in before_start['Date'].astype(str):106 data = session.query(*sel).filter(func.strftime("%Y-%m-%d", Measurement.date) == i).all()107 before_start['Min Temp'][j] = data[0][0]108 before_start['Avg Temp'][j] = data[0][1]109 before_start['Max Temp'][j] = data[0][2]110 j+=1111 session.close()112 results = before_start.to_json(orient='index', date_format='iso')113 return jsonify(results)114if __name__ == '__main__':...
04_partition.py
Source:04_partition.py
1import linkedlist as ll2def partition(head, x):3 """4 Partition a linked list around a value x.5 All nodes less than x come before all nodes greater than or equal to x.6 Runtime7 O(n) O(n)8 """9 10 before_start = None11 before_end = None12 after_start = None13 after_end = None14 current = head15 while current is not None:16 next = current.next17 current.next = None18 if current.data < x:19 if before_start is None:20 before_start = current21 before_end = before_start22 else:23 before_end.next = current24 before_end = current25 else:26 if after_start is None:27 after_start = current28 after_end = after_start29 else:30 after_end.next = current31 after_end = current32 current = current.next33 if before_start is None:34 return after_start35 before_end.next = after_start36 return before_start37if __name__ == "__main__":38 linked_list = ll.LinkedList()39 linked_list.append(3)40 linked_list.append(5)41 linked_list.append(8)42 linked_list.append(5)43 linked_list.append(10)44 linked_list.append(2)45 linked_list.append(1)46 x = 547 linked_list.print_list()48 partition_list = partition(linked_list.head, x)...
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