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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