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How to use save_raw method in yandex-tank
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id15v2.py
Source:id15v2.py 
1#!/usr/bin/env python2# -*- coding: utf-8 -*-3"""Data Analysis plugin tailored for ID154 5"""6__authors__ = ["Jérôme Kieffer"]7__contact__ = "Jerome.Kieffer@ESRF.eu"8__license__ = "MIT"9__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"10__date__ = "03/09/2020"11__status__ = "development"12version = "0.5.0"13import os14import numpy15import logging16import copy17import json18import glob19from dahu import version as dahu_version20from dahu.plugin import Plugin21from dahu.factory import register22from dahu.utils import get_isotime23logger = logging.getLogger("id15v2")24try:25 import fabio26 import pyFAI, pyFAI.io27 from pyFAI import azimuthalIntegrator28 from pyFAI.method_registry import IntegrationMethod29 from silx.opencl.codec.byte_offset import ByteOffset30except ImportError:31 logger.error("Failed to import PyFAI, fabio or silx: download and install it from pypi")32@register33class IntegrateManyFrames(Plugin):34 """This is the basic plugin of PyFAI for azimuthal integration35 36 Typical JSON file:37 {"poni_file": "/tmp/example.poni",38 "input_files": ["/tmp/file1.cbf", "/tmp/file2.cbf"],39 "monitor_values": [1, 1.1],40 "npt": 2000,41 "npt_azim": None, 42 "unit": "2th_deg",43 "output_file": "/path/to/dest.h5",44 "save_raw": "/path/to/hdf5/with/raw.h5",45 "delete_incoming": False,46 "do_SA":True,47 }48 Plus:49 "mask":50 "wavelength"51 "unit"52 "dummy"53 "delta_dummy"54 "do_polarziation"55 "polarization_factor"56 "do_SA"57 "norm"58 "raw_compression": "bitshuffle",59 "integration_method": "integrate1d"60 "sigma_clip_thresold":361 self.sigma_clip_max_iter: 562 63 """64 def __init__(self):65 """66 """67 Plugin.__init__(self)68 self.ai = None # this is the azimuthal integrator to use69 self.npt = 200070 self.npt_azim = 25671 self.input_files = []72 self.method = "ocl_nosplit_csr_gpu"73 self.unit = "q_nm^-1"74 self.output_file = None75 self.mask = None76 self.wavelength = None77 self.polarization_factor = None78 self.do_SA = False79 self.dummy = None80 self.delta_dummy = None81 self.norm = 182 self.error_model = None # "poisson"83 self.save_raw = None84 self.raw_nxs = None85 self.raw_ds = None86 self.raw_compression = None87 self.integration_method = "integrate1d"88 self.sigma_clip_thresold = 389 self.sigma_clip_max_iter = 590 self.medfilt1d_percentile = (10, 90)91 def setup(self, kwargs=None):92 """Perform the setup of the job.93 mainly parsing of the kwargs. 94 95 :param kwargs: dict with parmaters.96 :return: None97 """98 logger.debug("IntegrateManyFrames.setup")99 Plugin.setup(self, kwargs)100 self.input_files = self.input.get("input_files")101 if not self.input_files:102 self.log_error("InputError: input_files not in input.")103 if not isinstance(self.input_files, list):104 self.input_files = glob.glob(self.input_files)105 self.input_files.sort()106 if "output_file" not in self.input:107 self.log_error("InputWarning: output_file not in input, save in input directory",108 do_raise=False)109 self.output_file = os.path.join(os.path.dirname(self.input_files[0]), "output.h5")110 else:111 self.output_file = os.path.abspath(self.input["output_file"])112 if not self.output_file.endswith(".h5"):113 self.output_file = self.output_file + ".h5"114 poni_file = self.input.get("poni_file")115 if not poni_file:116 self.log_error("InputError: poni_file not in input.")117 self.ai = pyFAI.load(poni_file)118# stored = self._ais.get(poni_file, ai)119# if stored is ai:120# self.ai = stored121# else:122# self.ai = copy.deepcopy(stored)123 self.npt = int(self.input.get("npt", self.npt))124 self.npt_azim = self.input.get("npt_azim", self.npt_azim)125 self.unit = self.input.get("unit", self.unit)126 self.wavelength = self.input.get("wavelength", self.wavelength)127 if os.path.exists(self.input.get("mask", "")):128 self.mask = fabio.open(self.input["mask"]).data129 self.dummy = self.input.get("dummy", self.dummy)130 self.delta_dummy = self.input.get("delta_dummy", self.delta_dummy)131 if self.input.get("do_polarziation"):132 self.polarization_factor = self.input.get("polarization_factor", self.polarization_factor)133 self.do_SA = self.input.get("do_SA", self.do_SA)134 self.norm = self.input.get("norm", self.norm)135 self.save_raw = self.input.get("save_raw", self.save_raw)136 self.integration_method = self.input.get("integration_method", self.integration_method)137 self.sigma_clip_thresold = self.input.get("sigma_clip_thresold", self.sigma_clip_thresold)138 self.sigma_clip_max_iter = self.input.get("sigma_clip_max_iter", self.sigma_clip_max_iter)139 self.medfilt1d_percentile = self.input.get("medfilt1d_percentile", self.medfilt1d_percentile)140 method = self.input.get("method", self.method)141 if "1" in self.integration_method:142 integration_dim = 1143 else:144 integration_dim = 2145 if isinstance(method, (str, bytes)):146 self.method = IntegrationMethod.select_old_method(integration_dim, method)147 else:148 self.method = IntegrationMethod.select_one_available(method, dim=integration_dim, degradable=True)149 print(self.method)150 self.raw_compression = self.input.get("raw_compression", self.raw_compression)151 if self.save_raw:152 self.prepare_raw_hdf5(self.raw_compression)153 def process(self):154 Plugin.process(self)155 logger.debug("IntegrateManyFrames.process")156 if self.integration_method == "integrate2d":157 res = numpy.zeros((len(self.input_files), self.npt_azim, self.npt), dtype=numpy.float32) # numpy array for storing data158 else:159 res = numpy.zeros((len(self.input_files), self.npt), dtype=numpy.float32) # numpy array for storing data160 sigma = None161 if self.error_model or self.integration_method == "sigma_clip":162 if self.integration_method == "integrate2d":163 sigma = numpy.zeros((len(self.input_files), self.npt_azim, self.npt), dtype=numpy.float32) # numpy array for storing data164 else:165 sigma = numpy.zeros((len(self.input_files), self.npt), dtype=numpy.float32) # numpy array for storing data166 method = self.ai.__getattribute__(self.integration_method)167 common_param = {"method": self.method,168 "unit": self.unit,169 "dummy": self.dummy,170 "delta_dummy": self.delta_dummy,171 "mask": self.mask,172 "polarization_factor": self.polarization_factor,173 "normalization_factor": self.norm,174 "correctSolidAngle": self.do_SA}175 if self.integration_method in ("integrate1d", "integrate_radial"):176 common_param["npt"] = self.npt177 common_param["error_model"] = self.error_model178 common_param["safe"] = False179 else:180 common_param["npt_rad"] = self.npt181 common_param["npt_azim"] = self.npt_azim182 if self.integration_method == "sigma_clip":183 common_param["thres"] = self.sigma_clip_thresold,184 common_param["max_iter"] = self.sigma_clip_max_iter185 if self.integration_method == "medfilt1d":186 common_param["percentile"] = self.medfilt1d_percentile187 # prepare some tools188 cbf = fabio.open(self.input_files[0])189 bo = ByteOffset(os.path.getsize(self.input_files[0]), cbf.data.size,190 devicetype="gpu")191 shape = cbf.data.shape192 for idx, fname in enumerate(self.input_files):193 logger.debug("process %s: %s", idx, fname)194 if fname.endswith("cbf"):195 raw = cbf.read(fname, only_raw=True)196 data = bo(raw, as_float=False).get().reshape(shape)197 else:198 data = fabio.open(fname).data199 if data is None:200 self.log_error("Failed reading file: %s" % self.input_files[idx],201 do_raise=False)202 continue203 if self.save_raw:204 self.raw_ds[idx] = data205 out = method(data, **common_param)206 res[idx] = out.intensity207 if self.error_model or self.integration_method == "sigma_clip":208 sigma[idx] = out.sigma209 self.save_result(out, res, sigma)210 if self.input.get("delete_incoming"):211 for fname in self.input_files:212 try:213 os.unlink(fname)214 except IOError as err:215 self.log_warning(err)216 def prepare_raw_hdf5(self, filter_=None):217 """Prepare an HDF5 output file for saving raw data218 :param filter_: name of the compression filter 219 """220 kwfilter = {}221 if filter_ == "gzip":222 kwfilter = {"compression": "gzip", "shuffle": True}223 elif filter_ == "lz4":224 kwfilter = {"compression": 32004, "shuffle": True}225 elif filter_ == "bitshuffle":226 kwfilter = {"compression": 32008, "compression_opts": (0, 2)} # enforce lz4 compression227 first_image = self.input_files[0]228 fimg = fabio.open(first_image)229 shape = fimg.data.shape230 stack_shape = (len(self.input_files),) + shape231 first_frame_timestamp = os.stat(first_image).st_ctime232 try:233 self.raw_nxs = pyFAI.io.Nexus(self.save_raw, "a")234 except IOError as error:235 self.log_warning("invalid HDF5 file %s: remove and re-create!\n%s" % (self.save_raw, error))236 os.unlink(self.save_raw)237 self.raw_nxs = pyFAI.io.Nexus(self.save_raw)238 entry = self.raw_nxs.new_entry("entry",239 program_name="dahu",240 title="ID15.raw_data",241 force_time=first_frame_timestamp,242 force_name=True)243 entry["program_name"].attrs["version"] = dahu_version244 entry["plugin_name"] = numpy.string_(".".join((os.path.splitext(os.path.basename(__file__))[0], self.__class__.__name__)))245 entry["plugin_name"].attrs["version"] = version246 coll = self.raw_nxs.new_class(entry, "data", class_type="NXdata")247 try:248 self.raw_ds = coll.require_dataset(name="data", shape=stack_shape,249 dtype=fimg.data.dtype,250 chunks=(1,) + shape,251 **kwfilter)252 except Exception as error:253 logger.error("Error in creating dataset, disabling compression:%s", error)254 self.raw_ds = coll.require_dataset(name="data", shape=stack_shape,255 dtype=fimg.data.dtype,256 chunks=(1,) + shape)257 return self.raw_ds258 def save_result(self, out, I, sigma=None):259 """Save the result of the work as a HDF5 file260 261 :param out: scattering result 262 :param I: Intensities as 2D array263 :param sigma: standard deviation of I as 2D array, is possible 264 """265 logger.debug("IntegrateManyFrames.save_result")266 isotime = numpy.string_(get_isotime())267 try:268 nxs = pyFAI.io.Nexus(self.output_file, "a")269 except IOError as error:270 self.log_warning("invalid HDF5 file %s: remove and re-create!\n%s" % (self.output_file, error))271 os.unlink(self.output_file)272 nxs = pyFAI.io.Nexus(self.output_file)273 entry = nxs.new_entry("entry", program_name="dahu", title="ID15.IntegrateManyFrames ")274 entry["program_name"].attrs["version"] = dahu_version275 entry["plugin_name"] = numpy.string_(".".join((os.path.splitext(os.path.basename(__file__))[0], self.__class__.__name__)))276 entry["plugin_name"].attrs["version"] = version277 entry["input"] = numpy.string_(json.dumps(self.input))278 entry["input"].attrs["format"] = 'json'279 subentry = nxs.new_class(entry, "PyFAI", class_type="NXprocess")280 subentry["program"] = numpy.string_("PyFAI")281 subentry["version"] = numpy.string_(pyFAI.version)282 subentry["date"] = isotime283 subentry["processing_type"] = numpy.string_(self.integration_method)284 coll = nxs.new_class(subentry, "process_%s" % self.integration_method,285 class_type="NXdata")286 metadata_grp = coll.require_group("parameters")287 for key, value in self.ai.getPyFAI().items():288 metadata_grp[key] = numpy.string_(value)289 scale, unit = str(out.unit).split("_", 1)290 coll[scale] = out.radial.astype("float32")291 coll[scale].attrs["interpretation"] = "scalar"292 coll[scale].attrs["unit"] = unit293 coll["I"] = I.astype("float32")294 coll["I"].attrs["interpretation"] = "spectrum"295 coll["I"].attrs["signal"] = "1"296 coll.attrs["signal"] = "I"297 coll.attrs["axes"] = [".", scale]298 if sigma is not None:299 coll["errors"] = sigma.astype("float32")300 coll["errors"].attrs["interpretation"] = "spectrum"301 nxs.close()302 def teardown(self):303 Plugin.teardown(self)304 logger.debug("IntegrateManyFrames.teardown")305 # Create some output data306 self.output["output_file"] = self.output_file307 if self.save_raw:308 self.raw_nxs.close()309 self.output["save_raw"] = self.save_raw310 self.raw_nxs = None...
dribble.py
Source:dribble.py
1#!/usr/bin/env python32# ----------------------------------------------------------------------3# This file is part of the 'Dribble' package for percolation simulations.4# Copyright (c) 2013-2018 Alexander Urban (aurban@atomistic.net)5# ----------------------------------------------------------------------6# This Source Code Form is subject to the terms of the Mozilla Public7# License, v. 2.0. If a copy of the MPL was not distributed with this8# file, You can obtain one at http://mozilla.org/MPL/2.0/.9#10# This program is distributed in the hope that it will be useful, but11# WITHOUT ANY WARRANTY; without even the implied warranty of12# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the13# Mozilla Public License, v. 2.0, for more details.14"""15Dribble - Percolation Simulation on Lattices16Analyze the ionic percolation properties of an input structure.17"""18import argparse19import sys20import time21import numpy as np22from dribble.io import Input23from dribble.percolator import Percolator24from dribble.lattice import Lattice25from dribble.misc import uprint26__author__ = "Alexander Urban"27def check_if_percolating(percolator, inp, save_clusters, tortuosity):28 noccup = percolator.num_occupied29 nspan = percolator.check_spanning(verbose=True,30 save_clusters=save_clusters,31 static_sites=inp.static_sites)32 if (nspan > 0):33 uprint(" The initial structure is percolating.\n")34 uprint(" Fraction of accessible sites: {}\n".format(35 float(nspan)/float(noccup)))36 if tortuosity:37 for c in percolator.percolating_clusters:38 t_min, t_mean, t_std = percolator.get_tortuosity(c)39 uprint(" Tortuosity of cluster {} (min, mean): ".format(c)40 + "{:5.3f}, {:5.3f} +/- {:5.3f}".format(41 t_min, t_mean, t_std))42 uprint("")43 else:44 uprint(" The initial structure is NOT percolating.\n")45 uprint(" Fraction of accessible sites: 0.0\n")46def calc_critical_concentration(percolator, save_clusters, samples,47 file_name, sequence):48 if save_clusters:49 (pc_site_any, pc_site_two, pc_site_all, pc_bond_any,50 pc_bond_two, pc_bond_all) = percolator.percolation_point(51 sequence, samples=samples, file_name=file_name+".vasp")52 else:53 (pc_site_any, pc_site_two, pc_site_all, pc_bond_any,54 pc_bond_two, pc_bond_all) = percolator.percolation_point(55 sequence, samples=samples)56 uprint(" Critical site (bond) concentrations to find a "57 "wrapping cluster\n")58 uprint(" in one or more dimensions p_c1 = {:.8f} ({:.8f})".format(59 pc_site_any, pc_bond_any))60 uprint(" in two or three dimensions p_c2 = {:.8f} ({:.8f})".format(61 pc_site_two, pc_bond_two))62 uprint(" in all three dimensions p_c3 = {:.8f} ({:.8f})".format(63 pc_site_all, pc_bond_all))64 uprint("")65def calc_p_infinity(percolator, samples, save_raw, file_name, sequence):66 plist = np.arange(0.01, 1.00, 0.01)67 (Q, X) = percolator.calc_p_infinity(68 plist, sequence, samples=samples,69 save_discrete=save_raw)70 # integrate susceptibility X in order to normalize it71 intX = np.sum(X)*(plist[1]-plist[0])72 fname = file_name + ".infty"73 uprint(" Writing results to: {}\n".format(fname))74 with open(fname, 'w') as f:75 f.write("# {:^10s} {:>10s} {:>15s} {:>15s}\n".format(76 "p", "P_infty(p)", "Chi(p)", "normalized"))77 for p in range(len(plist)):78 f.write(" {:10.8f} {:10.8f} {:15.8f} {:15.8f}\n".format(79 plist[p], Q[p], X[p], X[p]/intX))80def calc_p_wrapping(percolator, samples, save_raw, file_name, sequence):81 plist = np.arange(0.01, 1.00, 0.01)82 (Q, Qc) = percolator.calc_p_wrapping(83 plist, sequence, samples=samples,84 save_discrete=save_raw)85 fname = file_name + ".wrap"86 uprint(" Writing results to: {}\n".format(fname))87 with open(fname, 'w') as f:88 f.write("# {:^10s} {:>10s} {:>10s}\n".format(89 "p", "P_wrap(p)", "cumulative"))90 for p in range(len(plist)):91 f.write(" {:10.8f} {:10.8f} {:10.8f}\n".format(92 plist[p], Q[p], Qc[p]))93def calc_inaccessible_sites(percolator, samples, save_raw, file_name,94 sequence, species):95 plist = np.arange(0.01, 1.00, 0.01)96 (F_inacc, nclus) = percolator.inaccessible_sites(97 plist, sequence, species, samples=samples,98 save_discrete=save_raw)99 fname = file_name + ".inacc"100 uprint(" Writing results to: {}\n".format(fname))101 with open(fname, 'w') as f:102 f.write("# {:^10s} {:>10s} {:>10s}\n".format(103 "p", "F_inacc(p)", "N_percol(p)"))104 for p in range(len(plist)):105 f.write(" {:10.8f} {:10.8f} {:12.6f}\n".format(106 plist[p], F_inacc[p], nclus[p]))107def calc_mean_tortuosity(percolator, samples, file_name, sequence):108 F_tort = percolator.mean_tortuosity(109 sequence, samples=samples)110 fname = file_name + ".tortuosity"111 uprint(" Writing results to: {}\n".format(fname))112 with open(fname, 'w') as f:113 f.write("# {:^10s} {:^10s} {:s}\n".format(114 "N", "p", "Tortuosity(p)"))115 N = len(F_tort)116 for i, T in enumerate(F_tort):117 f.write(" {:10d} {:10.8f} {:10.8f}\n".format(118 i+1, (i+1)/float(N), T))119def compute_percolation(input_file, structure_file, samples,120 save_clusters, save_raw, file_name, pc, check,121 pinf, pwrap, inaccessible, tortuosity,122 mean_tortuosity, supercell):123 if not (check or pc or pinf or pwrap or inaccessible or mean_tortuosity):124 print("\n Nothing to do.")125 print(" Please specify the quantity to be calculated.")126 print(" Use the `--help' flag to list all options.\n")127 sys.exit()128 input_params = {}129 if structure_file is not None:130 uprint("\n Reading structure from file: {}".format(structure_file))131 input_params['structure'] = structure_file132 uprint("\n Parsing input file '{}'...".format(input_file), end="")133 inp = Input.from_file(input_file, **input_params)134 uprint(" done.")135 uprint("\n Setting up lattice and neighbor lists...", end="")136 lattice = Lattice.from_input_object(inp, supercell=supercell)137 uprint(" done.")138 uprint(lattice)139 uprint(" Initializing percolator...", end="")140 percolator = Percolator.from_input_object(inp, lattice, verbose=True)141 uprint(" done.")142 uprint("\n MC percolation simulation\n -------------------------\n")143 if check: # check, if initial structure is percolating144 check_if_percolating(percolator, inp, save_clusters, tortuosity)145 if pc: # calculate critical site concentrations146 calc_critical_concentration(percolator, save_clusters, samples,147 file_name, inp.flip_sequence)148 if pinf: # estimate P_infinity(p)149 calc_p_infinity(percolator, samples, save_raw, file_name,150 inp.flip_sequence)151 if pwrap: # estimate P_wrapping(p)152 calc_p_wrapping(percolator, samples, save_raw, file_name,153 inp.flip_sequence)154 if inaccessible is not None: # fraction of inaccessible sites155 calc_inaccessible_sites(percolator, samples, save_raw,156 file_name, inp.flip_sequence,157 inaccessible)158 if mean_tortuosity: # tortuosity as function of concentration159 calc_mean_tortuosity(percolator, samples, file_name,160 inp.flip_sequence)161 dt = time.gmtime(time.clock())162 uprint(" All done. Elapsed CPU time: {:02d}h{:02d}m{:02d}s\n".format(163 dt.tm_hour, dt.tm_min, dt.tm_sec))164def main():165 parser = argparse.ArgumentParser(166 description=__doc__,167 formatter_class=argparse.RawDescriptionHelpFormatter)168 parser.add_argument(169 "input_file",170 help="Input file in JSON format")171 parser.add_argument(172 "structure_file",173 help="Optional structure file in VASP's POSCAR format.",174 default=None,175 nargs="?")176 parser.add_argument(177 "--supercell",178 help="List of multiples of the lattice cell" +179 " in the three lattice directions",180 type=int,181 default=(1, 1, 1),182 nargs=3)183 parser.add_argument(184 "--inaccessible", "-i",185 help="Calculate fraction of inaccessible sites for given "186 "reference species",187 type=str,188 default=None,189 metavar="SPECIES")190 parser.add_argument(191 "--pc", "-p",192 help="Calculate critical site concentrations",193 action="store_true")194 parser.add_argument(195 "--check",196 help="Check, if the initial structure is percolating.",197 action="store_true")198 parser.add_argument(199 "--pinf", "-s",200 help="Estimate P_infinity and percolation susceptibility",201 action="store_true")202 parser.add_argument(203 "--pwrap", "-w",204 help="Estimate P_wrap(p)",205 action="store_true")206 parser.add_argument(207 "--tortuosity", "-t",208 help="Compute tortuosity of the percolating clusters as function "209 "of the concentration. Together with '--check', only compute "210 "tortuosity of the input structure.",211 action="store_true")212 parser.add_argument(213 "--samples",214 help="number of samples to be averaged",215 type=int,216 default=500)217 parser.add_argument(218 "--file-name",219 help="base file name for all output files",220 default="percol")221 parser.add_argument(222 "--save-clusters",223 help="save wrapping clusters to file",224 action="store_true")225 parser.add_argument(226 "--save-raw",227 help="Also store raw data before convolution (where available).",228 action="store_true")229 parser.add_argument(230 "--debug",231 help="run in debugging mode",232 action="store_true")233 args = parser.parse_args()234 if args.debug:235 np.random.seed(seed=1)236 if args.tortuosity and not args.check:237 mean_tortuosity = True238 else:239 mean_tortuosity = False240 compute_percolation(input_file=args.input_file,241 structure_file=args.structure_file,242 samples=args.samples,243 save_clusters=args.save_clusters,244 save_raw=args.save_raw,245 file_name=args.file_name,246 pc=args.pc,247 check=args.check,248 pinf=args.pinf,249 pwrap=args.pwrap,250 inaccessible=args.inaccessible,251 tortuosity=args.tortuosity,252 mean_tortuosity=mean_tortuosity,253 supercell=args.supercell)254if (__name__ == "__main__"):...
get_chargecloud_data.py
Source:get_chargecloud_data.py
1import pandas as pd 2import typing 3import requests4import datetime5import logging 6import logging.config7import time 8import json 9import os10logger = logging.getLogger(__name__)11from settings import LOGGING_CONFIG12BASE_URL_CHARGECLOUD = "https://new-poi.chargecloud.de"13SCRAPING_INTERVAL = 1014CITIES_CC = ['koeln','dortmund','bonn','muenster','kiel',15 'chemnitz','krefeld','leverkusen','heidelberg',16 'solingen','ingolstadt','pforzheim','goettingen',17 'erlangen','tuebingen','bayreuth','bocholt','dormagen',18 'rastatt','hof','weinheim','bruchsal','nettetal','ansbach',19 'schwabach','ettlingen','crailsheim','deggendorf','forchheim',20 'bretten','buehl','zirndorf','roth','calw','herzogenaurach','wertheim',21 'kitzingen','lichtenfels']22DIR_SAVE_API_RESULTS = "../data/scraped_data"23SAVE_RAW = True24def scrape_cp_cities(cities: typing.List[str], 25 dir_save: str, 26 save_raw: bool = False): 27 """Scrape charging point information for a given list of cities28 Args:29 cities (typing.List[str], optional): list of cities to scrape.30 dir_save (str): directory for saving scraped cities. 31 save_raw (bool, optional): whether to save API result as raw json (True) or pickle file (False). Defaults to False.32 """ 33 data_cities = {}34 now = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") 35 for city in cities: 36 r = requests.get(BASE_URL_CHARGECLOUD + "/" + city)37 try: 38 data = json.loads(r.text)39 data_cities[city] = data40 logger.debug(f"Successfully scraped '{city}' at {now}.")41 except: 42 logger.error(f"Error occurred while scraping '{city}' at {now}.")43 if save_raw: 44 fname = now + "_cp_data_cities.json"45 path_save = os.path.join(dir_save, fname)46 with open(path_save, 'w', encoding='utf-8') as f:47 json.dump(data_cities, f)48 else: 49 fname = now + "_cp_data_cities.pkl"50 path_save = os.path.join(dir_save, fname)51 pd.to_pickle(data_cities, path_save)52 53def call_chargecloud_api(scraping_interval: typing.Union[int, float] = SCRAPING_INTERVAL, 54 cities: typing.List[str] = CITIES_CC, 55 dir_save_api_results: str = DIR_SAVE_API_RESULTS, 56 save_raw: str = SAVE_RAW): 57 """Scrape a given list of cities from chargecloud API in a given interval.58 Args:59 scraping_interval (typing.Union[int, float], optional): interval in minutes between API lookups. Defaults to SCRAPING_INTERVAL.60 cities (typing.List[str], optional): list of cities to scrape61 dir_save (str, optional): directory for saving scraped cities. Defaults to "../data/scraped_data".62 save_raw (bool, optional): whether to save API result as raw json (True) or pickle file (False). Defaults to False.63 """ 64 while True: 65 66 now = datetime.datetime.now().strftime("%Y/%m/%d_%H:%M:%S")67 info_msg = f"Scraping cities: {now}"68 logger.info(info_msg)69 70 scrape_cp_cities(cities=cities, dir_save=dir_save_api_results, save_raw=save_raw)71 72 # API call of all cities takes approx. 15 seconds, therefore subtract it from specified interval73 sleep = max(scraping_interval*60 - 15, 0)74 time.sleep(sleep)75if __name__ == '__main__':76 logging.config.dictConfig(LOGGING_CONFIG)...
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