How to use update_stream_mode method in localstack

Best Python code snippet using localstack_python

client.pyi

Source:client.pyi

...354        shards.355        [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.24.58/reference/services/kinesis.html#Kinesis.Client.update_shard_count)356        [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_kinesis/client.html#update_shard_count)357        """358    def update_stream_mode(359        self, *, StreamARN: str, StreamModeDetails: "StreamModeDetailsTypeDef"360    ) -> None:361        """362        Updates the capacity mode of the data stream.363        [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.24.58/reference/services/kinesis.html#Kinesis.Client.update_stream_mode)364        [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_kinesis/client.html#update_stream_mode)365        """366    @overload367    def get_paginator(self, operation_name: Literal["describe_stream"]) -> DescribeStreamPaginator:368        """369        [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.24.58/reference/services/kinesis.html#Kinesis.Paginator.DescribeStream)370        [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_kinesis/paginators.html#describestreampaginator)371        """372    @overload...

responses.py

Source:responses.py

...238    def stop_stream_encryption(self):239        stream_name = self.parameters.get("StreamName")240        self.kinesis_backend.stop_stream_encryption(stream_name=stream_name)241        return json.dumps(dict())242    def update_stream_mode(self):243        stream_arn = self.parameters.get("StreamARN")244        stream_mode = self.parameters.get("StreamModeDetails")245        self.kinesis_backend.update_stream_mode(stream_arn, stream_mode)...

ffr_run_dof.py

Source:ffr_run_dof.py

1import cPickle2import multiprocessing3import os4import sys5import time6import arcpy7import numpy as np8import pandas as pd9import indices.dof10import tools.helper as tool11from config import config12fd = config.var13def run_dof(stamp, para, paths):14    """15    Set up for multiprocessing. Creates a isolated processing environment,16    where each hydrological river basin is processed separately17    :param stamp: timestamp18    :param para: parameters19    :param paths: pathnames20    :return:21    """22    dam_fc = para["dams_fc"]23    streams_fc = para["streams_fc"]24    update_stream_mode = para["update_mode"]25    drf_upstream = para["drf_upstream"]26    drf_downstream = para["drf_downstream"]27    barrier_inc_field = para["barrier_inc_field"]28    dof_field = para["dof_field"]29    dof_mode = para["dof_mode"]30    use_dam_level_df = para["use_dam_level_df"]31    gdb_full_path = paths["gdb_full_path"]32    output_folder = para["output_folder"]33    output_folder = os.path.join(output_folder, "Results_" + stamp)34    scratch_ws = output_folder + r"\Scratch"35    tool.delete_path(scratch_ws)36    tool.create_path(scratch_ws)37    print ("Discharge range factor used (upstream): %s" % drf_upstream)38    print ("Discharge range factor used (downstream): %s" % drf_downstream)39    in_basins = list(get_unique(dam_fc, barrier_inc_field))40    print ("Loading {}".format(str(streams_fc)))41    streams = load_streams(streams_fc, dof_field)42    dams_temp = load_dams(dam_fc, barrier_inc_field, use_dam_level_df)43    pooled = True44    if pooled:45        pool = multiprocessing.Pool(8)46        jobs = []47        i = 148        print ("Starting analysis pooled")49        for basin in in_basins:50            # https://stackoverflow.com/a/8533626/34464751            # Much faster than querying the global dataset on disk is52            # to load the global dataset into memory first and then query it53            # using numpy methods54            streams_sel = np.copy(streams[streams[fd.BAS_ID] == basin])55            # Also faster than querying the feature dataset on disk,56            # it is better to to load it once and then57            # use numpy indexing to get the dams we want58            dams_sel = np.copy(dams_temp[dams_temp[fd.BAS_ID] == basin])59            jobs.append(pool.apply_async(run_basin, (streams_sel, dams_sel, basin,60                                                     stamp + str(i),61                                                     scratch_ws,62                                                     dof_field,63                                                     drf_upstream,64                                                     drf_downstream,65                                                     dof_mode,66                                                     use_dam_level_df)))67            i += 168        pool.close()69        pool.join()70        out_basin = [job.get() for job in jobs]71    else:72        jobs = []73        i = 174        print ("Starting analysis unpooled")75        for basin in in_basins:76            # https://stackoverflow.com/a/8533626/34464777            # Much faster than querying the global dataset on disk is78            # to load the global dataset into memory first and then query it79            # using numpy methods80            streams_sel = np.copy(streams[streams[fd.BAS_ID] == basin])81            # Also faster than querying the feature dataset on disk,82            # it is better to to load it once and then83            # use numpy indexing to get the dams we want84            dams_sel = np.copy(dams_temp[dams_temp[fd.BAS_ID] == basin])85            jobs.append(run_basin(streams_sel,86                                  dams_sel,87                                  basin,88                                  stamp + str(i),89                                  scratch_ws,90                                  dof_field,91                                  drf_upstream,92                                  drf_downstream,93                                  dof_mode,94                                  use_dam_level_df))95            i += 196        out_basin = [job for job in jobs]97    # Merge the temporary outputs98    print("Merging temporary outputs into output table %s ..." % gdb_full_path)99    i = 0100    tbl = {}101    for bas in out_basin:102        i += 1103        with open(bas, 'rb') as fp:104            tbl[i] = cPickle.load(fp)105    merged = np.concatenate(tbl.values(), 1)106    df = pd.DataFrame(merged)107    # Turn panda to numpy108    # https://my.usgs.gov/confluence/display/cdi/pandas.DataFrame+to+ArcGIS+Table109    x = np.array(np.rec.fromrecords(df.values))110    names = df.dtypes.index.tolist()111    x.dtype.names = tuple(names)112    output_table_location = gdb_full_path + "\\" + "dof"113    arcpy.da.NumPyArrayToTable(x, output_table_location)114    tool.add_index(lyr=merged, field_name="GOID")115    # Update automatically116    if update_stream_mode.lower() == "yes":117        print "Updating dof values in database {} ".format(streams_fc)118        tool.copy_between(to_join_fc=streams_fc,119                          to_join_field="GOID",120                          IntoJoinField=dof_field,121                          FromJoinFC=output_table_location,122                          FromJoinField="GOID",123                          FromValueField=dof_field,124                          over_mode=True,125                          over_value=0)126    tool.delete_path(scratch_ws)127def run_basin(streams, dams, basin, stamp, scratchws, dof_field, drf_upstream,128              drf_downstream, mode, use_dam_level_df):129    """130    Calculate DOF for all barriers in a specified river basin131    :param streams:132    :param dams:133    :param basin:134    :param stamp:135    :param scratchws:136    :param dof_field:137    :param drf_upstream:138    :param drf_downstream:139    :param mode:140    :param use_dam_level_df:141    :return:142    """143    # Setup isolated path and environment144    temp_out_folder = set_environment(scratchws, basin, stamp)145    # Update network ids for rivers and dams146    tool.update_stream_routing_index(streams)147    tool.update_dam_routing_index(dams, streams)148    # Calculate and write DOF into designated field149    indices.dof.calculate_DOF(dams, streams, mode, dof_field,150                              drf_upstream, drf_downstream, use_dam_level_df)151    # Export table to temporay geodatabase152    final_table = export(streams, basin, temp_out_folder)153    # returns the path to table for later merging154    return final_table155def get_unique(dam_table, inc_field):156    """157    Calculates a list of unique river basins that need to be processed based158    on the barriers to be considered.159    :param dam_table: numpy array with dams to process160    :param inc_field: field to determine dams to include161    :return: List of river basins162    """163    flds = [fd.BAS_ID, inc_field]164    where_clause = inc_field + ' = 1'165    where_clause = where_clause.replace("'", "")166    dams = arcpy.da.TableToNumPyArray(167        dam_table, flds, where_clause, null_value=-1)168    in_basins = np.unique(dams[fd.BAS_ID])169    if 0 in in_basins:170        sys.exit(171            "One of the dams has a basin ID of 0. BAS_ID cannot be zero. "172            "Please provide a BAS_ID other than 0 for "173            "all dams ")174    return in_basins175def set_environment(scratch_ws, basin, stamp):176    """177    Creates isolated output paths for specific basin using timestamp in scratch workspace.178    :param scratch_ws: scratch workspace179    :param basin:  river basin to process180    :param stamp: timestamp181    :return: fully specified pathname182    """183    out_folder = "basin_" + str(basin) + "_" + str(stamp)184    fullpath = os.path.join(scratch_ws, out_folder)185    tool.create_path(fullpath)186    return fullpath187def load_dams(dam_table, inc_field, use_dam_level_df):188    """189    This function loads from the database190    :param dam_table:191    :param inc_field:192    :param use_dam_level_df:193    :return: numpy array with dams194    """195    if use_dam_level_df.lower() == "yes":196        flds = [fd.BAS_ID, fd.GOID, fd.STOR_MCM, fd.DFU, fd.DFD,197                inc_field]198    else:199        flds = [fd.BAS_ID, fd.GOID, fd.STOR_MCM, inc_field]200    tool.check_fields(dam_table, flds)201    whereBClause2 = inc_field + ' > 0'202    whereBClause3 = ' AND ' + fd.INC + ' > 0'203    whereBClause4 = whereBClause2 + whereBClause3204    whereBClause5 = whereBClause4.replace("'", "")205    dams = arcpy.da.TableToNumPyArray(206        dam_table, flds, whereBClause5, null_value=0)207    return dams208def load_streams(stream_table, dof_field):209    """210    Loads the streams and adds a field for holding the DOF values211    :param stream_table: numpy array representing the river reaches212    :param dof_field: field name to store DOF results213    :return:214    """215    flds = [fd.BAS_ID, fd.GOID, fd.NOID, fd.NDOID, fd.NUOID, fd.RIV_ORD,216            fd.DIS_AV_CMS, fd.HYFALL]217    tool.check_fields(stream_table, flds)218    arr = arcpy.da.TableToNumPyArray(stream_table, flds, null_value=0)219    arr = tool.add_fields(arr, [(str(dof_field), 'f4')])220    arr[dof_field] = 0221    return arr222def export(streams, basin, folder):223    suffix = ".bas"224    name = "out_" + str(basin)225    fullname = name + suffix226    fullpath = os.path.join(folder, fullname)227    tool.save_as_cpickle(pickle_object=streams,228                         folder=folder,229                         name=name,230                         file_extension=suffix)231    return fullpath232def create_gdb_workspace(gdb_folder, gdb_name):233    """234    Creates a path and a geodatabase with timestamp as name235    :param stamp:236    :param gdb_folder:237    :param gdb_name:238    :return: full path and geodatabase name239    """240    if not os.path.exists(gdb_folder):241        os.makedirs(gdb_folder)242    gdb_file_name = gdb_name + ".gdb"243    gdb_full_path = gdb_folder + "\\" + gdb_file_name244    arcpy.CreateFileGDB_management(gdb_folder, gdb_file_name)...

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