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reworking basic parcels input summary
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,112 +1,45 @@ | ||
| # Load buffered parcel data and summarize by Regional Growth Center | ||
| import pandana as pdna | ||
| import pandas as pd | ||
| import numpy as np | ||
| import os, sys | ||
| sys.path.append(os.getcwd()) | ||
| sys.path.append(os.path.join(os.getcwd(),'scripts/accessibility')) | ||
| from accessibility_configuration import * | ||
| from standard_summary_configuration import * | ||
| from emme_configuration import * | ||
| import input_configuration | ||
| import re | ||
| import sys | ||
| from pyproj import Proj, transform | ||
| import h5py | ||
|
|
||
| print "running parcel summary" | ||
|
|
||
| def assign_nodes_to_dataset(dataset, network, column_name, x_name, y_name): | ||
| """Adds an attribute node_ids to the given dataset.""" | ||
| dataset[column_name] = network.get_node_ids(dataset[x_name].values, dataset[y_name].values) | ||
|
|
||
| def process_dist_attribute(parcels, network, name, x, y): | ||
| network.set_pois(name, x, y) | ||
| res = network.nearest_pois(max_dist, name, num_pois=1, max_distance=999) | ||
| res[res != 999] = (res[res != 999]/5280.).astype(res.dtypes) # convert to miles | ||
| res_name = "dist_%s" % name | ||
| parcels[res_name] = res.loc[parcels.node_ids].values | ||
|
|
||
| return parcels | ||
| import pandas as pd | ||
|
|
||
| def parcel_summary(): | ||
| """ | ||
| Summarize parcels by RGC for quick check of min, mean, max. | ||
| Summarize parcels for quick check of totals and means | ||
| """ | ||
| main_dir = os.path.abspath('') | ||
| try: | ||
| parcels = pd.read_table(main_dir + "/inputs/" + buffered_parcels, sep=' ') | ||
| except: | ||
| print "Missing 'buffered_parcels.dat'" | ||
|
|
||
| try: | ||
| map = pd.read_csv(main_dir + "/scripts/summarize/inputs/" + parcel_urbcen_map) | ||
| except: | ||
| print "Missing 'parcels_in_urbcens.csv'" | ||
|
|
||
| # Join the urban center location to the parcels file | ||
| parcels = pd.merge(parcels, map, left_on='parcelid', right_on='hhparcel') | ||
| print "Loading parcel data to summarize..." | ||
|
|
||
| # Summarize parcel fields by urban center | ||
| mean_by_urbcen = pd.DataFrame(parcels.groupby('NAME').mean()) | ||
| min_by_urbcen = pd.DataFrame(parcels.groupby('NAME').min()) | ||
| max_by_urbcen = pd.DataFrame(parcels.groupby('NAME').max()) | ||
| std_by_urbcen = pd.DataFrame(parcels.groupby('NAME').std()) | ||
|
|
||
| # Write results to separate worksheets in an Excel file | ||
| excel_writer = pd.ExcelWriter(main_dir + '/outputs/' + parcel_file_out) | ||
|
|
||
| mean_by_urbcen.to_excel(excel_writer=excel_writer, sheet_name='Mean') | ||
| min_by_urbcen.to_excel(excel_writer=excel_writer, sheet_name='Min') | ||
| max_by_urbcen.to_excel(excel_writer=excel_writer, sheet_name='Max') | ||
| std_by_urbcen.to_excel(excel_writer=excel_writer, sheet_name='Std Dev') | ||
|
|
||
| def transit_access(): | ||
| transit_df = pd.DataFrame.from_csv(transit_stops_name) | ||
|
|
||
| # Load parcel data | ||
| parcels = pd.DataFrame.from_csv(parcels_file_name, sep = " ", index_col=None ) | ||
|
|
||
| # Check for missing data | ||
| for col_name in parcels.columns: | ||
| # daysim does not use EMPRSC_P | ||
| if col_name != 'EMPRSC_P': | ||
| if parcels[col_name].sum() == 0: | ||
| print col_name + ' column sum is zero! Exiting program.' | ||
| sys.exit(1) | ||
|
|
||
| # nodes must be indexed by node_id column, which is the first column | ||
| nodes = pd.DataFrame.from_csv(nodes_file_name) | ||
| links = pd.DataFrame.from_csv(links_file_name, index_col = None ) | ||
|
|
||
| # assign impedence | ||
| imp = pd.DataFrame(links.Shape_Length) | ||
| imp = imp.rename(columns = {'Shape_Length':'distance'}) | ||
|
|
||
| # create pandana network | ||
| net = pdna.network.Network(nodes.x, nodes.y, links.from_node_id, links.to_node_id, imp) | ||
| for dist in distances: | ||
| net.precompute(dist) | ||
|
|
||
| # set the number of pois on the network for the distance variables (transit + 1 for parks) | ||
| net.init_pois(len(transit_modes)+1, max_dist, 1) | ||
|
|
||
| # assign network nodes to parcels, for buffer variables | ||
| assign_nodes_to_dataset(parcels, net, 'node_ids', 'XCOORD_P', 'YCOORD_P') | ||
|
|
||
| # calc the distance from each parcel to nearest transit stop by type | ||
| for attr in ['frequent']: | ||
| print attr | ||
| # get the records/locations that have this type of transit: | ||
| transit_type_df = transit_df.loc[(transit_df[attr] == 1)] | ||
| parcels = process_dist_attribute(parcels, net, attr, transit_type_df["x"], transit_type_df["y"]) | ||
| #Some parcels share the same network node and therefore have 0 distance. Recode this to .01. | ||
| field_name = "dist_%s" % attr | ||
| parcels.ix[parcels[field_name]==0, field_name] = .01 | ||
|
|
||
| parcels.to_csv(transit_access_outfile) | ||
| # Load parcels_urbansim input | ||
| df = pd.read_csv(r'inputs/scenario/landuse/parcels_urbansim.txt', delim_whitespace=True) | ||
|
|
||
| # Save results in flat text file | ||
| results_df = pd.DataFrame() | ||
|
|
||
| # Calculate totals for jobs by sector, households, students, and parking spaces | ||
| cols = [u'empedu_p', u'empfoo_p', u'empgov_p', u'empind_p', u'empmed_p', | ||
| u'empofc_p', u'empoth_p', u'empret_p', u'emprsc_p', u'empsvc_p', | ||
| u'emptot_p', u'hh_p','stugrd_p', u'stuhgh_p', u'stuuni_p', | ||
| u'parkdy_p', u'parkhr_p'] | ||
|
|
||
| _df = df[cols] | ||
| # Append results to results_df | ||
| results_df['value'] = _df.sum() | ||
| results_df['field'] = results_df.index | ||
| results_df.reset_index(inplace=True, drop=True) | ||
| results_df['measure'] = 'sum' | ||
|
|
||
| # Calculate average parking price | ||
| _df = pd.DataFrame(df[['ppricdyp','pprichrp']].mean(), | ||
| columns=['value']) | ||
| _df['measure'] = 'mean' | ||
| _df['field'] = _df.index | ||
| _df.reset_index(inplace=True, drop=True) | ||
| results_df = results_df.append(_df) | ||
|
|
||
| _df = pd.DataFrame(df[['ppricdyp','pprichrp']].max(), | ||
| columns=['value']) | ||
| _df['measure'] = 'max' | ||
| _df['field'] = _df.index | ||
| _df.reset_index(inplace=True, drop=True) | ||
| results_df = results_df.append(_df) | ||
|
|
||
| results_df.to_csv(r'outputs/landuse/parcels_urbansim_summary.txt', index=False) | ||
|
|
||
| if __name__ == '__main__': | ||
| parcel_summary() | ||
| transit_access() | ||
| parcel_summary() |
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