From 5171e56588333a7e13a67ab647fddb6b1ca8259b Mon Sep 17 00:00:00 2001 From: Gareth Simons Date: Wed, 26 May 2021 17:42:54 +0100 Subject: [PATCH] Splits compute_aggregated into compute_landuses and compute_stats --- cityseer/algos/data.py | 203 +++++------ cityseer/metrics/layers.py | 330 ++++++++---------- cityseer/metrics/networks.py | 5 - docs/content/intro.md | 10 +- docs/content/metrics/layers.md | 283 ++++++++------- docs/content/metrics/networks.md | 2 - docs/generateDocs.js | 4 +- docs/package.json | 8 +- docs/src/assets/plots/images/assignment.png | Bin 175559 -> 175559 bytes .../plots/images/assignment_decomposed.png | Bin 180918 -> 180905 bytes .../assets/plots/images/assignment_plot.png | Bin 184478 -> 184473 bytes docs/src/assets/plots/images/betas.png | Bin 40430 -> 40430 bytes docs/src/assets/plots/images/graph.png | Bin 129155 -> 129155 bytes docs/src/assets/plots/images/graph_after.png | Bin 129513 -> 129513 bytes docs/src/assets/plots/images/graph_before.png | Bin 129513 -> 129513 bytes .../assets/plots/images/graph_cleaning_1.png | Bin 878958 -> 877913 bytes .../assets/plots/images/graph_cleaning_2.png | Bin 810004 -> 810441 bytes .../assets/plots/images/graph_cleaning_3.png | Bin 780647 -> 782000 bytes .../assets/plots/images/graph_cleaning_4.png | Bin 786739 -> 788271 bytes .../assets/plots/images/graph_cleaning_5.png | Bin 731966 -> 732034 bytes docs/src/assets/plots/images/graph_colour.png | Bin 190074 -> 190074 bytes .../assets/plots/images/graph_decomposed.png | Bin 129478 -> 129478 bytes docs/src/assets/plots/images/graph_dual.png | Bin 247369 -> 247369 bytes .../src/assets/plots/images/graph_example.png | Bin 129513 -> 129513 bytes docs/src/assets/plots/images/graph_simple.png | Bin 111432 -> 111432 bytes .../assets/plots/images/intro_mixed_uses.png | Bin 361963 -> 361963 bytes .../plots/images/intro_segment_harmonic.png | Bin 286288 -> 286288 bytes .../images/osmnx_cityseer_simplification.png | Bin 982077 -> 981723 bytes .../plots/images/osmnx_simplification.png | Bin 985083 -> 985479 bytes setup.py | 4 +- tests/algos/test_data.py | 257 +++++++------- tests/metrics/test_layers.py | 319 +++++++---------- tests/metrics/test_networks.py | 11 - tests/tools/test_graphs.py | 15 +- 34 files changed, 650 insertions(+), 801 deletions(-) diff --git a/cityseer/algos/data.py b/cityseer/algos/data.py index 419a87d0..3e40c7fd 100644 --- a/cityseer/algos/data.py +++ b/cityseer/algos/data.py @@ -420,18 +420,15 @@ def aggregate_landuses(node_data: np.ndarray, checks.check_data_map(data_map, check_assigned=True) # raises ValueError data points are not assigned to a network checks.check_distances_and_betas(distances, betas) # check landuse encodings - compute_landuses = False if len(landuse_encodings) == 0: - if len(mixed_use_hill_keys) != 0 or len(mixed_use_other_keys) != 0 or len(accessibility_keys) != 0: - raise ValueError('Mixed use metrics or land-use accessibilities require an array of landuse labels.') + raise ValueError('Mixed use metrics or land-use accessibilities require an array of landuse labels.') elif len(landuse_encodings) != len(data_map): raise ValueError('The number of landuse encodings does not match the number of data points.') else: checks.check_categorical_data(landuse_encodings) # catch completely missing metrics if len(mixed_use_hill_keys) == 0 and len(mixed_use_other_keys) == 0 and len(accessibility_keys) == 0: - raise ValueError( - 'No metrics specified, please specify at least one metric to compute.') + raise ValueError('No metrics specified, please specify at least one metric to compute.') # catch missing qs if len(mixed_use_hill_keys) != 0 and len(qs) == 0: raise ValueError('Hill diversity measures require that at least one value of q is specified.') @@ -491,18 +488,16 @@ def disp_check(disp_matrix): netw_nodes_live = node_data[:, 2] # setup data structures # hill mixed uses are structured separately to take values of q into account - mixed_use_hill_data = np.full((4, q_n, d_n, netw_n), np.nan) # 4 dim - mixed_use_other_data = np.full((3, d_n, netw_n), np.nan) # 3 dim + mixed_use_hill_data = np.full((4, q_n, d_n, netw_n), 0.0) # 4 dim + mixed_use_other_data = np.full((3, d_n, netw_n), 0.0) # 3 dim accessibility_data = np.full((len(accessibility_keys), d_n, netw_n), 0.0) accessibility_data_wt = np.full((len(accessibility_keys), d_n, netw_n), 0.0) # iterate through each vert and aggregate + # parallelise over n nodes: + # each distance or stat array index is therefore only touched by one thread at a time + # i.e. no need to use inner array deductions as with centralities steps = int(netw_n / 10000) for netw_src_idx in prange(netw_n): - # shadowed arrays for non-race reductions - _mixed_use_hill_data = np.full((4, q_n, d_n, netw_n), np.nan) # 4 dim - _mixed_use_other_data = np.full((3, d_n, netw_n), np.nan) # 3 dim - _accessibility_data = np.full((len(accessibility_keys), d_n, netw_n), 0.0) - _accessibility_data_wt = np.full((len(accessibility_keys), d_n, netw_n), 0.0) if not suppress_progress: checks.progress_bar(netw_src_idx, netw_n, steps) # only compute for live nodes @@ -543,8 +538,8 @@ def disp_check(disp_matrix): # if within distance, and if in accessibility keys, then aggregate accessibility too for ac_idx, ac_code in enumerate(accessibility_keys): if ac_code == cl_code: - _accessibility_data[ac_idx, d_idx, netw_src_idx] += 1 - _accessibility_data_wt[ac_idx, d_idx, netw_src_idx] += np.exp(-b * data_dist) + accessibility_data[ac_idx, d_idx, netw_src_idx] += 1 + accessibility_data_wt[ac_idx, d_idx, netw_src_idx] += np.exp(-b * data_dist) # if a match was found, then no need to check others break # mixed uses can be calculated now that the local class counts are aggregated @@ -558,37 +553,32 @@ def disp_check(disp_matrix): for mu_hill_key in mixed_use_hill_keys: for q_idx, q_key in enumerate(qs): if mu_hill_key == 0: - _mixed_use_hill_data[0, q_idx, d_idx, netw_src_idx] = \ + mixed_use_hill_data[0, q_idx, d_idx, netw_src_idx] = \ diversity.hill_diversity(cl_counts, q_key) elif mu_hill_key == 1: - _mixed_use_hill_data[1, q_idx, d_idx, netw_src_idx] = \ + mixed_use_hill_data[1, q_idx, d_idx, netw_src_idx] = \ diversity.hill_diversity_branch_distance_wt(cl_counts, cl_nearest, q=q_key, beta=b) elif mu_hill_key == 2: - _mixed_use_hill_data[2, q_idx, d_idx, netw_src_idx] = \ + mixed_use_hill_data[2, q_idx, d_idx, netw_src_idx] = \ diversity.hill_diversity_pairwise_distance_wt(cl_counts, cl_nearest, q=q_key, beta=b) # land-use classification disparity hill diversity # the wt matrix can be used without mapping because cl_counts is based on all classes # regardless of whether they are reachable elif mu_hill_key == 3: - _mixed_use_hill_data[3, q_idx, d_idx, netw_src_idx] = \ + mixed_use_hill_data[3, q_idx, d_idx, netw_src_idx] = \ diversity.hill_diversity_pairwise_matrix_wt(cl_counts, wt_matrix=cl_disparity_wt_matrix, q=q_key) for mu_other_key in mixed_use_other_keys: if mu_other_key == 0: - _mixed_use_other_data[0, d_idx, netw_src_idx] = \ + mixed_use_other_data[0, d_idx, netw_src_idx] = \ diversity.shannon_diversity(cl_counts) elif mu_other_key == 1: - _mixed_use_other_data[1, d_idx, netw_src_idx] = \ + mixed_use_other_data[1, d_idx, netw_src_idx] = \ diversity.gini_simpson_diversity(cl_counts) elif mu_other_key == 2: - _mixed_use_other_data[2, d_idx, netw_src_idx] = \ + mixed_use_other_data[2, d_idx, netw_src_idx] = \ diversity.raos_quadratic_diversity(cl_counts, wt_matrix=cl_disparity_wt_matrix) - # reduce - mixed_use_hill_data += _mixed_use_hill_data - mixed_use_other_data += _mixed_use_other_data - accessibility_data += _accessibility_data - accessibility_data_wt += _accessibility_data_wt # send the data back in the same types and same order as the original keys - convert to int for indexing mu_hill_k_int = np.full(len(mixed_use_hill_keys), 0) for i, k in enumerate(mixed_use_hill_keys): @@ -597,9 +587,9 @@ def disp_check(disp_matrix): for i, k in enumerate(mixed_use_other_keys): mu_other_k_int[i] = k - return mixed_use_hill_data[mu_hill_k_int], \ + return mixed_use_hill_data[mu_hill_k_int],\ mixed_use_other_data[mu_other_k_int], \ - accessibility_data, \ + accessibility_data,\ accessibility_data_wt @@ -636,46 +626,33 @@ def aggregate_stats(node_data: np.ndarray, checks.check_network_maps(node_data, edge_data, node_edge_map) checks.check_data_map(data_map, check_assigned=True) # raises ValueError data points are not assigned to a network checks.check_distances_and_betas(distances, betas) - # when passing an empty 2d array to numba, use: np.array(np.full((0, 0), np.nan)) if numerical_arrays.shape[1] != len(data_map): raise ValueError('The length of the numerical data arrays do not match the length of the data map.') checks.check_numerical_data(numerical_arrays) - # establish variables netw_n = len(node_data) d_n = len(distances) n_n = len(numerical_arrays) global_max_dist = float(np.nanmax(distances)) netw_nodes_live = node_data[:, 2] - # setup data structures - stats_sum = np.full((n_n, d_n, netw_n), np.nan) - stats_sum_wt = np.full((n_n, d_n, netw_n), np.nan) + stats_sum = np.full((n_n, d_n, netw_n), 0.0) + stats_sum_wt = np.full((n_n, d_n, netw_n), 0.0) stats_mean = np.full((n_n, d_n, netw_n), np.nan) stats_mean_wt = np.full((n_n, d_n, netw_n), np.nan) - stats_count = np.full((n_n, d_n, netw_n), np.nan) # use np.nan instead of 0 to avoid division by zero issues - stats_count_wt = np.full((n_n, d_n, netw_n), np.nan) + stats_count = np.full((n_n, d_n, netw_n), 0.0) + stats_count_wt = np.full((n_n, d_n, netw_n), 0.0) stats_variance = np.full((n_n, d_n, netw_n), np.nan) stats_variance_wt = np.full((n_n, d_n, netw_n), np.nan) stats_max = np.full((n_n, d_n, netw_n), np.nan) stats_min = np.full((n_n, d_n, netw_n), np.nan) - # iterate through each vert and aggregate steps = int(netw_n / 10000) + # parallelise over n nodes: + # each distance or stat array index is therefore only touched by one thread at a time + # i.e. no need to use inner array deductions as with centralities for netw_src_idx in prange(netw_n): - # setup shadow arrays for reductions - _stats_sum = np.full((n_n, d_n, netw_n), np.nan) - _stats_sum_wt = np.full((n_n, d_n, netw_n), np.nan) - _stats_mean = np.full((n_n, d_n, netw_n), np.nan) - _stats_mean_wt = np.full((n_n, d_n, netw_n), np.nan) - _stats_count = np.full((n_n, d_n, netw_n), np.nan) # use np.nan instead of 0 to avoid division by zero issues - _stats_count_wt = np.full((n_n, d_n, netw_n), np.nan) - _stats_variance = np.full((n_n, d_n, netw_n), np.nan) - _stats_variance_wt = np.full((n_n, d_n, netw_n), np.nan) - _stats_max = np.full((n_n, d_n, netw_n), np.nan) - _stats_min = np.full((n_n, d_n, netw_n), np.nan) - if not suppress_progress: checks.progress_bar(netw_src_idx, netw_n, steps) # only compute for live nodes @@ -711,33 +688,28 @@ def aggregate_stats(node_data: np.ndarray, # increment mean aggregations at respective distances if the distance is less than current d if data_dist <= d: # aggregate - if np.isnan(_stats_sum[num_idx, d_idx, netw_src_idx]): - _stats_sum[num_idx, d_idx, netw_src_idx] = num - _stats_count[num_idx, d_idx, netw_src_idx] = 1 - _stats_sum_wt[num_idx, d_idx, netw_src_idx] = num * np.exp(-b * data_dist) - _stats_count_wt[num_idx, d_idx, netw_src_idx] = np.exp(-b * data_dist) - else: - _stats_sum[num_idx, d_idx, netw_src_idx] += num - _stats_count[num_idx, d_idx, netw_src_idx] += 1 - _stats_sum_wt[num_idx, d_idx, netw_src_idx] += num * np.exp(-b * data_dist) - _stats_count_wt[num_idx, d_idx, netw_src_idx] += np.exp(-b * data_dist) - - if np.isnan(_stats_max[num_idx, d_idx, netw_src_idx]): - _stats_max[num_idx, d_idx, netw_src_idx] = num - elif num > _stats_max[num_idx, d_idx, netw_src_idx]: - _stats_max[num_idx, d_idx, netw_src_idx] = num - - if np.isnan(_stats_min[num_idx, d_idx, netw_src_idx]): - _stats_min[num_idx, d_idx, netw_src_idx] = num - elif num < _stats_min[num_idx, d_idx, netw_src_idx]: - _stats_min[num_idx, d_idx, netw_src_idx] = num + stats_sum[num_idx, d_idx, netw_src_idx] += num + stats_count[num_idx, d_idx, netw_src_idx] += 1 + stats_sum_wt[num_idx, d_idx, netw_src_idx] += num * np.exp(-b * data_dist) + stats_count_wt[num_idx, d_idx, netw_src_idx] += np.exp(-b * data_dist) + # max + if np.isnan(stats_max[num_idx, d_idx, netw_src_idx]): + stats_max[num_idx, d_idx, netw_src_idx] = num + elif num > stats_max[num_idx, d_idx, netw_src_idx]: + stats_max[num_idx, d_idx, netw_src_idx] = num + # min + if np.isnan(stats_min[num_idx, d_idx, netw_src_idx]): + stats_min[num_idx, d_idx, netw_src_idx] = num + elif num < stats_min[num_idx, d_idx, netw_src_idx]: + stats_min[num_idx, d_idx, netw_src_idx] = num # finalise mean calculations - this is happening for a single netw_src_idx, so fairly fast for num_idx in range(n_n): for d_idx in range(d_n): - _stats_mean[num_idx, d_idx, netw_src_idx] = \ - _stats_sum[num_idx, d_idx, netw_src_idx] / _stats_count[num_idx, d_idx, netw_src_idx] - _stats_mean_wt[num_idx, d_idx, netw_src_idx] = \ - _stats_sum_wt[num_idx, d_idx, netw_src_idx] / _stats_count_wt[num_idx, d_idx, netw_src_idx] + # use divide so that division through zero doesn't trigger + stats_mean[num_idx, d_idx, netw_src_idx] = np.divide(stats_sum[num_idx, d_idx, netw_src_idx], + stats_count[num_idx, d_idx, netw_src_idx]) + stats_mean_wt[num_idx, d_idx, netw_src_idx] = np.divide(stats_sum_wt[num_idx, d_idx, netw_src_idx], + stats_count_wt[num_idx, d_idx, netw_src_idx]) # calculate variances - counts are already computed per above # weighted version is IDW by division through equivalently weighted counts above # iterate the reachable indices and related distances @@ -757,41 +729,31 @@ def aggregate_stats(node_data: np.ndarray, # increment variance aggregations at respective distances if the distance is less than current d if data_dist <= d: # aggregate - if np.isnan(_stats_variance[num_idx, d_idx, netw_src_idx]): - _stats_variance[num_idx, d_idx, netw_src_idx] = \ - np.square(num - _stats_mean[num_idx, d_idx, netw_src_idx]) - _stats_variance_wt[num_idx, d_idx, netw_src_idx] = \ - np.square(num - _stats_mean_wt[num_idx, d_idx, netw_src_idx]) * np.exp( - -b * data_dist) + if np.isnan(stats_variance[num_idx, d_idx, netw_src_idx]): + stats_variance[num_idx, d_idx, netw_src_idx] = \ + np.square(num - stats_mean[num_idx, d_idx, netw_src_idx]) + stats_variance_wt[num_idx, d_idx, netw_src_idx] = \ + np.square(num - stats_mean_wt[num_idx, d_idx, netw_src_idx]) * np.exp(-b * data_dist) else: - _stats_variance[num_idx, d_idx, netw_src_idx] += \ - np.square(num - _stats_mean[num_idx, d_idx, netw_src_idx]) - _stats_variance_wt[num_idx, d_idx, netw_src_idx] += \ - np.square(num - _stats_mean_wt[num_idx, d_idx, netw_src_idx]) * np.exp( - -b * data_dist) + stats_variance[num_idx, d_idx, netw_src_idx] += \ + np.square(num - stats_mean[num_idx, d_idx, netw_src_idx]) + stats_variance_wt[num_idx, d_idx, netw_src_idx] += \ + np.square(num - stats_mean_wt[num_idx, d_idx, netw_src_idx]) * np.exp(-b * data_dist) # finalise variance calculations for num_idx in range(n_n): for d_idx in range(d_n): - _stats_variance[num_idx, d_idx, netw_src_idx] = \ - _stats_variance[num_idx, d_idx, netw_src_idx] / _stats_count[num_idx, d_idx, netw_src_idx] - _stats_variance_wt[num_idx, d_idx, netw_src_idx] = \ - _stats_variance_wt[num_idx, d_idx, netw_src_idx] / _stats_count_wt[num_idx, d_idx, netw_src_idx] - # reductions - stats_sum += _stats_sum - stats_sum_wt += _stats_sum_wt - stats_mean += _stats_mean - stats_mean_wt += _stats_mean_wt - stats_count += _stats_count - stats_count_wt += _stats_count_wt - stats_variance += _stats_variance - stats_variance_wt += _stats_variance_wt - stats_max += _stats_max - stats_min += _stats_min + # use divide so that division through zero doesn't trigger + stats_variance[num_idx, d_idx, netw_src_idx] = np.divide( + stats_variance[num_idx, d_idx, netw_src_idx], + stats_count[num_idx, d_idx, netw_src_idx]) + stats_variance_wt[num_idx, d_idx, netw_src_idx] = np.divide( + stats_variance_wt[num_idx, d_idx, netw_src_idx], + stats_count_wt[num_idx, d_idx, netw_src_idx]) return stats_sum, stats_sum_wt, stats_mean, stats_mean_wt, stats_variance, stats_variance_wt, stats_max, stats_min -@njit(cache=True, nogil=True) +@njit(cache=True, nogil=True, parallel=True) def singly_constrained(node_data: np.ndarray, edge_data: np.ndarray, node_edge_map: Dict, @@ -835,28 +797,22 @@ def singly_constrained(node_data: np.ndarray, if len(i_weights) != len(i_data_map): raise ValueError('The i_weights array must be the same length as the i_data_map.') - if len(j_weights) != len(j_data_map): raise ValueError('The j_weights array must be the same length as the j_data_map.') - # establish variables netw_n = len(node_data) d_n = len(distances) global_max_dist = np.max(distances) - netw_flows = np.full((d_n, netw_n), 0.0) - i_n = len(i_data_map) k_agg = np.full((d_n, i_n), 0.0) - - j_n = len(j_data_map) - j_assigned = np.full((d_n, j_n), 0.0) - # iterate all i nodes # filter all reachable nodes k and aggregate k attractiveness * negative exponential of distance steps = int(i_n / 10000) - for i_idx in range(i_n): + for i_idx in prange(i_n): if not suppress_progress: checks.progress_bar(i_idx, i_n, steps) + # setup shadowed array for reductions + _k_agg = np.full((d_n, i_n), 0.0) # get the nearest node i_assigned_netw_idx = int(i_data_map[i_idx, 2]) # calculate the base distance from the data point to the nearest assigned node @@ -872,7 +828,6 @@ def singly_constrained(node_data: np.ndarray, j_data_map, global_max_dist, angular) - # aggregate the weighted j (all k) nodes # iterate the reachable indices and related distances for j_idx, (j_reachable, j_dist) in enumerate(zip(reachable_j, reachable_j_dist)): @@ -883,23 +838,28 @@ def singly_constrained(node_data: np.ndarray, total_dist = j_dist + i_door_dist # increment weighted k aggregations at respective distances if the distance is less than current d if total_dist <= d: - k_agg[d_idx, i_idx] += j_weights[j_idx] * np.exp(-b * total_dist) - + _k_agg[d_idx, i_idx] += j_weights[j_idx] * np.exp(-b * total_dist) + # array reductions (non-race) + k_agg += _k_agg # this is the second step # this time, filter all reachable j vertices and aggregate the proportion of flow from i to j # this is done by dividing i-j flow through i-k_agg flow from previous step + netw_flows = np.full((d_n, netw_n), 0.0) + j_n = len(j_data_map) + j_assigned = np.full((d_n, j_n), 0.0) steps = int(i_n / 10000) - for i_idx in range(i_n): + for i_idx in prange(i_n): if not suppress_progress: checks.progress_bar(i_idx, i_n, steps) - + # setup shadowed arrays for reductions + _netw_flows = np.full((d_n, netw_n), 0.0) + _j_assigned = np.full((d_n, j_n), 0.0) # get the nearest node i_assigned_netw_idx = int(i_data_map[i_idx, 2]) # calculate the base distance from the data point to the nearest assigned node i_x, i_y = i_data_map[i_idx, :2] n_x, n_y = node_data[i_assigned_netw_idx, :2] i_door_dist = np.hypot(i_x - n_x, i_y - n_y) - # find the reachable j data points and their respective points from the closest node reachable_j, reachable_j_dist, tree_preds = aggregate_to_src_idx(i_assigned_netw_idx, node_data, @@ -908,10 +868,10 @@ def singly_constrained(node_data: np.ndarray, j_data_map, global_max_dist, angular) - # aggregate j divided through all k nodes # iterate the reachable indices and related distances for j_idx, (j_reachable, j_dist) in enumerate(zip(reachable_j, reachable_j_dist)): + # if not j_reachable: continue # iterate the distance dimensions @@ -925,15 +885,15 @@ def singly_constrained(node_data: np.ndarray, if k_agg[d_idx, i_idx] == 0: assigned = 0 else: - assigned = i_weights[i_idx] * j_weights[j_idx] * np.exp(-b * total_dist) / k_agg[d_idx, - i_idx] - j_assigned[d_idx, j_idx] += assigned + assigned = np.divide(i_weights[i_idx] * j_weights[j_idx] * np.exp(-b * total_dist), + k_agg[d_idx, i_idx]) + _j_assigned[d_idx, j_idx] += assigned # assign trips to network if assigned != 0: # get the j assigned node j_assigned_netw_idx = int(j_data_map[j_idx, 2]) # in this case start and end nodes are counted...! - netw_flows[d_idx, j_assigned_netw_idx] += assigned + _netw_flows[d_idx, j_assigned_netw_idx] += assigned # skip if same start / end node if j_assigned_netw_idx == i_assigned_netw_idx: continue @@ -941,11 +901,14 @@ def singly_constrained(node_data: np.ndarray, inter_idx = np.int(tree_preds[j_assigned_netw_idx]) while True: # end nodes counted, so place above break - netw_flows[d_idx, inter_idx] += assigned + _netw_flows[d_idx, inter_idx] += assigned # break out of while loop if the intermediary has reached the source node if inter_idx == i_assigned_netw_idx: break # follow the chain inter_idx = np.int(tree_preds[inter_idx]) + # array reductions (non-race) + netw_flows += _netw_flows + j_assigned += _j_assigned return j_assigned, netw_flows diff --git a/cityseer/metrics/layers.py b/cityseer/metrics/layers.py index d9cc95dd..9a5eca6b 100644 --- a/cityseer/metrics/layers.py +++ b/cityseer/metrics/layers.py @@ -347,11 +347,6 @@ def compute_aggregated(self, **kwargs): """ This method is deprecated and, if invoked, will raise a DeprecationWarning. Please use [`compute_landuses`](#datalayercompute_landuses) or [`compute_stats`](#datalayercompute_stats) instead. - - Raises - ------ - DeprecationWarning - """ raise DeprecationWarning('The compute_aggregated method has been deprecated. ' 'It has been split into two: ' @@ -662,23 +657,95 @@ def compute_landuses(self, for d_idx, d_key in enumerate(self.Network.distances): self.Network.metrics['accessibility'][k][ac_label][d_key] = ac_data[ac_idx][d_idx] + def hill_diversity(self, + landuse_labels: Union[list, tuple, np.ndarray], + qs: Union[list, tuple, np.ndarray] = None): + """ + Compute hill diversity for the provided `landuse_labels` at the specified values of `q`. See + [`DataLayer.compute_landuses`](#datalayercompute_landuses) for additional information. + + Parameters + ---------- + landuse_labels + A set of land-use labels corresponding to the length and order of the data points. The labels should + correspond to descriptors from the land-use schema, such as "retail" or "commercial". + qs + The values of `q` for which to compute Hill diversity, by default None + + Notes + ----- + The data key is `hill`, e.g.: + + `NetworkLayer.metrics['mixed_uses']['hill'][<>][<>][<>]` + """ + return self.compute_landuses(landuse_labels, mixed_use_keys=['hill'], qs=qs) + + def hill_branch_wt_diversity(self, + landuse_labels: Union[list, tuple, np.ndarray], + qs: Union[list, tuple, np.ndarray] = None): + """ + Compute distance-weighted hill diversity for the provided `landuse_labels` at the specified values of `q`. See + [`DataLayer.compute_landuses`](#datalayercompute_landuses) for additional information. + + Parameters + ---------- + landuse_labels + A set of land-use labels corresponding to the length and order of the data points. The labels should + correspond to descriptors from the land-use schema, such as "retail" or "commercial". + qs + The values of `q` for which to compute Hill diversity, by default None + + Notes + ----- + The data key is `hill_branch_wt`, e.g.: + + `NetworkLayer.metrics['mixed_uses']['hill_branch_wt'][<>][<>][<>]` + """ + return self.compute_landuses(landuse_labels, mixed_use_keys=['hill_branch_wt'], qs=qs) + + def compute_accessibilities(self, + landuse_labels: Union[list, tuple, np.ndarray], + accessibility_keys: Union[list, tuple]): + """ + Compute land-use accessibilities for the specified land-use classification keys. See + [`DataLayer.compute_landuses`](#datalayercompute_landuses) for additional information. + + Parameters + ---------- + landuse_labels + A set of land-use labels corresponding to the length and order of the data points. The labels should + correspond to descriptors from the land-use schema, such as "retail" or "commercial". + accessibility_keys + The land-use keys for which to compute accessibilies. The keys should be selected from the same land-use + schema used for the `landuse_labels` parameter, e.g. "retail". The calculations will be performed in both + `weighted` and `non_weighted` variants. + + Notes + ----- + The data keys will correspond to the `accessibility_keys` specified, e.g. where computing `retail` + accessibility: + + `NetworkLayer.metrics['accessibility']['weighted']['retail'][<>][<>]`
+ `NetworkLayer.metrics['accessibility']['non_weighted']['retail'][<>][<>]` + """ + return self.compute_landuses(landuse_labels, accessibility_keys=accessibility_keys) + def compute_stats(self, - stats_keys: Union[list, tuple], - stats_data_arrs: Union[List[Union[list, tuple, np.ndarray]], - Tuple[Union[list, tuple, np.ndarray]], - np.ndarray], + stats_keys: Union[str, list, tuple], + stats_data_arrs: Union[ + List[Union[list, tuple, np.ndarray]], + Tuple[Union[list, tuple, np.ndarray]], + list, + tuple, + np.ndarray], angular: bool = False): """ - This method wraps the underlying `numba` optimised functions for computing statistical measures. Situations - requiring only a single measure can instead make use of the simplified - [`DataLayer.compute_stats_single`](#datalayercompute_stats_single), and - [`DataLayer.compute_stats_multiple`](#datalayercompute_stats_multiple) methods. - - The data is aggregated and computed over the street network relative to the `Network Layer` nodes, with the - implication that statistical aggregations are generated from the same locations as for centrality computations, - which can therefore be correlated or otherwise compared. The outputs of the calculations are written to the - corresponding node indices in the same `NetworkLayer.metrics` dictionary used for centrality methods, and will - be categorised by the respective keys and parameters. + This method wraps the underlying `numba` optimised functions for computing statistical measures. The data is + aggregated and computed over the street network relative to the `Network Layer` nodes, with the implication + that statistical aggregations are generated from the same locations as for centrality computations, which can + therefore be correlated or otherwise compared. The outputs of the calculations are written to the corresponding + node indices in the same `NetworkLayer.metrics` dictionary used for centrality methods, and will be categorised + by the respective keys and parameters. For example, if a `valuations` stats key is computed on a `Network Layer` instantiated with 800m and 1600m distance thresholds, then the dictionary would assume the following structure: @@ -729,15 +796,17 @@ def compute_stats(self, Parameters ---------- stats_keys - A `list` or `tuple` of keys corresponding to the number of nested arrays passed to the `stats_data_arrs` - parameter. The computed stats will be saved to the `N.metrics` dictionary using these keys. This parameter - is only required if computing stats for a `stats_data_arrs` parameter. + If computing a single stat: a `str` key describing the stats computed for the `stats_data_arr` parameter. + If computing multiple stats: a `list` or `tuple` of keys. Computed stats will be saved under the supplied + key to the `N.metrics` dictionary. stats_data_arrs - A 2d `list`, `tuple` or `numpy` array of numerical data, where the first dimension corresponds to the number - of keys in the `stats_keys` parameter and the second dimension corresponds to number of data points in the - `DataLayer`. e.g: + If computing a single stat: a 1d `list`, `tuple` or `numpy` array of numerical data, where the length + corresponds to the number of data points in the `DataLayer`. + If computing multiple stats keys: a 2d `list`, `tuple`, or `numpy` array of numerical data, where the first + dimension corresponds to the number of keys in the `stats_keys` parameter and the second dimension + corresponds to number of data points in the `DataLayer`. e.g: ```python - # for a DataLayer containg 5 data points + # if computing three keys for a DataLayer containg 5 data points stats_keys = ['valuations', 'floors', 'occupants'] stats_data_arrs = [ [50000, 60000, 55000, 42000, 46000], # valuations @@ -752,6 +821,12 @@ def compute_stats(self, Notes ----- + The data keys will correspond to the `stats_keys` parameter, e.g.: + + `NetworkLayer.metrics['stats']['valuations'][<>][<>][<>]`
+ `NetworkLayer.metrics['stats']['floors'][<>][<>][<>]`
+ `NetworkLayer.metrics['stats']['occupants'][<>][<>][<>]` + A worked example: ```python from cityseer.metrics import networks, layers @@ -773,16 +848,9 @@ def compute_stats(self, L = layers.DataLayerFromDict(data_dict) # assign to the network L.assign_to_network(N, max_dist=500) - # compute some metrics - here we'll use the full interface, see below for simplified interfaces - # FULL INTERFACE - # ============== - L.compute_stats(stats_keys=['mock_stat'], + # compute some metrics + L.compute_stats(stats_keys='mock_stat', stats_data_arrs=stats_data) - # note that the above measures can optionally be run individually using simplified interfaces, e.g. - # SIMPLIFIED INTERFACES - # ===================== - # L.compute_stats_single('mock_stat', stats_data[0]) # this method requires a 1d array - # let's prepare some keys for accessing the computational outputs # distance idx: any of the distances with which the NetworkLayer was initialised distance_idx = 200 @@ -794,21 +862,39 @@ def compute_stats(self, # prints: 71297.82967202332 ``` - Note that the data can also be unpacked to a dictionary using [`NetworkLayer.metrics_to_dict`](/metrics/networks/#networklayermetrics_to_dict), or transposed to a `networkX` graph using [`NetworkLayer.to_networkX`](/metrics/networks/#networklayerto_networkx). + Note that the data can also be unpacked to a dictionary using + [`NetworkLayer.metrics_to_dict`](/metrics/networks/#networklayermetrics_to_dict), or transposed to a `networkX` + graph using [`NetworkLayer.to_networkX`](/metrics/networks/#networklayerto_networkx). + :::tip Comment + Per the above worked example, the following stat types will be available for each `stats_key` for each of the + computed distances: + - `max` and `min` + - `sum` and `sum_weighted` + - `mean` and `mean_weighted` + - `variance` and `variance_weighted` + ::: """ if self.Network is None: raise ValueError('Assign this data layer to a network prior to computing mixed-uses or accessibilities.') - elif len(stats_data_arrs) != len(stats_keys): + # check keys + if not isinstance(stats_keys, (str, list, tuple)): + raise TypeError('Stats keys should be a string else a list or tuple of strings.') + # wrap single keys + if isinstance(stats_keys, str): + stats_keys = [stats_keys] + # check data arrays + if not isinstance(stats_data_arrs, (list, tuple, np.ndarray)): + raise TypeError('Stats data must be in the form of a list, tuple, or numpy array.') + stats_data_arrs = np.array(stats_data_arrs) + # check for single dimensional arrays and change to 2d if necessary + if stats_data_arrs.ndim == 1: + stats_data_arrs = np.expand_dims(stats_data_arrs, axis=0) + # lengths of keys and array dims should match + if len(stats_data_arrs) != len(stats_keys): raise ValueError('An equal number of stats labels and stats data arrays is required.') - elif not isinstance(stats_data_arrs, (list, tuple, np.ndarray)): - raise ValueError('Stats data must be in the form of a list, tuple, or numpy array.') - else: - stats_data_arrs = np.array(stats_data_arrs) - if stats_data_arrs.ndim == 1: - stats_data_arrs = np.array([stats_data_arrs]) - if stats_data_arrs.shape[1] != len(self._data): - raise ValueError('The length of all data arrays must match the number of data points.') + if stats_data_arrs.shape[1] != len(self._data): + raise ValueError('The length of data arrays must match the number of data points.') if not checks.quiet_mode: logger.info(f'Computing stats for: {", ".join(stats_keys)}') # call the underlying method @@ -847,150 +933,23 @@ def compute_stats(self, for d_idx, d_key in enumerate(self.Network.distances): self.Network.metrics['stats'][stats_key][k][d_key] = stats_data[num_idx][d_idx] - def hill_diversity(self, - landuse_labels: Union[list, tuple, np.ndarray], - qs: Union[list, tuple, np.ndarray] = None): - """ - Compute hill diversity for the provided `landuse_labels` at the specified values of `q`. See - [`DataLayer.compute_aggregated`](#datalayercompute_aggregated) for additional information. - - Parameters - ---------- - landuse_labels - A set of land-use labels corresponding to the length and order of the data points. The labels should - correspond to descriptors from the land-use schema, such as "retail" or "commercial". - qs - The values of `q` for which to compute Hill diversity, by default None - - Notes - ----- - The data key is `hill`, e.g.: - - `NetworkLayer.metrics['mixed_uses']['hill'][<>][<>][<>]` - """ - return self.compute_landuses(landuse_labels, mixed_use_keys=['hill'], qs=qs) - - def hill_branch_wt_diversity(self, - landuse_labels: Union[list, tuple, np.ndarray], - qs: Union[list, tuple, np.ndarray] = None): - """ - Compute distance-weighted hill diversity for the provided `landuse_labels` at the specified values of `q`. See - [`DataLayer.compute_aggregated`](#datalayercompute_aggregated) for additional information. - - Parameters - ---------- - landuse_labels - A set of land-use labels corresponding to the length and order of the data points. The labels should - correspond to descriptors from the land-use schema, such as "retail" or "commercial". - qs - The values of `q` for which to compute Hill diversity, by default None - - Notes - ----- - The data key is `hill_branch_wt`, e.g.: - - `NetworkLayer.metrics['mixed_uses']['hill_branch_wt'][<>][<>][<>]` - """ - return self.compute_landuses(landuse_labels, mixed_use_keys=['hill_branch_wt'], qs=qs) - - def compute_accessibilities(self, - landuse_labels: Union[list, tuple, np.ndarray], - accessibility_keys: Union[list, tuple]): - """ - Compute land-use accessibilities for the specified land-use classification keys. See - [`DataLayer.compute_aggregated`](#datalayercompute_aggregated) for additional information. - - Parameters - ---------- - landuse_labels - A set of land-use labels corresponding to the length and order of the data points. The labels should - correspond to descriptors from the land-use schema, such as "retail" or "commercial". - accessibility_keys - The land-use keys for which to compute accessibilies. The keys should be selected from the same land-use - schema used for the `landuse_labels` parameter, e.g. "retail". The calculations will be performed in both - `weighted` and `non_weighted` variants. - - Notes - ----- - The data keys will correspond to the `accessibility_keys` specified, e.g. where computing `retail` - accessibility: - - `NetworkLayer.metrics['accessibility']['weighted']['retail'][<>][<>]`
- `NetworkLayer.metrics['accessibility']['non_weighted']['retail'][<>][<>]` + # deprecated method + def compute_stats_single(self, **kwargs): """ - return self.compute_landuses(landuse_labels, accessibility_keys=accessibility_keys) - - def compute_stats_single(self, - stats_key: str, - stats_data_arr: Union[list, tuple, np.ndarray]): + This method is deprecated and, if invoked, will raise a DeprecationWarning. Please use + [`compute_stats`](#datalayercompute_stats) instead. """ - Compute stats for a single `stats_key` parameter. As with the landuse and mixed-use measures: stats will be - computed for each distance that was specified when initialising the `NetworkLayer`. - - Parameters - ---------- - stats_key - A `str` key describing the stats computed for the `stats_data_arr` parameter. The computed stats will be - saved to the `N.metrics` dictionary under this key. - stats_data_arr - A 1d `list`, `tuple` or `numpy` array of numerical data, where the length corresponds to the number of data - points in the `DataLayer`. - - Notes - ----- - The data key will correspond to the `stats_key` parameter, e.g. where using `occupants` as the key: - - `NetworkLayer.metrics['stats']['occupants'][<>][<>][<>]` + raise DeprecationWarning('The compute_stats_single method has been deprecated. ' + 'Please use the compute_stats method instead.') - :::tip Comment - Per the above worked example, the following stat types will be available for each `stats_key` for each of the computed distances: - - `max` and `min` - - `sum` and `sum_weighted` - - `mean` and `mean_weighted` - - `variance` and `variance_weighted` - - ::: - """ - if stats_data_arr.ndim != 1: - raise ValueError( - 'The stats_data_arr must be a single dimensional array with a length corresponding to the number of data points in the DataLayer.') - return self.compute_stats(stats_keys=[stats_key], stats_data_arrs=[stats_data_arr]) - - def compute_stats_multiple(self, - stats_keys: Union[list, tuple], - stats_data_arrs: Union[list, tuple, np.ndarray]): + # deprecated method + def compute_stats_multiple(self, **kwargs): """ - - - Parameters - ---------- - stats_keys - A `list` or `tuple` of keys corresponding to the number of nested arrays passed to the `stats_data_arrs` - parameter. The computed stats will be saved to the `N.metrics` dictionary under these keys. - stats_data_arrs - A 2d `list`, `tuple` or `numpy` array of numerical data, where the first dimension corresponds to the number - of keys in the `stats_keys` parameter and the second dimension corresponds to number of data points in the - `DataLayer`. See the below example. - - Notes - ----- - ```python - # for a DataLayer containg 5 data points - stats_keys = ['valuations', 'floors', 'occupants'] - stats_data_arrs = [ - [50000, 60000, 55000, 42000, 46000], # valuations - [3, 3, 2, 3, 5], # floors - [420, 300, 220, 250, 600] # occupants - ] - ``` - The data keys will correspond to the `stats_keys` parameter: - - `NetworkLayer.metrics['stats']['valuations'][<>][<>][<>]`
- `NetworkLayer.metrics['stats']['floors'][<>][<>][<>]`
- `NetworkLayer.metrics['stats']['occupants'][<>][<>][<>]` + This method is deprecated and, if invoked, will raise a DeprecationWarning. Please use + [`compute_stats`](#datalayercompute_stats) instead. """ - - return self.compute_stats(stats_keys=stats_keys, stats_data_arrs=stats_data_arrs) + raise DeprecationWarning('The compute_stats_multiple method has been deprecated. ' + 'Please use the compute_stats method instead.') def model_singly_constrained(self, key: str, @@ -1002,7 +961,6 @@ def model_singly_constrained(self, """ Undocumented method for computing singly-constrained interactions. """ - j_assigned, netw_flows = data.singly_constrained(self.Network._node_data, self.Network._edge_data, self.Network._node_edge_map, @@ -1032,7 +990,7 @@ class DataLayerFromDict(DataLayer): [`DataLayer`](#class-datalayer) class, which can be referenced for more information. """ - def __init__(self, data_dict: dict) -> DataLayer: + def __init__(self, data_dict: dict): """ Parameters ---------- diff --git a/cityseer/metrics/networks.py b/cityseer/metrics/networks.py index e52e73fb..a0844743 100644 --- a/cityseer/metrics/networks.py +++ b/cityseer/metrics/networks.py @@ -655,11 +655,6 @@ def compute_centrality(self, **kwargs): This method is deprecated and, if invoked, will raise a DeprecationWarning. Please use [`node_centrality`](#networklayernode_centrality) or [`segment_centrality`](#networklayersegment_centrality) instead. - - Raises - ------ - DeprecationWarning - """ raise DeprecationWarning('The compute_centrality method has been deprecated. ' 'It has been split into two: ' diff --git a/docs/content/intro.md b/docs/content/intro.md index 75668eea..94ee86f1 100644 --- a/docs/content/intro.md +++ b/docs/content/intro.md @@ -93,7 +93,6 @@ The [`NetworkLayer.node_centrality`](/metrics/networks/#networklayernode_central ```python from cityseer.metrics import networks - # create a Network layer from the networkX graph N = networks.NetworkLayerFromNX(G_decomp, distances=[200, 400, 800, 1600]) # the underlying method allows the computation of various centralities simultaneously, e.g. @@ -106,7 +105,6 @@ A [`DataLayer`](/metrics/layers/#class-datalayer) represents data points. A `Dat ```python from cityseer.metrics import layers - # a mock data dictionary representing the 'x', 'y' attributes for data points data_dict = mock.mock_data_dict(G_decomp, random_seed=25) print(data_dict[0], data_dict[1], 'etc.') @@ -125,14 +123,13 @@ _Data points assigned to a Network Layer._ ![DataLayer assigned to a decomposed NetworkLayer](../src/assets/plots/images/assignment_decomposed.png) _Data assignment becomes more precise on a decomposed Network Layer._ -Once the data has been assigned, the [`DataLayer.compute_aggregated`](/metrics/layers/#datalayercompute_aggregated) method is used for the calculation of mixed-use, accessibility, and statistical measures. As with the centrality methods, the measures are all computed simultaneously (and for all distances); however, simpler stand-alone methods are also available, including: [`DataLayer.hill_diversity`](/metrics/layers/#datalayerhill_diversity) , [`DataLayer.hill_branch_wt_diversity`](/metrics/layers/#datalayerhill_branch_wt_diversity) , [`DataLayer.compute_accessibilities`](/metrics/layers/#datalayercompute_accessibilities) , [`DataLayer.compute_stats_single`](/metrics/layers/#datalayercompute_stats_single), and [`DataLayer.compute_stats_multiple`](/metrics/layers/#datalayercompute_stats_multiple). +Once the data has been assigned, the [`DataLayer.compute_landuses`](/metrics/layers/#datalayercompute_landuses) method is used for the calculation of mixed-use and land-use accessibility measures whereas [`DataLayer.compute_stats`](/metrics/layers/#datalayercompute_stats) can likewise be used for statistical measures. As with the centrality methods, the measures are all computed simultaneously (and for all distances); however, simpler stand-alone methods are also available, including [`DataLayer.hill_diversity`](/metrics/layers/#datalayerhill_diversity), [`DataLayer.hill_branch_wt_diversity`](/metrics/layers/#datalayerhill_branch_wt_diversity), and [`DataLayer.compute_accessibilities`](/metrics/layers/#datalayercompute_accessibilities). Landuse labels can be used to generate mixed-use and land-use accessibility measures. Let's create mock landuse labels for the points in our data dictionary and compute mixed-uses and land-use accessibilities: ```python landuse_labels = mock.mock_categorical_data(len(data_dict), random_seed=25) print(landuse_labels) -# prints: ['e' 'g' 'h' 'c' 'i' 'e' 'j' 'e' 'f' 'b' etc. # example easy-wrapper method for computing mixed-uses D.hill_branch_wt_diversity(landuse_labels, qs=[0, 1, 2]) # example easy-wrapper method for computing accessibilities @@ -140,7 +137,7 @@ D.hill_branch_wt_diversity(landuse_labels, qs=[0, 1, 2]) # for which accessibilities will be computed D.compute_accessibilities(landuse_labels, accessibility_keys=['a', 'c']) # or compute multiple measures at once, e.g.: -D.compute_aggregated(landuse_labels, +D.compute_landuses(landuse_labels, mixed_use_keys=['hill', 'hill_branch_wt', 'shannon'], accessibility_keys=['a', 'c'], qs=[0, 1, 2]) @@ -152,7 +149,7 @@ We can do the same thing with numerical data. Let's generate some mock numerical mock_valuations_data = mock.mock_numerical_data(len(data_dict), random_seed=25) print(mock_valuations_data) # compute max, min, mean, mean-weighted, variance, and variance-weighted -D.compute_stats_single(stats_key='valuations', stats_data_arr=mock_valuations_data[0]) +D.compute_stats('valuations', mock_valuations_data) ``` The data is aggregated and computed over the street network relative to the `NetworkLayer` (i.e. street) nodes. The mixed-use, accessibility, and statistical aggregations can therefore be compared directly to centrality computations from the same locations, and can be correlated or otherwise compared. The outputs of the calculations are written to the corresponding node indices in the same `NetworkLayer.metrics` dictionary used for centrality methods, and will be categorised by the respective keys and parameters. @@ -188,7 +185,6 @@ The data can then be passed to data analysis or plotting methods. For example, t ```python # plot centrality from matplotlib import colors - segment_harmonic_vals = [] mixed_uses_vals = [] for node, data in G_metrics.nodes(data=True): diff --git a/docs/content/metrics/layers.md b/docs/content/metrics/layers.md index a71d3408..91d03a2f 100644 --- a/docs/content/metrics/layers.md +++ b/docs/content/metrics/layers.md @@ -290,22 +290,30 @@ _Assignment of data to network nodes becomes more contextually precise on decomp
-DataLayer.compute_aggregated(landuse_labels=None,
-                             mixed_use_keys=None,
-                             accessibility_keys=None,
-                             cl_disparity_wt_matrix=None,
-                             qs=None,
-                             stats_keys=None,
-                             stats_data_arrs=None,
-                             angular=False)
+DataLayer.compute_aggregated(**kwargs)
 
-This method wraps the underlying `numba` optimised functions for aggregating and computing various mixed-use, land-use accessibility, and statistical measures. These are computed simultaneously for any required combinations of measures (and distances), which can have significant speed implications. Situations requiring only a single measure can instead make use of the simplified [`DataLayer.hill_diversity`](#datalayerhill_diversity), [`DataLayer.hill_branch_wt_diversity`](#datalayerhill_branch_wt_diversity), [`DataLayer.compute_accessibilities`](#datalayercompute_accessibilities), [`DataLayer.compute_stats_single`](#datalayercompute_stats_single), and [`DataLayer.compute_stats_multiple`](#datalayercompute_stats_multiple) methods. +This method is deprecated and, if invoked, will raise a DeprecationWarning. Please use [`compute_landuses`](#datalayercompute_landuses) or [`compute_stats`](#datalayercompute_stats) instead. -The data is aggregated and computed over the street network relative to the `Network Layer` nodes, with the implication that mixed-use, accessibility, and statistical aggregations are generated from the same locations as for centrality computations, which can therefore be correlated or otherwise compared. The outputs of the calculations are written to the corresponding node indices in the same `NetworkLayer.metrics` dictionary used for centrality methods, and will be categorised by the respective keys and parameters. +## DataLayer.compute\_landuses -For example, if `hill` and `shannon` mixed-use keys; `shops` and `factories` accessibility keys; and a `valuations` stats keys are computed on a `Network Layer` instantiated with 800m and 1600m distance thresholds, then the dictionary would assume the following structure: + +
+DataLayer.compute_landuses(landuse_labels,
+                           mixed_use_keys=None,
+                           accessibility_keys=None,
+                           cl_disparity_wt_matrix=None,
+                           qs=None,
+                           angular=False)
+
+
+ +This method wraps the underlying `numba` optimised functions for aggregating and computing various mixed-use and land-use accessibility measures. These are computed simultaneously for any required combinations of measures (and distances), which can have significant speed implications. Situations requiring only a single measure can instead make use of the simplified [`DataLayer.hill_diversity`](#datalayerhill_diversity), [`DataLayer.hill_branch_wt_diversity`](#datalayerhill_branch_wt_diversity), and [`DataLayer.compute_accessibilities`](#datalayercompute_accessibilities) methods. + +The data is aggregated and computed over the street network relative to the `Network Layer` nodes, with the implication that mixed-use and land-use accessibility aggregations are generated from the same locations as for centrality computations, which can therefore be correlated or otherwise compared. The outputs of the calculations are written to the corresponding node indices in the same `NetworkLayer.metrics` dictionary used for centrality methods, and will be categorised by the respective keys and parameters. + +For example, if `hill` and `shannon` mixed-use keys; `shops` and `factories` accessibility keys are computed on a `Network Layer` instantiated with 800m and 1600m distance thresholds, then the dictionary would assume the following structure: ```python NetworkLayer.metrics = { @@ -351,44 +359,6 @@ NetworkLayer.metrics = { 1600: [...] } } - }, - 'stats': { - # stats grouped by each stats key - 'valuations': { - # each stat will have the following key-value pairs - 'max': { - 800: [...], - 1600: [...] - }, - 'min': { - 800: [...], - 1600: [...] - }, - 'sum': { - 800: [...], - 1600: [...] - }, - 'sum_weighted': { - 800: [...], - 1600: [...] - }, - 'mean': { - 800: [...], - 1600: [...] - }, - 'mean_weighted': { - 800: [...], - 1600: [...] - }, - 'variance': { - 800: [...], - 1600: [...] - }, - 'variance_weighted': { - 800: [...], - 1600: [...] - } - } } } ``` @@ -397,7 +367,7 @@ NetworkLayer.metrics = { -A set of land-use labels corresponding to the length and order of the data points. The labels should correspond to descriptors from the land-use schema, such as "retail" or "commercial". This parameter is only required if computing mixed-uses or land-use accessibilities, by default None. +A set of land-use labels corresponding to the length and order of the data points. The labels should correspond to descriptors from the land-use schema, such as "retail" or "commercial". This parameter is only required if computing mixed-uses or land-use accessibilities. @@ -425,30 +395,6 @@ The values of `q` for which to compute Hill diversity. This parameter is only re - - -A `list` or `tuple` of keys corresponding to the number of nested arrays passed to the `stats_data_arrs` parameter. The computed stats will be saved to the `N.metrics` dictionary using these keys. This parameter is only required if computing stats for a `stats_data_arrs` parameter, by default None - - - - - -A 2d `list`, `tuple` or `numpy` array of numerical data, where the first dimension corresponds to the number of keys in the `stats_keys` parameter and the second dimension corresponds to number of data points in the `DataLayer`, by default None. e.g: - -```python -# for a DataLayer containg 5 data points -stats_keys = ['valuations', 'floors', 'occupants'] -stats_data_arrs = [ - [50000, 60000, 55000, 42000, 46000], # valuations - [3, 3, 2, 3, 5], # floors - [420, 300, 220, 250, 600] # occupants -] -``` - - - Whether to use a simplest-path heuristic in-lieu of a shortest-path heuristic when calculating aggregations and distances, by default False @@ -489,8 +435,6 @@ N = networks.NetworkLayerFromNX(G, distances=[200, 400, 800, 1600]) data_dict = mock.mock_data_dict(G, random_seed=25) # prepare some mock land-use classifications landuses = mock.mock_categorical_data(len(data_dict), random_seed=25) -# let's also prepare some numerical data -stats_data = mock.mock_numerical_data(len(data_dict), num_arrs=1, random_seed=25) # generate a data layer L = layers.DataLayerFromDict(data_dict) @@ -499,18 +443,15 @@ L.assign_to_network(N, max_dist=500) # compute some metrics - here we'll use the full interface, see below for simplified interfaces # FULL INTERFACE # ============== -L.compute_aggregated(landuse_labels=landuses, - mixed_use_keys=['hill'], - qs=[0, 1], - accessibility_keys=['c', 'd', 'e'], - stats_keys=['mock_stat'], - stats_data_arrs=stats_data) +L.compute_landuses(landuse_labels=landuses, + mixed_use_keys=['hill'], + qs=[0, 1], + accessibility_keys=['c', 'd', 'e']) # note that the above measures can optionally be run individually using simplified interfaces, e.g. # SIMPLIFIED INTERFACES # ===================== # L.hill_diversity(landuses, qs=[0]) # L.compute_accessibilities(landuses, ['a', 'b']) -# L.compute_stats_single('mock_stat', stats_data[0]) # this method requires a 1d array # let's prepare some keys for accessing the computational outputs # distance idx: any of the distances with which the NetworkLayer was initialised @@ -527,8 +468,6 @@ print(N.metrics['accessibility']['weighted']['d'][distance_idx][node_idx]) # prints: 0.019168843947614676 print(N.metrics['accessibility']['non_weighted']['d'][distance_idx][node_idx]) # prints: 1.0 -print(N.metrics['stats']['mock_stat']['mean_weighted'][distance_idx][node_idx]) -# prints: 71297.82967202332 ``` Note that the data can also be unpacked to a dictionary using [`NetworkLayer.metrics_to_dict`](/metrics/networks/#networklayermetrics_to_dict), or transposed to a `networkX` graph using [`NetworkLayer.to_networkX`](/metrics/networks/#networklayerto_networkx). @@ -547,7 +486,7 @@ DataLayer.hill_diversity(landuse_labels, -Compute hill diversity for the provided `landuse_labels` at the specified values of `q`. See [`DataLayer.compute_aggregated`](#datalayercompute_aggregated) for additional information. +Compute hill diversity for the provided `landuse_labels` at the specified values of `q`. See [`DataLayer.compute_landuses`](#datalayercompute_landuses) for additional information. Parameters @@ -578,7 +517,7 @@ DataLayer.hill_branch_wt_diversity(landuse_labels, -Compute distance-weighted hill diversity for the provided `landuse_labels` at the specified values of `q`. See [`DataLayer.compute_aggregated`](#datalayercompute_aggregated) for additional information. +Compute distance-weighted hill diversity for the provided `landuse_labels` at the specified values of `q`. See [`DataLayer.compute_landuses`](#datalayercompute_landuses) for additional information. Parameters @@ -609,7 +548,7 @@ DataLayer.compute_accessibilities(landuse_labels, -Compute land-use accessibilities for the specified land-use classification keys. See [`DataLayer.compute_aggregated`](#datalayercompute_aggregated) for additional information. +Compute land-use accessibilities for the specified land-use classification keys. See [`DataLayer.compute_landuses`](#datalayercompute_landuses) for additional information. Parameters @@ -632,37 +571,144 @@ The data keys will correspond to the `accessibility_keys` specified, e.g. where `NetworkLayer.metrics['accessibility']['weighted']['retail'][<>][<>]`
`NetworkLayer.metrics['accessibility']['non_weighted']['retail'][<>][<>]` -## DataLayer.compute\_stats\_single +## DataLayer.compute\_stats
-DataLayer.compute_stats_single(stats_key,
-                               stats_data_arr)
+DataLayer.compute_stats(stats_keys,
+                        stats_data_arrs,
+                        angular=False)
 
-Compute stats for a single `stats_key` parameter. As with the landuse and mixed-use measures: stats will be computed for each distance that was specified when initialising the `NetworkLayer`. +This method wraps the underlying `numba` optimised functions for computing statistical measures. The data is aggregated and computed over the street network relative to the `Network Layer` nodes, with the implication that statistical aggregations are generated from the same locations as for centrality computations, which can therefore be correlated or otherwise compared. The outputs of the calculations are written to the corresponding node indices in the same `NetworkLayer.metrics` dictionary used for centrality methods, and will be categorised by the respective keys and parameters. + +For example, if a `valuations` stats key is computed on a `Network Layer` instantiated with 800m and 1600m distance thresholds, then the dictionary would assume the following structure: + +```python +NetworkLayer.metrics = { + 'stats': { + # stats grouped by each stats key + 'valuations': { + # each stat will have the following key-value pairs + 'max': { + 800: [...], + 1600: [...] + }, + 'min': { + 800: [...], + 1600: [...] + }, + 'sum': { + 800: [...], + 1600: [...] + }, + 'sum_weighted': { + 800: [...], + 1600: [...] + }, + 'mean': { + 800: [...], + 1600: [...] + }, + 'mean_weighted': { + 800: [...], + 1600: [...] + }, + 'variance': { + 800: [...], + 1600: [...] + }, + 'variance_weighted': { + 800: [...], + 1600: [...] + } + } + } +} +``` Parameters - + + +If computing a single stat: a `str` key describing the stats computed for the `stats_data_arr` parameter. If computing multiple stats: a `list` or `tuple` of keys. Computed stats will be saved under the supplied key to the `N.metrics` dictionary. + + + + -A `str` key describing the stats computed for the `stats_data_arr` parameter. The computed stats will be saved to the `N.metrics` dictionary under this key. +If computing a single stat: a 1d `list`, `tuple` or `numpy` array of numerical data, where the length corresponds to the number of data points in the `DataLayer`. If computing multiple stats keys: a 2d `list`, `tuple`, or `numpy` array of numerical data, where the first dimension corresponds to the number of keys in the `stats_keys` parameter and the second dimension corresponds to number of data points in the `DataLayer`. e.g: + +```python +# if computing three keys for a DataLayer containg 5 data points +stats_keys = ['valuations', 'floors', 'occupants'] +stats_data_arrs = [ + [50000, 60000, 55000, 42000, 46000], # valuations + [3, 3, 2, 3, 5], # floors + [420, 300, 220, 250, 600] # occupants +] +``` - + -A 1d `list`, `tuple` or `numpy` array of numerical data, where the length corresponds to the number of data points in the `DataLayer`. +Whether to use a simplest-path heuristic in-lieu of a shortest-path heuristic when calculating aggregations and distances, by default False Notes -The data key will correspond to the `stats_key` parameter, e.g. where using `occupants` as the key: +The data keys will correspond to the `stats_keys` parameter, e.g.: +`NetworkLayer.metrics['stats']['valuations'][<>][<>][<>]`
+`NetworkLayer.metrics['stats']['floors'][<>][<>][<>]`
`NetworkLayer.metrics['stats']['occupants'][<>][<>][<>]` +A worked example: + +```python +from cityseer.metrics import networks, layers +from cityseer.tools import mock, graphs + +# prepare a mock graph +G = mock.mock_graph() +G = graphs.nX_simple_geoms(G) + +# generate the network layer +N = networks.NetworkLayerFromNX(G, distances=[200, 400, 800, 1600]) + +# prepare a mock data dictionary +data_dict = mock.mock_data_dict(G, random_seed=25) +# let's prepare some numerical data +stats_data = mock.mock_numerical_data(len(data_dict), num_arrs=1, random_seed=25) + +# generate a data layer +L = layers.DataLayerFromDict(data_dict) +# assign to the network +L.assign_to_network(N, max_dist=500) +# compute some metrics +L.compute_stats(stats_keys='mock_stat', + stats_data_arrs=stats_data) +# let's prepare some keys for accessing the computational outputs +# distance idx: any of the distances with which the NetworkLayer was initialised +distance_idx = 200 +# a node idx +node_idx = 0 + +# the data is available at N.metrics +print(N.metrics['stats']['mock_stat']['mean_weighted'][distance_idx][node_idx]) +# prints: 71297.82967202332 +``` + +Note that the data can also be unpacked to a dictionary using [`NetworkLayer.metrics_to_dict`](/metrics/networks/#networklayermetrics_to_dict), or transposed to a `networkX` graph using [`NetworkLayer.to_networkX`](/metrics/networks/#networklayerto_networkx). + :::tip Comment Per the above worked example, the following stat types will be available for each `stats_key` for each of the computed distances: @@ -671,49 +717,27 @@ Per the above worked example, the following stat types will be available for eac - `sum` and `sum_weighted` - `mean` and `mean_weighted` - `variance` and `variance_weighted` - ::: -## DataLayer.compute\_stats\_multiple +## DataLayer.compute\_stats\_single
-DataLayer.compute_stats_multiple(stats_keys,
-                                 stats_data_arrs)
+DataLayer.compute_stats_single(**kwargs)
 
-Parameters - - - -A `list` or `tuple` of keys corresponding to the number of nested arrays passed to the `stats_data_arrs` parameter. The computed stats will be saved to the `N.metrics` dictionary under these keys. +This method is deprecated and, if invoked, will raise a DeprecationWarning. Please use [`compute_stats`](#datalayercompute_stats) instead. - - - - -A 2d `list`, `tuple` or `numpy` array of numerical data, where the first dimension corresponds to the number of keys in the `stats_keys` parameter and the second dimension corresponds to number of data points in the `DataLayer`. See the below example. - - - -Notes - -```python -# for a DataLayer containg 5 data points -stats_keys = ['valuations', 'floors', 'occupants'] -stats_data_arrs = [ - [50000, 60000, 55000, 42000, 46000], # valuations - [3, 3, 2, 3, 5], # floors - [420, 300, 220, 250, 600] # occupants -] -``` +## DataLayer.compute\_stats\_multiple - The data keys will correspond to the `stats_keys` parameter: + +
+DataLayer.compute_stats_multiple(**kwargs)
+
+
-`NetworkLayer.metrics['stats']['valuations'][<>][<>][<>]`
-`NetworkLayer.metrics['stats']['floors'][<>][<>][<>]`
-`NetworkLayer.metrics['stats']['occupants'][<>][<>][<>]` +This method is deprecated and, if invoked, will raise a DeprecationWarning. Please use [`compute_stats`](#datalayercompute_stats) instead. ## DataLayer.model\_singly\_constrained @@ -739,7 +763,6 @@ Directly transposes an appropriately prepared data dictionary into a `DataLayer`
 DataLayerFromDict(data_dict)
-                  -> DataLayer
 
diff --git a/docs/content/metrics/networks.md b/docs/content/metrics/networks.md index 9fd8c64d..10ee86f4 100644 --- a/docs/content/metrics/networks.md +++ b/docs/content/metrics/networks.md @@ -485,8 +485,6 @@ NetworkLayer.compute_centrality(**kwargs) This method is deprecated and, if invoked, will raise a DeprecationWarning. Please use [`node_centrality`](#networklayernode_centrality) or [`segment_centrality`](#networklayersegment_centrality) instead. -Raises - ## NetworkLayer.node\_centrality diff --git a/docs/generateDocs.js b/docs/generateDocs.js index 8eedafe4..e02d4e19 100644 --- a/docs/generateDocs.js +++ b/docs/generateDocs.js @@ -1,3 +1,4 @@ +// this script stopped working and no idea why or how to debug. const path = require('path') const { PythonShell } = require('python-shell') @@ -9,6 +10,7 @@ const options = { args: ['--config', '../.yap_config.yaml'], } -PythonShell.run('__init__.py', options, function (err) { +PythonShell.run('__init__.py', options, function (err, results) { if (err) throw err + console.log(results) }) diff --git a/docs/package.json b/docs/package.json index c2bb1de3..4bc5ef70 100644 --- a/docs/package.json +++ b/docs/package.json @@ -1,10 +1,10 @@ { "scripts": { - "generate": "node generateDocs.js", + "NOTE": "echo NOTE: Run yapper from terminal in project root directory to build files.", "lint": "markdownlint 'content/**/*.md' --fix", "validateLinks": "remark -u validate-links .", - "dev": "yarn run generate && yarn run lint && gridsome develop", - "build": "yarn run generate && yarn run lint && gridsome build", + "dev": "yarn run NOTE && yarn run lint && gridsome develop", + "build": "yarn run NOTE && yarn run lint && gridsome build", "ci": "yarn run lint && gridsome build", "explore": "yarn run generate && yarn run lint && gridsome explore" }, @@ -28,7 +28,7 @@ "@silvenon/remark-smartypants": "^1.0.0", "@tailwindcss/postcss7-compat": "^2.1.2", "autoprefixer": "^9", - "eslint": "^7.26.0", + "eslint": "^7.27.0", "eslint-config-prettier": "^8.3.0", "eslint-plugin-gridsome": "^1.6.2", "eslint-plugin-prettier": "^3.4.0", diff --git a/docs/src/assets/plots/images/assignment.png b/docs/src/assets/plots/images/assignment.png index 738f24a88c885d29d878faf9c3ef6c24a534f45f..361d587b1ce20d6d0f7a44ca590ee7829bbf2bf4 100644 GIT binary patch delta 51 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