0:\n wt_out_file = f'{save_file}-{epoch}.p'\n torch.save({'model_state_params': net.state_dict(),'loss_history':loss_history}, wt_out_file)\n\n loss_history.append(loss_contrastive.item())\n\n # Update training info and save JSON every display_steps\n if epoch % conf.display_step == 0 and epoch > 0 and json_file is not None:\n logging.info(f'Epoch {epoch}, Loss: {loss_contrastive.item()}')\n train_info['train_loss'].append(loss_contrastive.item())\n train_info['step'].append(epoch)\n\n # Save JSON file with same format as PoseCommon_pytorch.py\n json_data = {}\n for x in train_info.keys():\n json_data[x] = np.array(train_info[x]).astype(np.float64).tolist()\n\n with open(json_file, 'w') as f:\n json.dump(json_data, f)\n\n if epoch%conf.save_step == 0 and epoch > 0:\n torch.save({'model_state_params': net.state_dict(), 'loss_history': loss_history}, save_file+'.int')\n\n # dump the final loss history\n logging.info(f'Epoch {n_iters}, Loss: {loss_contrastive.item():.4f}')\n train_info['train_loss'].append(loss_contrastive.item())\n train_info['step'].append(n_iters)\n\n # Save JSON file with same format as PoseCommon_pytorch.py\n json_data = {}\n for x in train_info.keys():\n json_data[x] = np.array(train_info[x]).astype(np.float64).tolist()\n\n with open(json_file, 'w') as f:\n json.dump(json_data, f)\n\n wt_out_file = f'{save_file}'\n torch.save({'model_state_params': net.state_dict(), 'loss_history': loss_history}, wt_out_file)\n if os.path.exists(save_file+'.int'):\n os.remove(save_file+'.int')\n\n logging.info(f'Training complete, saved weights to {wt_out_file}')\n\n del train_iter, train_loader, train_dset\n\n load_task.cancel()\n try:\n await load_task\n except asyncio.CancelledError:\n pass\n\n return net, loss_history\n\nfrom tqdm import tqdm\ndef get_id_embeddings(linked_trks,net,mov_files,conf,all_trx,rescale,min_len_select,debug):\n\n\n net.eval()\n all_data = []\n preds = None\n pred_map = []\n # pred_map keeps track of which sample belongs to which trajectory\n\n\n # sample images for each tracklet and then find the embeddings for them\n for ndx in tqdm(range(len(linked_trks))):\n # Sample images from the tracklets\n trk = linked_trks[ndx]\n mov_file = mov_files[ndx]\n trx = all_trx[ndx]\n ss, ee = trk.get_startendframes()\n\n # For each tracklet chose n_per_trk random examples and the find their embedding. Ignore short tracklets\n sel_tgt = np.where((ee-ss+1)>=min_len_select)[0]\n sel_ss = ss[sel_tgt]; sel_ee = ee[sel_tgt]\n trk_info = list(zip(sel_tgt, sel_ss, sel_ee))\n logging.info(f'Sampling images from {len(sel_tgt)} tracklets to assign identity to the tracklets ...')\n start_t = time.time()\n\n cur_preds, tgt_id, debug_data = pred_ims_par(trx,trk_info,mov_file,conf,net, rescale,False)\n\n logging.info(f'Predicting on images took {round((time.time()-start_t)/60)} minutes')\n\n if preds is None:\n preds = cur_preds\n else:\n preds = np.concatenate([preds, cur_preds], axis=0)\n\n # pred_map keeps track of which sample belongs to which trajectory\n pred_map.extend([[ndx,tt] for tt in tgt_id])\n cur_d = [debug_data, sel_tgt, tgt_id, ss, ee, ss[tgt_id], ee[tgt_id]]\n all_data.append(cur_d)\n\n pred_map = np.array(pred_map)\n\n return preds, pred_map,all_data\n\ndef get_id_dist_xmat_old(linked_trks,net,mov_files,conf,all_trx,rescale,min_len_select,debug):\n\n\n net.eval()\n all_data = []\n preds = None\n pred_map = []\n # pred_map keeps track of which sample belongs to which trajectory\n\n\n # sample images for each tracklet and then find the embeddings for them\n for ndx in range(len(linked_trks)):\n # Sample images from the tracklets\n trk = linked_trks[ndx]\n mov_file = mov_files[ndx]\n trx = all_trx[ndx]\n ss, ee = trk.get_startendframes()\n\n # For each tracklet chose n_per_trk random examples and the find their embedding. Ignore short tracklets\n sel_tgt = np.where((ee-ss+1)>=min_len_select)[0]\n sel_ss = ss[sel_tgt]; sel_ee = ee[sel_tgt]\n trk_info = list(zip(sel_tgt, sel_ss, sel_ee))\n logging.info(f'Sampling images from {len(sel_ss)} tracklets to assign identity to the tracklets ...')\n start_t = time.time()\n cur_data = asyncio.run(read_ims_par(trx, trk_info, mov_file, conf))\n end_t = time.time()\n logging.info(f'Sampling images took {round((end_t-start_t)/60)} minutes')\n\n merge_data = []\n merge_tgt_id = []\n s_sz = 200\n # find ceil\n n_split = int(np.ceil(len(cur_data)/s_sz))\n # split data in batches to do predictions\n for idx in tqdm(range(n_split)):\n\n # data = read_data_files([curf])\n ids1 = s_sz*idx\n ids2 = min(s_sz*(idx+1), len(cur_data))\n data = cur_data[ids1:ids2]\n tgt_id = np.array([r[1] for r in data])\n merge_tgt_id.extend(tgt_id.tolist())\n\n if debug:\n merge_data.extend(data)\n\n # pred_map keeps track of which sample belongs to which trajectory\n ims = []\n for curndx in range(len(data)):\n curims = data[curndx][0]\n ims.append(curims)\n pred_map.append([ndx, tgt_id[curndx]])\n\n # Find the embeddings for the images\n cur_preds = tracklet_pred(ims, net, conf, rescale)\n\n if cur_preds.size>0:\n if preds is None:\n preds = cur_preds\n else:\n preds = np.concatenate([preds, cur_preds],axis=0)\n\n merge_tgt_id = np.array(merge_tgt_id)\n cur_d = [merge_data, sel_tgt, merge_tgt_id, ss, ee, sel_ss, sel_ee]\n all_data.append(cur_d)\n\n pred_map = np.array(pred_map)\n\n dist_mat = get_id_dist_mat(preds)\n return dist_mat, pred_map,all_data, preds\n\n\ndef get_id_thresh(dist_mat, pred_map, all_data):\n thresh_perc = 5 # percentile to use for thresholds\n diag_mat = np.diag(dist_mat)\n\n # Find thresholds for close and far\n\n # use intra tracklet distance to find the close threshold\n close_thresh = np.percentile(diag_mat,100-thresh_perc)\n close_thresh = max(0.5,close_thresh)\n\n # use distance to overlapping tracklets to find the far thresholds. Using just the first movie for now\n mov1_sel = pred_map[:,0]==0\n sel = pred_map[mov1_sel, 1]\n st_sel = all_data[0][-2]\n en_sel = all_data[0][-1]\n ns = sel.size\n\n overlap = np.zeros([ns,ns])\n for ndx in range(ns):\n aa, bb = get_overlap(st_sel, en_sel, st_sel[ndx], en_sel[ndx], ndx)\n if aa.size > 0:\n overlap[ndx, aa] = bb\n mov1_dist = dist_mat[np.ix_(mov1_sel,mov1_sel)]\n overlap_dist = mov1_dist[overlap>0.1]\n far_thresh = np.percentile(overlap_dist,thresh_perc)\n return close_thresh, far_thresh\n\n\ndef group_tracklets_with_links(dist_mat_orig,pred_map_orig,linked_trks,conf,maxcosts_all,all_data,link_costs_arr,close_thresh,far_thresh,min_len_select):\n '''Groups the tracklets by *first* clustering the tracklets based on id embeddings and then filling in the gaps in the group using the linking distances'''\n\n ignore_far = False\n min_group_frac = 0.1\n\n dist_mat = dist_mat_orig.copy()\n n_tr = dist_mat.shape[0]\n dist_mat[range(n_tr),range(n_tr)] = 0.\n\n minv, maxv = linked_trks[0].get_min_max_val()\n minv = np.min(minv, axis=0)\n maxv = np.max(maxv, axis=0)\n bignumber = np.sum(maxv - minv) * 2000\n\n st_sel = all_data[0][-2]\n en_sel = all_data[0][-1]\n\n mov_len = max(en_sel)\n st_all = np.concatenate([a[-2] for a in all_data],0)\n en_all = np.concatenate([a[-1] for a in all_data], 0)\n sel_len = en_all-st_all+1\n max_group_sz_for_filling = mov_len*min_group_frac\n\n maxn_all = []\n for trk in linked_trks:\n ss, ee = trk.get_startendframes()\n maxn_all.append(max(ee))\n\n t_info = [d[3:5] for d in all_data]\n\n pred_map = pred_map_orig.copy()\n rem_id_trks = np.zeros(dist_mat.shape[0]) < 0.5\n groups = []\n groups_only_id = []\n used_trks = []\n\n # create id clusters iteratively by first finding the largest cluster and then adding the missing links. Most of the codes dirtiness is for keeping track of the id tracks and other tracks that have been used till now\n all_gr_sz = []\n while True:\n rem_id_idx = np.where(rem_id_trks)[0]\n if len(rem_id_idx)==0:\n break\n elif len(rem_id_idx)==1:\n gr = rem_id_idx\n far_ids = np.array([])\n else:\n dist_mat_cur = dist_mat[rem_id_trks, :][:,rem_id_trks]\n pred_map_cluster = pred_map_orig[rem_id_trks]\n gr_cur, gr_sz = get_largest_cluster(dist_mat_cur, close_thresh, t_info, pred_map_cluster)\n all_gr_sz.append(gr_sz)\n gr = rem_id_idx[gr_cur]\n\n close_trk_sz = np.zeros(dist_mat_cur.shape[0])\n close_trk_count = np.zeros(dist_mat_cur.shape[0])\n sel_len_cur = sel_len[rem_id_idx]\n for ix in range(dist_mat_cur.shape[0]):\n ff = np.where(dist_mat_cur[ix] < close_thresh)[0]\n close_trk_sz[ix] = sum(sel_len_cur[ff])\n close_trk_count[ix] = len(ff)\n far_trks = (close_trk_sz > max_group_sz_for_filling) & ( close_trk_count>3)\n far_ids = np.where(far_trks)[0]\n # far_ids = dist_mat_cur[gr_cur].mean(axis=0) > far_thresh\n far_ids = rem_id_idx[far_ids]\n far_ids = np.array(list(set(far_ids) - set(gr)))\n\n gr = gr.tolist()\n groups_only_id.append(gr.copy())\n\n # Ignore the tracklets that have been used already for the next round.\n for mov_ndx in range(len(linked_trks)):\n ids_ignore = []\n if ignore_far:\n for ff in far_ids:\n if pred_map_orig[ff][0] == mov_ndx:\n ids_ignore.append(pred_map_orig[ff][1])\n for uu in used_trks:\n if pred_map[uu][0] == mov_ndx:\n ids_ignore.append(pred_map[uu][1])\n\n # we add the missing links before grouping. Consider the case where there is missing link that perfectly fills the gap but doesn't match the identity because animal is doing weird stuff. In that case if we group before filling in the gaps, this tracklet will end up with its own group and we wouldn't know whether ungroup it to add it as part of missing link or not. We could set the minimum group size but that would not work with the case where animals move in or out.\n gr, pred_map = add_missing_links(linked_trks, [gr], conf, pred_map, mov_ndx, ids_ignore, maxcosts_all[mov_ndx],maxn_all[mov_ndx], bignumber, link_costs_arr)\n gr = gr[0]\n\n for gg in gr:\n if gg<len(rem_id_trks):\n rem_id_trks[gg] = False\n\n used_trks.extend(gr)\n groups.append(gr)\n\n return groups,pred_map,[groups_only_id,groups,pred_map]\n\ndef group_tracklets(dist_mat_orig,pred_map_orig,linked_trks,conf,maxcosts_all,all_data,link_costs_arr,close_thresh,far_thresh,min_len_select,preds):\n '''Groups the tracklets by *first* clustering the tracklets based on id embeddings and then filling in the gaps in the group using the linking distances'''\n\n ignore_far = False\n min_group_frac = 0.1\n\n dist_mat = dist_mat_orig.copy()\n n_tr = dist_mat.shape[0]\n dist_mat[range(n_tr),range(n_tr)] = 0.\n\n minv, maxv = linked_trks[0].get_min_max_val()\n minv = np.min(minv, axis=0)\n maxv = np.max(maxv, axis=0)\n bignumber = np.sum(maxv - minv) * 2000\n\n # st_sel = all_data[0][-2]\n # en_sel = all_data[0][-1]\n\n # mov_len = max(en_sel)\n # st_all = np.concatenate([a[-2] for a in all_data],0)\n # en_all = np.concatenate([a[-1] for a in all_data], 0)\n # sel_len = en_all-st_all+1\n # max_group_sz_for_filling = mov_len*min_group_frac\n\n maxn_all = []\n for trk in linked_trks:\n ss, ee = trk.get_startendframes()\n maxn_all.append(max(ee))\n\n t_info = [d[3:5] for d in all_data]\n\n pred_map = pred_map_orig.copy()\n rem_id_trks = np.zeros(dist_mat.shape[0]) < 0.5\n groups = []\n groups_only_id = []\n used_trks = []\n\n # create id clusters iteratively by first finding the largest cluster. Most of the codes dirtiness is for keeping track of the id tracks and other tracks that have been used till now\n all_gr_sz = []\n while True:\n rem_id_idx = np.where(rem_id_trks)[0]\n if len(rem_id_idx)==0:\n break\n elif len(rem_id_idx)==1:\n gr = rem_id_idx\n else:\n dist_mat_cur = dist_mat[rem_id_trks, :][:,rem_id_trks]\n pred_map_cluster = pred_map_orig[rem_id_trks]\n gr_cur, gr_sz = get_largest_cluster(dist_mat_cur, close_thresh, t_info, pred_map_cluster,preds)\n all_gr_sz.append(gr_sz)\n gr = rem_id_idx[gr_cur]\n\n gr = gr.tolist()\n groups_only_id.append(gr.copy())\n\n for gg in gr:\n if gg<len(rem_id_trks):\n rem_id_trks[gg] = False\n\n\n for gr_in in groups_only_id:\n far_ids = np.array([])\n\n # Ignore the tracklets that have been used already for the next round.\n for mov_ndx in range(len(linked_trks)):\n ids_ignore = []\n for uu in itertools.chain(*groups_only_id):\n if pred_map[uu][0] == mov_ndx:\n ids_ignore.append(pred_map[uu][1])\n\n for uu in itertools.chain(*groups):\n if pred_map[uu][0] == mov_ndx:\n ids_ignore.append(pred_map[uu][1])\n\n gr, pred_map = add_missing_links(linked_trks, [gr_in], conf, pred_map, mov_ndx, ids_ignore, maxcosts_all[mov_ndx],maxn_all[mov_ndx], bignumber, link_costs_arr)\n gr = gr[0]\n\n groups.append(gr)\n\n return groups,pred_map,[groups_only_id,groups,pred_map]\n\n\ndef match_motion_id(link_costs, jj, sel_tgt, close_thresh,dist_mat):\n ''' Find the lowest greedy motion match for tracklet jj going forward and backward. If the lowest match also matches in id space, then return the match. Otherwise return the match and the next tracklet that it matches in id space. Also return the link costs for the matched tracklets'''\n\n sjj = sel_tgt[jj]\n curl = link_costs[sjj][1]\n lls = []\n match = False\n ns = []\n while True:\n if len(curl) < 1: break\n miix = np.argmin(curl[:, 3])\n nextl = int(curl[miix, 0])\n ns = np.where(sel_tgt == nextl)[0]\n lls.append(curl[miix])\n if len(ns) > 0: # if the next tracklet is in the selected tracklets the check if it is close in id space\n if dist_mat[jj, ns] < close_thresh:\n match = True\n else:\n match = False\n break\n curl = link_costs[nextl][1]\n\n ns = ns[0] if len(ns) > 0 else ns\n\n if match: # if we match in forward direction then check if we match in backward direction\n bmatch = False\n curl = link_costs[sel_tgt[ns]][0]\n while True:\n if len(curl) < 1: break\n miix = np.argmin(curl[:, 3])\n nextl = int(curl[miix, 0])\n bs = np.where(sel_tgt == nextl)[0]\n if len(bs) > 0:\n if bs == jj:\n bmatch = True\n else:\n bmatch = False\n break\n curl = link_costs[nextl][0]\n\n match = match & bmatch\n\n return match,ns,sel_tgt[ns], lls\n\ndef find_fwd_path(curl, link_costs, sel_tgt, depth):\n if depth > 20:\n return []\n if len(curl)<1:\n return []\n all_rr = []\n jx = np.argsort(curl[:,-1])\n jx = jx[:2] if len(jx)> 2 else jx\n for ndx in jx:\n if curl[ndx][0] in sel_tgt:\n all_rr.append([curl[ndx]])\n else:\n rr = find_fwd_path(link_costs[int(curl[ndx][0])][1], link_costs, sel_tgt, depth + 1)\n for r in rr:\n r.insert(0, curl[ndx])\n all_rr.extend(rr)\n return all_rr\n\n\ndef find_bck_path(curl, link_costs, sel_tgt, depth):\n if depth > 20:\n return []\n if len(curl)<1:\n return []\n all_rr = []\n jx = np.argsort(curl[:,-1])\n jx = jx[:2] if len(jx)> 2 else jx\n for ndx in jx:\n if curl[ndx][0] in sel_tgt:\n all_rr.append([curl[ndx]])\n else:\n rr = find_bck_path(link_costs[int(curl[ndx][0])][0], link_costs, sel_tgt, depth + 1)\n for r in rr:\n r.insert(0, curl[ndx])\n all_rr.extend(rr)\n return all_rr\n\n\ndef get_path_link_cost(cur_link):\n q = np.array(cur_link)\n dist_cost = max(q[:, 1])\n motion_cost = max(q[:, 3])\n return dist_cost,motion_cost\n\n\ndef conn_bck(idx_end,idx_start,link_costs,sel_tgt,mthresh,conn_ratio=2):\n # checks if non unique path from start_idx to end_idx also is the lowest cost path backwards from idx_end to idx_start and\n back_links = find_bck_path(link_costs[idx_end][0],link_costs,sel_tgt,0)\n all_starts = np.unique([int(qr[-1][0]) for qr in back_links])\n i1 = np.where(idx_start==all_starts)[0]\n if len(i1)==0:\n return False\n\n min_cost_i1 = 1000000\n min_cost_rest = 1000000\n for cur_p in back_links:\n dc,mc = get_path_link_cost(cur_p)\n if cur_p[-1][0]==idx_start:\n min_cost_i1 = min(min_cost_i1,mc)\n else:\n min_cost_rest = min(min_cost_rest,mc)\n\n if ((min_cost_rest/min_cost_i1)>conn_ratio) and (min_cost_i1<mthresh):\n return True\n else:\n return False\n\n\ndef group_tracklets_motion(dist_mat,linked_trk,link_costs,close_thresh,min_len_select):\n '''\n Groups tracklets based on motion estimate errors. It has 3 steps when starting from a selected tracklet\n 1) If the lowest cost motion tracklet going forward in time also is close in ID embedding space, then link them. Using all the linking that we do in this step, we find what is a reasonable motion estimate error \"mtresh\"\n 2) The tracklets for which we couldn't find a match in step 1), we then find the number of lowest cost matching tracklets that we can link to. If there is only 1 tracklet we can link to and the cost to linking to it is less than mthresh, then link them -- these are unique (unq) pairs.\n 3) If we can link to multiple tracklets then we find the tracklet with lowest cost and the tracklet with second lowest cost. If the cost of linking to the lowest cost tracklet is less than \"mthresh\" and the ratio of the lowest costs and second lowest costs > 2, then we link them\n '''\n\n ss, ee = linked_trk.get_startendframes()\n sel_tgt = np.where((ee - ss + 1) >= min_len_select)[0]\n\n lc = []\n for jj in range(len(sel_tgt)):\n lc.append(list(match_motion_id(link_costs,jj,sel_tgt,close_thresh,dist_mat)))\n # match_motion_id returns match,ns,sel_tgt[ns], lls\n\n match_m = np.array([max(np.array(l[3])[:, 3]) for l in lc if (len(l[3]) > 0) and l[0] and len(l) > 0])\n mthresh = np.percentile(match_m, 95)\n\n # break links where the linking cost is ridiculously high\n for ll in lc:\n if ll[0] and max(np.array(ll[3])[:,3])>mthresh*3:\n ll[0] = False\n\n taken = np.zeros(len(ss))\n for l in lc:\n if l[0]:\n taken[l[2]] = 1.\n\n link_type = np.zeros([len(ss),2])\n links = []\n for jj in range(len(sel_tgt)):\n if lc[jj][0]:\n link_type[sel_tgt[jj],0] = 1\n link_type[sel_tgt[jj],1] = lc[jj][2]\n continue\n\n sjj = sel_tgt[jj]\n curl = link_costs[sjj][1]\n links.append([jj, find_fwd_path(curl, link_costs, sel_tgt, 0)])\n\n non_unq = []\n unq = []\n for ix, li in enumerate(links):\n jr = [int(l[-1][0]) for l in li[1]]\n ur = np.unique(jr) # all the tracklets that the current tracklet can link to\n kl = [[100000, 100000, ijj] for ijj in ur]\n if len(ur) > 1:\n # for each non-unique end tracklet find the minimum cost of the connection\n for l in li[1]:\n cur_end = np.where(ur == l[-1][0])[0][0]\n dc,mc = get_path_link_cost(l)\n kl[cur_end][0] = min(kl[cur_end][0], dc)\n kl[cur_end][1] = min(kl[cur_end][1], mc)\n non_unq.append([ix, li[0], kl, li]) # ix is the index of the start tracklet, li[0] is the start tracklet, kl is the minimum cost to link to each end tracklet, li is the path to each end tracklet\n\n elif len(ur) == 1:\n for l in li[1]:\n dc,mc = get_path_link_cost(l)\n kl[0][0] = min(kl[0][0], dc)\n kl[0][1] = min(kl[0][1], mc)\n unq.append([ix, li[0], kl[0], li]) # ix is the index of the start tracklet, li[0] is the start tracklet, kl is the minimum cost to link to the end tracklet, li is the path to the end tracklet\n\n mu = np.array([gq[2][1] for gq in unq]) # all motion costs for unique pairs\n mn = np.array([np.sort(np.array(gq[2])[:, 1])[0] for gq in non_unq]) #lowest motion cost for non unique pairs\n mn1 = np.array([np.sort(np.array(gq[2])[:, 1])[1] for gq in non_unq]) #second lowest motion cost for non unique pairs\n\n mux = [l[1] for l in unq]\n mnux = [l[1] for l in non_unq]\n mu_sel = mu < mthresh\n mn_sel = (mn < mthresh) & ((mn1 / mn) > 2)\n\n mgrs = []\n mgrs_startndx = []\n\n gr_ids = np.ones(len(ss)).astype('int')*-1\n all_paths = [[] for _ in range(len(ss))]\n for jj in np.argsort(ss[sel_tgt]):\n ojj = sel_tgt[jj]\n\n path = []\n if lc[jj][0]:\n path = [int(l[0]) for l in lc[jj][3]]\n elif jj in mux:\n muxj = mux.index(jj)\n if mu_sel[muxj] and (taken[unq[muxj][2][-1]]<0.5):\n idx = int(unq[muxj][3][1][0][-1][0])\n if conn_bck(idx,ojj,link_costs,sel_tgt,mthresh):\n paths = [l for l in unq[muxj][3][1] if np.all(np.array(l)[:, 3] < mthresh)]\n pl = [len(l) for l in paths]\n plsel = paths[np.argmax(pl)]\n # from multiple paths select the path with most intermediate links but where each link is still < mthresh\n path = [int(l[0]) for l in plsel]\n link_type[ojj,0] = 2\n link_type[ojj,1] = path[-1]\n else:\n logging.info(f'A unique path from {ojj} to {idx} does not link back!')\n\n elif jj in mnux:\n mnuxj = mnux.index(jj)\n if mn_sel[mnuxj]:\n idx = non_unq[mnuxj][2][np.argmin(np.array(non_unq[mnuxj][2])[:, 1])][-1]\n cback = conn_bck(idx,ojj,link_costs,sel_tgt,mthresh)\n if (taken[idx]>0.5) and cback:\n logging.info(f'End of a non-unique path from {ojj} to {idx} is already linked')\n\n if (taken[idx]<0.5) and cback:\n # Find the end tracklet with lowest motion cost\n plen = -1\n path = []\n ps = non_unq[mnuxj][-1][1]\n # ps has all the paths starting from tracklet ojj\n link_type[ojj, 0] = 3\n link_type[ojj, 1] = idx\n for pp in ps:\n # for non unique paths, choose the path with the most number of intermediate tracklets to the matching end tracklet\n if pp[-1][0] == idx and (len(pp) > plen) and (np.all(np.array(pp)[:, 3] < mthresh)):\n plen = len(pp)\n path = [int(p[0]) for p in pp]\n\n path.insert(0,ojj)\n all_paths[ojj] = path.copy()\n prev_grs = gr_ids[path]\n prev_grs = prev_grs[prev_grs>=0]\n if len(prev_grs)==0:\n mgrs.append(path)\n mgx = len(mgrs)-1\n mgrs_startndx.append([jj,])\n else:\n mgx = np.unique(prev_grs)\n if len(mgx)>1:\n logging.warning('!!!')\n logging.warning('Something very wrong in grouping tracklets with motion!!!')\n logging.warning('!!!')\n mgx = mgx[0]\n mgrs[mgx].extend(path)\n mgrs_startndx[mgx].append(jj)\n gr_ids[path] = mgx\n\n mgrs = [list(set(m)) for m in mgrs]\n return mgrs, link_type, all_paths\n\ndef overlap_dist(dmat, c1, c2, overlap,mgr_lens):\n l1 = np.sum(mgr_lens[c1])\n l2 = np.sum(mgr_lens[c2])\n overl = np.sum(overlap[c1][:,c2])\n ov = overl/min(l1,l2)\n if ov > 0.1:\n return 2.\n else:\n return np.mean(dmat)\n\n\ndef weighted_linkage(xmat, overlap, mgr_lens, thresh):\n ymat = xmat.copy()\n clusters = [[i] for i in range(xmat.shape[0])]\n while True:\n min_d = np.inf\n for i in range(len(clusters)):\n for j in range(i + 1, len(clusters)):\n dcur = ymat[i, j]\n if dcur < min_d:\n min_d = dcur\n min_i, min_j = i, j\n\n if min_d > thresh:\n break\n\n clusters[min_i] += clusters[min_j]\n del clusters[min_j]\n\n ymat[min_j:-1, :] = ymat[min_j + 1:]\n ymat[:, min_j:-1] = ymat[:, min_j + 1:]\n for i in range(len(clusters)):\n if i != min_i:\n c1 = clusters[i]\n c2 = clusters[min_i]\n ymat[i, min_i] = overlap_dist(xmat[c1][:, c2], c1, c2, overlap, mgr_lens)\n ymat[min_i, i] = ymat[i, min_i]\n\n return clusters\n\n\ndef group_tracklets_motion_all(dist_mat,pred_map_orig,linked_trks,conf,maxcosts_all,all_data,link_costs_arr,close_thresh,far_thresh,min_len_select):\n\n pred_map = pred_map_orig.copy()\n motion_grs_all = []\n sel_ids = []\n tr_len = []\n maxn = 0\n ss_all = []\n ee_all = []\n\n link_data = []\n for ndx in range(len(linked_trks)):\n trk = linked_trks[ndx]\n cur_ss, cur_ee = trk.get_startendframes()\n ss_all.append(cur_ss)\n ee_all.append(cur_ee)\n maxn = max(maxn,max(cur_ee))\n tr_len.append(cur_ee-cur_ss+1)\n link_cost =link_costs_arr[ndx]\n cur_sel = np.array([ix for ix in range(len(pred_map)) if pred_map[ix,0]==ndx])\n sel_tgt = pred_map[cur_sel,1]\n cur_dist_mat = dist_mat[cur_sel][:,cur_sel]\n motion_grs,link_type,all_paths = group_tracklets_motion(cur_dist_mat,trk,link_cost,close_thresh,min_len_select)\n link_data.append(link_type)\n for m in motion_grs:\n motion_grs_all.append([ndx,m])\n cur_sel_ids = []\n for mm in m:\n gndx = np.where(np.all(pred_map==np.array([ndx,mm]),axis=1))[0]\n if len(gndx)>0:\n cur_sel_ids.append(gndx[0])\n sel_ids.append(cur_sel_ids)\n\n tlen = []\n for pp in pred_map:\n tlen.append(tr_len[pp[0]][pp[1]])\n tlen = np.array(tlen)\n\n\n # For each group created above, find the id distance between the tracklets in the group and use that to create a distance matrix between the groups\n\n nsel = len(sel_ids)\n xmat = np.zeros([nsel, nsel])\n for x1, s1 in enumerate(sel_ids):\n for x2, s2 in enumerate(sel_ids):\n cmat = dist_mat[s1][:, s2].copy()\n if x1 == x2:\n if len(s1) == 1:\n cmat[range(len(s1)), range(len(s1))] = np.nan\n else:\n cmat[range(len(s1)), range(len(s1))] = 2.\n mx1 = np.average(np.min(cmat,axis=0),weights=tlen[s2])\n mx2 = np.average(np.min(cmat,axis=1),weights=tlen[s1])\n xmat[x1,x2] = (mx1+mx2)/2\n # mxx = np.concatenate([np.min(cmat, axis=0), np.min(cmat, axis=1)])\n # wxx = np.concatenate([tlen[s2], tlen[s1]]).astype('float')\n # xmat[x1, x2] = np.average(mxx, weights=wxx)\n # xmat[x1,x2] = np.median(mxx)\n\n xthresh = np.nanpercentile(np.diag(xmat), 90)\n xthresh = max(close_thresh,xthresh)\n\n # diagnal as 0.\n xmat[range(nsel), range(nsel)] = 0.\n\n overlaps = np.zeros([nsel,nsel]) #overlap between different groups\n mgr_lens = np.zeros(nsel) # length of each group\n mov_ndx = np.array([p[0] for p in motion_grs_all])\n\n for ndx in range(len(linked_trks)):\n cur_mgrs = np.where(mov_ndx==ndx)[0]\n lenso = np.zeros([len(cur_mgrs), maxn])\n for nxx, gndx in enumerate(cur_mgrs):\n gi = motion_grs_all[gndx][1]\n for gii in gi:\n lenso[nxx, ss_all[ndx][gii]:ee_all[ndx][gii] + 1] = 1\n\n for x1 in range(len(lenso)):\n mgr_lens[cur_mgrs[x1]] = np.sum(lenso[x1]>0)\n for x1 in range(len(lenso)):\n for x2 in range(len(lenso)):\n if x1 >= x2: continue\n ll = np.sum((lenso[x1] * lenso[x2]) > 0)\n overlaps[cur_mgrs[x1],cur_mgrs[x2]] = ll\n overlaps[cur_mgrs[x2],cur_mgrs[x1]] = ll\n\n # groups using linkage\n F1 = weighted_linkage(xmat,overlaps,mgr_lens,xthresh)\n F = np.zeros(nsel).astype('int')\n for ndx, c in enumerate(F1):\n for cc in c:\n F[cc] = ndx+1\n\n grs = []\n ggv = []\n gv_len = []\n new_pred_map = []\n gr_data = []\n for gi in range(max(F)):\n gv = np.where(F == (gi + 1))[0]\n ggv.append(gv)\n curg = []\n clen = 0\n for gvv in gv:\n curmov = motion_grs_all[gvv][0]\n for g in motion_grs_all[gvv][1]:\n curg.append(len(new_pred_map))\n new_pred_map.append([curmov,g])\n clen += ee_all[curmov][g] - ss_all[curmov][g]+1\n grs.append(curg)\n gv_len.append(clen)\n gr_data.append([motion_grs_all[gvv] for gvv in gv])\n\n new_pred_map = np.array(new_pred_map)\n minv, maxv = linked_trks[0].get_min_max_val()\n minv = np.min(minv, axis=0)\n maxv = np.max(maxv, axis=0)\n bignumber = np.sum(maxv - minv) * 2000\n\n for mov_ndx in range(len(linked_trks)):\n grs, new_pred_map = add_missing_links(linked_trks, grs, conf, new_pred_map, mov_ndx, None, maxcosts_all[mov_ndx], maxn, bignumber, link_costs_arr)\n\n debug_data = [link_data,motion_grs_all,pred_map,gr_data,gv_len,xmat]\n return grs, new_pred_map, debug_data\n\n\ndef link_trklet_id(linked_trks, net, mov_files, conf, all_trx, rescale=1, min_len_select=5, debug=False, keep_all_preds=False,link_method='motion',out_file=None,num_animals=None,\n detected_identities_file=None,id_model_file=None,trk_files=None):\n '''\n Links the pure tracklets using identity\n\n :param linked_trks: pure linked tracks\n :param net: id network\n :param mov_files: movie files\n :param conf: poseconfig object\n :param all_trx: pure linked tracks loaded in the trx format for apt.create_batch_ims\n :param rescale:\n :param min_len_select:\n :param debug:\n :param detected_identities_file: for graph_cut linking, file with the saved detected identities. If it exists, the identities are read from it, otherwise the identities found here are saved to it.\n :param id_model_file: path of the ID model used to compute the embeddings. Saved to the detected identities file for provenance.\n :param trk_files: detection trk files being linked. Saved to the detected identities file for provenance.\n :return: list of id linked tracklets\n '''\n\n if detected_identities_file is not None and link_method != 'graph_cut':\n logging.warning(f'A detected identities file ({detected_identities_file}) was specified, but the linking method is '\n f'{link_method}. Detected identities are used only by graph_cut linking, so the file will be ignored.')\n\n # True exactly when group_graph_cut() will load (not compute) the cluster\n # centers -- i.e. the final identity labels have externally-meaningful\n # values (indices into the saved cluster centers) that should be preserved\n # by Tracklet.unique() rather than remapped in raw-tracklet encounter order.\n keep_id = link_method == 'graph_cut' and detected_identities_file is not None and os.path.exists(detected_identities_file)\n\n preds, pred_map, all_data = get_id_embeddings(linked_trks,net,mov_files,conf,all_trx,rescale,min_len_select,debug)\n\n # dist_mat, pred_map, all_data,preds = get_id_dist_xmat(linked_trks,net,mov_files,conf,all_trx,rescale,min_len_select,debug)\n if link_method == 'graph_cut':\n # For graph_cut we only need the diagonal (intra-tracklet distances) to compute close_thresh.\n # The full N×N dist_mat is not needed here; get_id_cluster_centers computes only the\n # relevant submatrix internally.\n dist_diag = get_id_dist_mat_diag(preds)\n thresh_perc = 5\n close_thresh = max(0.5, np.percentile(dist_diag, 100 - thresh_perc))\n far_thresh = None\n else:\n dist_mat = get_id_dist_mat(preds)\n close_thresh, far_thresh = get_id_thresh(dist_mat, pred_map, all_data)\n\n if out_file is not None:\n if link_method == 'graph_cut':\n var_list = ['preds', 'pred_map', 'all_data', 'conf', 'mov_files', 'dist_diag', 'close_thresh']\n else:\n var_list = ['preds', 'pred_map', 'all_data', 'conf', 'mov_files', 'dist_mat', 'close_thresh', 'far_thresh']\n out_dict = {}\n for vv in var_list:\n exec(f'out_dict[\"{vv}\"]={vv}')\n import pickle\n with open(out_file, 'wb') as out_file_:\n pickle.dump(out_dict, out_file_)\n\n\n\n logging.info('Stitching tracklets based on identity ...')\n\n maxcosts_all = []\n params = get_default_params(conf)\n link_costs_arr = []\n for tndx in range(len(linked_trks)):\n maxcost_missed = estimate_maxcost_missed(linked_trks[tndx], params)\n maxcost = estimate_maxcost(linked_trks[tndx], params)\n maxcosts_all.append(np.array([maxcost, ] + maxcost_missed.tolist()))\n # FInd the linkinking costs between the tracklets\n st, en = linked_trks[tndx].get_startendframes()\n link_costs = get_link_costs(linked_trks[tndx], st, en, params)\n link_costs_arr.append(link_costs)\n\n # Cluster the embedding using linkage. each group in groups specifies which tracklets belong to the same animal\n pred_map_orig = pred_map.copy()\n if link_method == 'graph_cut':\n groups, pred_map, all_labels, cluster_centers = group_graph_cut(linked_trks,pred_map,preds,dist_diag,close_thresh,maxcosts_all,link_costs_arr,conf=conf,num_animals=num_animals,\n detected_identities_file=detected_identities_file,id_model_file=id_model_file,mov_files=mov_files,trk_files=trk_files)\n debug_data = []\n elif link_method=='motion':\n groups,pred_map,debug_data = group_tracklets_motion_all(dist_mat,pred_map,linked_trks,conf,maxcosts_all,all_data,link_costs_arr,close_thresh,far_thresh,min_len_select)\n else:\n groups,pred_map,debug_data = group_tracklets(dist_mat,pred_map,linked_trks,conf,maxcosts_all,all_data,link_costs_arr,close_thresh,far_thresh,min_len_select,preds)\n\n\n n_detected_ids = len(groups)\n\n if False:\n # to visualize the clusters\n import itertools\n vv = [[gxx for gxx in gg if gxx < len(pred_map_orig)] for gg in groups]\n aa = list(itertools.chain(*vv))\n plt.figure()\n plt.imshow(dist_mat[aa][:, aa])\n\n # If we want to keep all the predictions, then we need to add the remaining tracklets to the groups\n if keep_all_preds:\n used_trks = []\n for g in groups:\n used_trks.extend(g)\n\n for mov_ndx in range(len(linked_trks)):\n cur_used_ids = [pred_map[uu][1] for uu in used_trks if pred_map[uu][0] == mov_ndx]\n for ids in range(linked_trks[mov_ndx].ntargets):\n if ids not in cur_used_ids:\n pred_map = np.concatenate([pred_map, [[mov_ndx, ids]]], axis=0)\n groups.append([len(pred_map)-1])\n\n\n # Old style grouping\n # groups_new = groups.copy()\n # pred_map_id = pred_map.copy()\n # groups = cluster_tracklets_id(preds, pred_map_orig, t_info, conf.link_maxframes_delete)\n #\n # id_groups = groups.copy()\n # pred_map = pred_map_orig.copy()\n # for mov_ndx in range(len(linked_trks)):\n # groups, pred_map = add_missing_links(linked_trks, groups, conf, pred_map, mov_ndx, None, maxcosts_all[mov_ndx],maxn_all[mov_ndx],bignumber)\n\n\n # Link the actual pose data. ids is TrkFile.Tracklet instance that keeps track about which tracklet in which frame belongs to which animal\n\n ids = []\n for trk, data in zip(linked_trks,all_data):\n ss, ee = data[3:5]\n cur_id = TrkFile.Tracklet(defaultval=-1, size=(1, trk.ntargets,trk.T))\n cur_id.allocate( (1,), ss, ee)\n ids.append(cur_id)\n\n for ndx, gr in enumerate(groups):\n for gg in gr:\n mov_ndx, trk_ndx = pred_map[gg]\n cur_id = ids[mov_ndx]\n cur_trk = linked_trks[mov_ndx]\n data = all_data[mov_ndx]\n sf,ef = data[3:5]\n cur_p = np.ones(ef[trk_ndx]-sf[trk_ndx]+1)* ndx\n cur_id.settarget(cur_p, trk_ndx, sf[trk_ndx], ef[trk_ndx])\n\n # cur_tgt = min(sel_tgt[gr])\n # for gg in gr:\n # if sel_tgt[gg] == cur_tgt: continue\n # match_tgt = sel_tgt[gg]\n # trk.pTrk[..., ss[match_tgt]:ee[match_tgt]+1, cur_tgt] = trk.pTrk[..., ss[match_tgt]:ee[match_tgt]+1, match_tgt]\n # to_remove.append(match_tgt)\n # assigned_ids[match_tgt] = cur_tgt\n #\n # # Delete the trks that have been merged\n # trk.pTrk = np.delete(trk.pTrk, to_remove, -1)\n # for k in trk.trkFields:\n # if trk.__dict__[k] is not None:\n # trk.__dict__[k] = np.delete(trk.__dict__[k], to_remove, -1)\n # trk.ntargets = trk.ntargets - len(to_remove)\n\n if not keep_all_preds:\n logging.info(f'Deleting short trajectories with length less than {conf.link_maxframes_delete}')\n\n params = get_default_params(conf)\n for cur_id, cur_trk in zip(ids, linked_trks):\n _, maxv = cur_id.get_min_max_val()\n nids = np.max(maxv) + 1\n\n if not keep_all_preds:\n t0s = np.zeros(nids, dtype=int)\n t1s = np.zeros(nids, dtype=int)\n ids_remove = []\n idx_all = cur_id.where_all(nids)\n for id in range(nids):\n idx = [idx_all[0][id],idx_all[1][id]]\n # idx = np.nonzero(id==ids)\n if idx[1].size>0:\n t0s[id] = np.min(idx[1])\n t1s[id] = np.max(idx[1])\n else:\n t1s[id] = -1\n ids_remove.append(id)\n isdummy = TrkFile.Tracklet(defaultval=False, size=(1, nids, cur_id.T))\n isdummy.allocate((1,), t0s, t1s)\n\n ids_short = []\n cur_id, ids_short = delete_short(cur_id, isdummy, params)\n if len(linked_trks)<2:\n _, cur_id = cur_id.unique(keep_id=keep_id)\n else:\n ids_short = []\n ids_remove = []\n\n ids_left = [i for i in range(nids) if (i not in ids_short) and (i not in ids_remove)]\n # Apply the ids to trk.\n cur_trk.apply_ids(cur_id)\n cur_trk.pTrkiTgt = np.array(ids_left)\n if not keep_all_preds:\n interpolate_gaps(cur_trk)\n\n return linked_trks,debug_data,n_detected_ids\n\n\ndef interpolate_gaps(trk):\n '''\n Fill in small gaps using interpolation\n :param trk:\n :return:\n '''\n\n thresh = 1.\n # If the radio of (distance of the pose across the gap) and\n # (the size of the animal) is less than this thresh than interpolate\n\n max_gap = 3\n ss,ee = trk.get_startendframes()\n for xx in range(trk.ntargets):\n if (ee[xx]<0) or (ss[xx]<0):\n continue\n\n # Fast check: look for NaN directly in raw data before paying the cost\n # of gettargetframe. Most tracklets have no gaps so this skips them cheaply.\n if trk.issparse:\n raw_jj = trk.pTrk.data[xx][0, 0, :]\n else:\n raw_jj = trk.pTrk[0, 0, ss[xx]-trk.T0:ee[xx]-trk.T0+1, xx]\n if not np.any(np.isnan(raw_jj)):\n continue\n\n pp = trk.gettargetframe(xx,np.arange(ss[xx],ee[xx]+1))\n jj = pp[0, 0, :, 0]\n qq = np.ones(jj.shape[0]+2)>0\n qq[1:-1] = np.isnan(jj)\n\n qq1 = np.where( qq[:-2] & (~qq[1:-1]) )[0]\n qq2 = np.where( (~qq[1:-1]) & qq[2:] )[0]\n rr = qq1[1:]-qq2[:-1]-1\n did_interpolate = False\n for ndx in range(rr.size):\n a1 = pp[:,:,qq1[ndx+1],0]\n a2 = pp[:,:,qq2[ndx],0]\n dd = a1-a2\n dd = np.linalg.norm(dd,axis=1).mean()\n dd = dd/rr[ndx] # amortize the distance by the gap size\n sz1 = a1.max(axis=0)-a1.min(axis=0)\n sz1 = np.mean(sz1)\n sz2 = a2.max(axis=0)-a2.min(axis=0)\n sz2 = np.mean(sz2)\n csz = (sz1+sz2)/2\n\n if (rr[ndx]<= max_gap) and (dd/csz < thresh):\n for dim in range(pp.shape[1]):\n ii = PoseTools.linspacev(a1[:,dim],a2[:,dim],rr[ndx]+2)\n pp[:,dim,qq2[ndx]+1:qq1[ndx+1],0] = ii[:,1:-1]\n did_interpolate = True\n if did_interpolate:\n trk.settarget(pp[...,0],xx,ss[xx],ee[xx])\n\n\ndef get_dist(p1, p2):\n return np.sum(np.abs(p1 - p2), axis=(0, 1)) / p2.shape[0]\n # return np.sum(np.abs(p1 - p2), axis=(0, 1)) / p2.shape[0]\n\n\ndef add_missing_links(linked_trks, groups, conf, pred_map, tndx, ignore_idx, maxcosts_all, maxn, bignumber, link_costs_arr):\n\n # params = get_default_params(conf)\n mult = 15\n\n st, en = linked_trks[tndx].get_startendframes()\n link_costs = link_costs_arr[tndx]\n\n # occ = occupied or filled frames\n occ = np.ones([len(groups), maxn], 'int') * -1\n for ndx, gr in enumerate(groups):\n for gg in gr:\n mov_ndx, trk_ndx = pred_map[gg]\n if mov_ndx == tndx:\n occ[ndx, st[trk_ndx]:en[trk_ndx] + 1] = trk_ndx\n\n\n # FInd all the breaks for the identity groups\n breaks = []\n for ndx in range(occ.shape[0]):\n qq = np.zeros(maxn + 2) < 1\n qq[1:-1] = occ[ndx] > -0.5\n\n qq1 = np.where(qq[:-2] & (~qq[1:-1]))[0]\n qq2 = np.where((~qq[1:-1]) & qq[2:])[0]\n # to_rem = (qq1==0) | (qq2==maxn-1)\n # qq1 = qq1[~to_rem]\n # qq2 = qq2[~to_rem]\n breaks.append(np.array(list(zip(qq1, qq2))))\n\n if ignore_idx is None:\n taken = [pred_map[g,1] for gr in groups for g in gr if pred_map[g,0]==tndx]\n else:\n taken = ignore_idx\n\n linked_groups = groups.copy()\n new_pred_map = []\n # MOst of the crappiness of the code is keeping track of what has been taken and what has not been\n for ndx in range(len(linked_groups)):\n for bx in range(breaks[ndx].shape[0]):\n b_st, b_en = breaks[ndx][bx, :]\n\n if b_st > 0 and b_en<maxn-1:\n st_trk = occ[ndx][b_st - 1]\n en_trk = occ[ndx][b_en + 1]\n elif b_st<=0 and b_en<maxn-1:\n st_trk = -1\n en_trk = occ[ndx][b_en+1]\n elif b_st>0 and b_en>=maxn-1:\n st_trk = occ[ndx][b_st-1]\n en_trk = -1\n elif b_st==0 and b_en>=maxn-1:\n # covers the whole movie\n continue\n else:\n logging.warning('Weird stuff in linking')\n continue\n\n trk2add = find_path(link_costs, st_trk, en_trk, b_en + 1, st, en, maxcosts_all, taken, mult, bignumber)\n for tid in trk2add:\n match_pred = np.where((pred_map==[tndx,tid]).all(axis=1))[0]\n if len(match_pred)>0:\n linked_groups[ndx].append(match_pred[0])\n else:\n new_pred_map.append([tndx,tid])\n linked_groups[ndx].append(len(pred_map)+len(new_pred_map)-1)\n taken.append(tid)\n logging.info('DOne adding paths...')\n\n if len(new_pred_map)>0:\n new_pred_map = np.concatenate([pred_map, new_pred_map],axis=0)\n else:\n new_pred_map = pred_map\n return linked_groups, new_pred_map\n\n##\n\ndef find_path(link_costs, st_idx, en_idx, en_fr,st, en, maxcosts_all,taken, mult, bignumber):\n '''\n\n :param link_costs: cost of linking current tracklet to other tracklets\n :param st_idx: tracklet id to start linking from\n :param en_idx: tracklet id to link to\n :param en_fr: end frame\n :param st: all the tracklet starts\n :param en: all the tracklet ends\n :param maxcosts_all: linking costs across missing frames\n :param taken: do no use these tracklets\n :param mult:\n :param bignumber:\n :return: path for linking as a list of tracklets\n\n This function uses the shorteset path algorithm. Cost of linking tracklets tr1 that ends at t and tr2 that starts at t+n is maxcosts_all[n-1]/2. This is because we only want to mildly penalize the missing frame, as otherwise the algorithm will try to fill in some other tracklet tr3 that could be far from both tr1 and tr2.\n '''\n\n\n MAX_D = 100\n if st_idx>=0:\n link_st = en[st_idx] + 1\n else:\n link_st = 0\n if en_idx>=0:\n link_en = st[en_idx] - 1\n else:\n link_en = max(en)\n\n taken_arr = np.zeros(len(st)) > 0.5\n taken_arr[taken] = True\n\n # int_trks are the intermediate tracklets that lie with the gap and are not taken\n int_trks = np.where( (st>=link_st) & (en<= link_en) &\n~taken_arr)[0]\n n_trks = len(int_trks)\n\n # edge mat will be used to compute the shortest path. [:,0] corresponds to starting tracklet and [:,-1] correspoinds to ending tracklet\n debug = False\n if debug:\n logging.info(f'st_idx, {st_idx} en_idx{en_idx} link_st, {link_st}, link_en, {link_en}, ntrk, {n_trks},mem, {PoseTools.get_memory_usage()}')\n edge_mat = np.ones([n_trks+2,n_trks+2])*bignumber*(link_en-link_st+1)\n\n def get_link_cost(cur_link):\n cost = cur_link[1]\n n_miss = int(cur_link[2])\n if cost > mult*maxcosts_all[n_miss]:\n connect = False\n else:\n connect = True\n\n\n if n_miss>0:\n # if there are gaps then adjust the cost. The adjusting factor is (n_miss+1)*maxcosts_all[0]/maxcosts_all[n_miss]. maxcosts_all[0]/maxcosts_all[n_miss] is the sort of to counter the curvature. It seems the adjusting factor usually ends up being close to 1. So maybe it could be removed entirely.\n # To understand the adjusting factor, think there is a gap of 1 between S and E and there is a detection M in the in-between frame that has a distance of maxcosts_all[0] to both the start and the end. If cost(S,E) = maxcosts_all[1], then very likely detection M will get skipped. However, if we adjust using the adjusting factor then the detection M will get used when diong the shortest path. We bump it by 1.5 just to be safe.\n cur_cost = cost * 1.5 * (n_miss + 1) * maxcosts_all[0] / maxcosts_all[n_miss]\n else:\n cur_cost = cost\n\n return connect, cur_cost\n\n if st_idx>=0 and en_idx>=0:\n # direct cost between the start and the end tracklets if they can be connected\n if link_costs[st_idx][1].size > 0:\n en_ix = np.where(link_costs[st_idx][1][:,0]==en_idx)[0]\n if en_ix.size>0:\n connect, cost = get_link_cost(link_costs[st_idx][1][en_ix[0]])\n edge_mat[0,-1] = cost\n\n if st_idx>=0:\n # Costs from the start tracklet\n for ix in range(link_costs[st_idx][1].shape[0]):\n lix = int(link_costs[st_idx][1][ix, 0])\n if lix not in int_trks: continue\n lix_id = np.where(int_trks==lix)[0][0]\n connect, cost = get_link_cost(link_costs[st_idx][1][ix])\n if connect:\n edge_mat[0,lix_id+1] = cost\n\n if en_idx>=0:\n # Costs to the end tracklet\n for ix in range(link_costs[en_idx][0].shape[0]):\n lix = int(link_costs[en_idx][0][ix, 0])\n if lix not in int_trks: continue\n lix_id = np.where(int_trks==lix)[0][0]\n connect, cost = get_link_cost(link_costs[en_idx][0][ix])\n if connect:\n edge_mat[lix_id+1,-1] = cost\n\n # For all other tracklets\n for ix1 in range(n_trks):\n cur_trk = int_trks[ix1]\n for ix in range(link_costs[cur_trk][1].shape[0]):\n lix = int(link_costs[cur_trk][1][ix, 0])\n if lix not in int_trks: continue\n lix_id = np.where(int_trks==lix)[0][0]\n connect, cost = get_link_cost(link_costs[cur_trk][1][ix])\n if connect:\n edge_mat[ix1+1,lix_id + 1] = cost\n\n\n if st_idx<0:\n broken = True\n plen = np.array([0])\n else:\n conn_nodes, pred = scipy.sparse.csgraph.breadth_first_order(edge_mat<bignumber,0)\n plen = get_path_len(pred)\n if (pred[-1] < 0) or (plen[-1]>MAX_D):\n broken = True\n else:\n broken = False\n\n # do shortest path only on nodes that can be connected by less than MAX_D nodes\n sel_nodes = np.where( (plen<=MAX_D)&(plen>=0))[0]\n sel_edge_mat = edge_mat[sel_nodes][:,sel_nodes]\n dmat, conn_mat = scipy.sparse.csgraph.shortest_path(sel_edge_mat,return_predecessors=True,indices=0)\n\n\n if broken:\n # This happens if thre is no path. In this case connect as much as possible to the st_idx tracklet and en_idx tracklet\n if sel_nodes.size > 1:\n path = greedy_longest_path(sel_edge_mat,0,bignumber)\n # sel_int = sel_nodes[1:]\n # possible_end_trks = int_trks[sel_int-1]\n # lengths = en[possible_end_trks] - en[st_idx]\n # max_length_idx = np.argmax(lengths)\n # p_end = max_length_idx + 1\n # path.append(p_end)\n else:\n path = []\n else:\n path = []\n p_end = len(sel_nodes) - 1\n p_start = 0\n while p_end!=p_start:\n p_end = conn_mat[p_end]\n path.append(p_end)\n # remove the first tracklet which corresponds to st_idx\n path = path[:-1] if len(path)>0 else path\n\n path_sel = path\n path = [(sel_nodes[p]-1) for p in path_sel]\n\n if broken and en_idx>=0:\n # connect the longest possible path to en_idx\n\n conn_nodes, pred = scipy.sparse.csgraph.breadth_first_order(edge_mat.T < bignumber, edge_mat.shape[0]-1)\n plen_en = get_path_len(pred,len(pred)-1)\n\n # do shortest path only on nodes that can be connected by less than MAX_D nodes\n sel_nodes = np.where( (plen_en <= MAX_D) & (plen_en>=0))[0]\n if sel_nodes.size>1:\n sel_edge_mat = edge_mat[sel_nodes][:, sel_nodes]\n en_path = greedy_longest_path(sel_edge_mat.T,-1,bignumber)\n for p in en_path:\n path.append(sel_nodes[p]-1)\n\n # dmat_e, conn_mat = scipy.sparse.csgraph.shortest_path(sel_edge_mat.T, return_predecessors=True, indices=-1)\n #\n # sel_int = sel_nodes[:-1]-1\n # assert np.all(sel_int>=0), 'Some weird stuff'\n # possible_st_trks = int_trks[sel_int]\n # lengths = st[en_idx] - st[possible_st_trks]\n # max_length_idx = np.argmax(lengths)\n # p_start = max_length_idx\n # p_end = len(sel_nodes)-1\n #\n # while p_start!=p_end:\n # path.append(sel_nodes[p_start]-1)\n # p_start = conn_mat[p_start]\n\n # reconstruct the path in terms of tracklets\n path = [int_trks[ix] for ix in path]\n\n return path\n\ndef greedy_longest_path(edge_mat,st_pt,max_val):\n path = []\n if edge_mat.shape[0]<2: return path\n while True:\n nextp = np.argmin(edge_mat[st_pt])\n if edge_mat[st_pt,nextp]>(max_val-1): return path\n path.append(nextp)\n st_pt = nextp\n\ndef get_path_len(pred,end_pt=0):\n # pred is predecessors for graphs\n n = len(pred)\n plen = np.zeros(n)\n update = [[] for i in range(n)]\n cur_pred = pred.copy()\n\n def do_update(ix):\n for uix in update[ix]:\n plen[uix] = plen[ix] + 1\n do_update(uix)\n\n for ix in range(n):\n if cur_pred[ix]<0:\n plen[ix] = -1\n continue\n pp = cur_pred[ix]\n update[pp].append(ix)\n plen[end_pt] = 0\n do_update(end_pt)\n\n return plen\n\n\n\ndef get_link_costs(tt, st, en, params):\n maxn = max(en)\n n_missed = params['maxcost_framesfit']\n motion_eps = estimate_motion_stats(tt,st,en)\n link_costs = []\n for curt in range(tt.ntargets):\n\n cur_st = st[curt]\n cur_en = en[curt]\n start_matches = []\n p_cur = tt.gettargetframe(curt, cur_st)[...,0, 0]\n if (cur_en==cur_st) or (cur_st==maxn):\n p_next = p_cur\n else:\n p_next = tt.gettargetframe(curt,cur_st+1)[...,0,0]\n vmag = np.linalg.norm(p_cur-p_next,axis=-1).mean(axis=0)\n for tends in range(n_missed + 1):\n mpred = p_cur + (tends+1)*(p_cur-p_next)\n stfr = cur_st - tends - 1\n if stfr < 0:\n continue\n trk_sts = np.where(en == stfr)[0]\n for idx in trk_sts:\n p_en = tt.gettargetframe(idx, stfr)[...,0, 0]\n cur_cost = get_dist(p_cur, p_en)\n if st[idx]==en[idx]:\n p_en1 = p_en\n else:\n p_en1 = tt.gettargetframe(idx, stfr-1)[...,0, 0]\n mpred1 = p_en + (tends+1)*(p_en-p_en1)\n vmag1 = np.linalg.norm(p_en-p_en1,axis=-1).mean(axis=0)\n cur_mcost = np.linalg.norm(mpred-mpred1,axis=-1).mean(axis=0)/(vmag+vmag1+motion_eps)\n start_matches.append([idx, cur_cost, tends, cur_mcost])\n start_matches = np.array(start_matches)\n\n end_matches = []\n p_cur = tt.gettargetframe(curt, cur_en)[..., 0,0]\n if (cur_en==cur_st) or (cur_en==0):\n p_prev = p_cur\n else:\n p_prev = tt.gettargetframe(curt,cur_en-1)[...,0,0]\n vmag = np.linalg.norm(p_cur-p_prev,axis=-1).mean(axis=0)\n for tends in range(n_missed + 1):\n enfr = cur_en + tends + 1\n if enfr > maxn:\n continue\n mpred = p_cur + (tends+1)*(p_cur-p_prev)\n trk_ends = np.where(st == enfr)[0]\n for idx in trk_ends:\n p_st = tt.gettargetframe(idx, enfr)[..., 0,0]\n cur_cost = get_dist(p_cur, p_st)\n if st[idx]==en[idx]:\n p_st1 = p_st\n else:\n p_st1 = tt.gettargetframe(idx, enfr+1)[...,0, 0]\n mpred1 = p_st + (tends+1)*(p_st-p_st1)\n vmag1 = np.linalg.norm(p_st-p_st1,axis=-1).mean(axis=0)\n cur_mcost = np.linalg.norm(mpred-mpred1,axis=-1).mean(axis=0)/(vmag+vmag1+motion_eps)\n end_matches.append([idx, cur_cost, tends, cur_mcost])\n end_matches = np.array(end_matches)\n link_costs.append([start_matches, end_matches])\n return link_costs\n\ndef get_id_dist_mat(embed, dtype=np.float32, memory_budget=512 * 1024 ** 2):\n \"\"\"Mean distance between every pair of tracklets' sampled embeddings.\n\n dist_mat[i,j] is the mean over all sample pairs (a,b) of\n ||embed[i,a] - embed[j,b]||.\n\n Squared distances come from |x|^2 + |y|^2 - 2 x.y, so the pairwise term is one\n GEMM that BLAS threads for us, and only the upper triangle is computed and\n then mirrored. The previous version broadcast an (n_trk, n_ex, n_ex, dim)\n array per row -- hundreds of MB of temporaries per row -- and split the rows\n over a 24-process spawn pool, each worker re-importing this module.\n\n Being exactly symmetric by construction, the result no longer needs averaging\n with its own transpose before ssd.squareform (which checks symmetry exactly);\n the old row-splitting left a ~1e-6 asymmetry that did.\n\n dtype: the reduction is memory-bandwidth bound, so float32 roughly halves the\n time. It costs ~1e-4 absolute on distances of order 1; pass np.float64\n for ~1e-7 at twice the cost.\n memory_budget: cap in bytes on the largest temporary, which sets the row block\n size.\"\"\"\n n_trk, n_ex, dim = embed.shape\n flat = np.ascontiguousarray(embed.reshape(n_trk * n_ex, dim), dtype=dtype)\n sq = np.einsum('ij,ij->i', flat, flat)\n\n # The widest temporary is the first block: (block*n_ex) x (n_trk*n_ex).\n bytes_per_row = n_ex * n_trk * n_ex * np.dtype(dtype).itemsize\n block = max(1, int(memory_budget // max(bytes_per_row, 1)))\n\n dist_mat = np.zeros((n_trk, n_trk))\n for start in range(0, n_trk, block):\n stop = min(start + block, n_trk)\n rows = flat[start * n_ex:stop * n_ex]\n cols = flat[start * n_ex:] # upper triangle only; the rest is mirrored\n d2 = sq[start * n_ex:stop * n_ex, None] + sq[None, start * n_ex:]\n d2 -= 2 * (rows @ cols.T)\n np.maximum(d2, 0, out=d2) # tiny negatives from rounding\n np.sqrt(d2, out=d2)\n means = d2.reshape(stop - start, n_ex, n_trk - start, n_ex).mean(axis=(1, 3))\n # Within the diagonal block BLAS may accumulate dot(i,j) and dot(j,i) in\n # different orders, leaving them a rounding step apart. Mirror the upper\n # triangle onto the lower so the block, and hence dist_mat, is exactly\n # symmetric -- ssd.squareform checks that exactly.\n diag_block = means[:, :stop - start]\n lower = np.tril_indices(diag_block.shape[0], -1)\n diag_block[lower] = diag_block.T[lower]\n dist_mat[start:stop, start:] = means\n dist_mat[start:, start:stop] = means.T\n return dist_mat\n\ndef get_id_dist_mat_diag(embed):\n \"\"\"Compute only the diagonal of the embedding distance matrix (intra-tracklet distances).\n dist_mat[i,i] is the mean pairwise distance between different crops of tracklet i,\n measuring internal consistency. This is O(N*n_ex^2) vs O(N^2*n_ex^2) for the full matrix.\"\"\"\n n_tr = embed.shape[0]\n diag = np.zeros(n_tr)\n for i in range(n_tr):\n e = embed[i] # (n_ex, embed_dim)\n diag[i] = np.mean(np.linalg.norm(e[:, None, :] - e[None, :, :], axis=-1))\n return diag\n\ndef get_largest_cluster(dist_mat, thresh, t_info, pred_map,preds):\n distArray = ssd.squareform( dist_mat)\n Z = linkage(distArray, 'average')\n # plt.figure()\n # dn = dendrogram(Z)\n\n F = fcluster(Z, thresh, criterion='distance')\n\n groups = []\n n_fr = [max(sel[1]) for sel in t_info]\n tr_len = []\n for mov_ndx, trk_ndx in pred_map:\n cur_len = t_info[mov_ndx][1][trk_ndx] - t_info[mov_ndx][0][trk_ndx] + 1\n tr_len.append(cur_len)\n tr_len = np.array(tr_len)\n\n g_len = np.array([tr_len[F==(i+1)].sum() for i in range(max(F))])\n largest_cluster = np.argmax(g_len)\n sel_idx = np.where(F==(largest_cluster+1))[0]\n\n # sort by distance to the mean of the cluster before checking for overlaps\n fq = preds[sel_idx]\n fm = np.mean(fq, axis=0)\n dm = np.linalg.norm(fq - fm[None], axis=-1).mean(axis=-1)\n sel_idx = sel_idx[np.argsort(dm)]\n\n # idx_index_Z = [np.where(Z[:,:2]==ss)[0][0] for ss in sel_idx]\n # sel_idx = sel_idx[np.argsort(idx_index_Z)]\n\n cur_group = []\n extra_groups = []\n ctline = [np.zeros(n) for n in n_fr]\n # ctline keeps track of the frames that are already part of the current group.\n for cc in sel_idx:\n mov_ndx, trk_ndx = pred_map[cc]\n sel_ss = t_info[mov_ndx][0][trk_ndx]\n sel_ee = t_info[mov_ndx][1][trk_ndx]\n prev_overlap = np.sum(ctline[mov_ndx][sel_ss:sel_ee + 1]) / (sel_ee - sel_ss + 1)\n if prev_overlap < 0.05:\n cur_group.append(cc)\n ctline[mov_ndx][sel_ss:sel_ee + 1] += 1\n\n sz = [np.count_nonzero(ct) for ct in ctline]\n return cur_group,sz\n\n\n\ndef cluster_tracklets_id(embed, pred_map, t_info, min_len):\n\n n_tr = embed.shape[0]\n n_ex = embed.shape[1]\n ddm = get_id_dist_mat(embed)\n # ddm = np.ones([n_tr, n_tr]) * np.nan\n #\n # for xx in tqdm(range(n_tr)):\n # ddm[xx, :] = np.median(np.linalg.norm(embed[xx, None, :, None] - embed[:, None, :], axis=-1), axis=(1,2))\n # plt.figure(); plt.imshow(ddm)\n\n\n ddm[range(n_tr), range(n_tr)] = 0.\n distArray = ssd.squareform( ddm)\n Z = linkage(distArray, 'average')\n # plt.figure()\n # dn = dendrogram(Z)\n\n ##\n thres = 1.\n F = fcluster(Z, thres, criterion='distance')\n\n groups = []\n n_fr = [max(sel[1]) for sel in t_info]\n tr_len = []\n for mov_ndx, trk_ndx in pred_map:\n cur_len = t_info[mov_ndx][1][trk_ndx] - t_info[mov_ndx][0][trk_ndx] + 1\n tr_len.append(cur_len)\n tr_len = np.array(tr_len)\n\n g_len = np.array([tr_len[F==(i+1)].sum() for i in range(max(F))])\n f_order = np.argsort(g_len)[::-1]\n\n # Create groups in order of their sizes. While creating groups ensure two tracklets that have significant overlap in time are not part of the same group.\n\n for ndx in f_order:\n cur_gr = np.where(np.array(F) == (ndx + 1))[0]\n cur_gr_ord = np.argsort(-tr_len[cur_gr])\n cur_gr = cur_gr[cur_gr_ord]\n\n cur_group = []\n extra_groups = []\n ctline = [np.zeros(n) for n in n_fr]\n # ctline keeps track of the frames that are already part of the current group.\n for cc in cur_gr:\n mov_ndx, trk_ndx = pred_map[cc]\n sel_ss = t_info[mov_ndx][0][trk_ndx]\n sel_ee = t_info[mov_ndx][1][trk_ndx]\n prev_overlap = np.sum(ctline[mov_ndx][sel_ss:sel_ee+1])/(sel_ee-sel_ss+1)\n if prev_overlap>0.05:\n if (sel_ee-sel_ss+1)>min_len:\n extra_groups.append([cc])\n else:\n cur_group.append(cc)\n ctline[mov_ndx][sel_ss:sel_ee+1] +=1\n\n tot_len = sum([ct.sum() for ct in ctline])\n if tot_len>min_len:\n groups.append(cur_group)\n groups.extend(extra_groups)\n\n return groups\n\n# Graph cut based linking of tracklets\n\ndef get_motion_links(trk, maxcosts,max_ov=50,max_fr_gap=200):\n ss, ee = trk.get_startendframes()\n n_trk = trk.ntargets\n\n m_mat = np.ones([n_trk, n_trk]) * np.inf # motion cost matrix\n tlen = ee - ss + 1\n d_norm = maxcosts[0] * 2\n\n for n in tqdm(range(n_trk)):\n n_close = 0\n sel_m = np.where(~((ss > (ee[n] + max_fr_gap)) | (ee < (ss[n] - max_fr_gap))))[0]\n sel_m = sel_m[sel_m > n]\n for m in sel_m:\n\n overlap = max(0, min(ee[n] + 1, ee[m] + 1) - max(ss[n], ss[m]))\n ov_val = max(overlap / min(tlen[n], tlen[m]), overlap / max_ov)\n n_close = n_close + 1\n if overlap == 0:\n\n if ee[n] < ss[m]:\n before_ndx = n\n after_ndx = m\n frame_gap = ss[m] - ee[n]\n else:\n before_ndx = m\n after_ndx = n\n frame_gap = ss[n] - ee[m]\n\n\n if tlen[before_ndx] > 1 and tlen[after_ndx] > 1:\n pred_before = fwd_motion_estimate(trk.pTrk.data[before_ndx][..., -2:], frame_gap/2)\n pred_after = bwd_motion_estimate(trk.pTrk.data[after_ndx][..., :2], frame_gap/2)\n elif tlen[before_ndx] > 1:\n pred_before = fwd_motion_estimate(trk.pTrk.data[before_ndx][..., -2:],frame_gap)\n pred_after = trk.pTrk.data[after_ndx][..., 0]\n elif tlen[after_ndx] > 1:\n pred_before = trk.pTrk.data[before_ndx][..., 0]\n pred_after = bwd_motion_estimate(trk.pTrk.data[after_ndx][..., :2],frame_gap)\n else:\n pred_before = trk.pTrk.data[before_ndx][..., -1]\n pred_after = trk.pTrk.data[after_ndx][..., 0]\n\n spatial_dist = np.linalg.norm(pred_before - pred_after, axis=-1).mean()\n m_mat[n, m] = spatial_dist / d_norm / frame_gap + (frame_gap - 1) / 5.0\n m_mat[m, n] = m_mat[n, m]\n\n elif ov_val < 0.1:\n # if overlapping, see how close the poses are in the overlapping region. If they are close then give them a low cost to be linked.\n ov_frames = np.arange(max(ss[n], ss[m]), min(ee[n], ee[m]))\n last_pos_n = trk.pTrk.data[n][..., ov_frames - ss[n]]\n first_pos_m = trk.pTrk.data[m][..., ov_frames - ss[m]]\n spatial_dists = np.linalg.norm(last_pos_n - first_pos_m, axis=-1).mean()\n m_mat[n, m] = spatial_dists / d_norm + ov_val * 10.0\n m_mat[m, n] = m_mat[n, m]\n else:\n m_mat[n, m] = np.inf\n m_mat[m, n] = np.inf\n\n # Refine costs using intermediate tracklets: for each pair (a, b), find all tracklets c\n # that lie strictly between a and b temporally. Add 0.5 * min_c(orig_cost(a,c) + orig_cost(c,b))\n # to the original cost. Always uses original (unrefined) costs to avoid propagating updates.\n m_mat_orig = m_mat.copy()\n m_mat_refined = m_mat_orig.copy()\n\n for a in range(n_trk):\n sel_m = np.where(~((ss > (ee[a] + max_fr_gap)) | (ee < (ss[a] - max_fr_gap))))[0]\n sel_m = sel_m[sel_m > a]\n for b in sel_m:\n if not (ee[a] < ss[b]):\n continue\n # tracklets strictly between a and b in time\n between = np.where((ss > ee[a]) & (ee < ss[b]))[0]\n if len(between) == 0:\n continue\n path_costs = m_mat_orig[a, between] + m_mat_orig[between, b]\n min_path = np.nansum(path_costs)\n if np.isfinite(min_path):\n m_mat_refined[a, b] += 0.25 * min_path\n m_mat_refined[b, a] = m_mat_refined[a, b]\n\n m_mat = m_mat_refined\n\n # for each tracklet find the top 10 best matches that come after it and top 10 best matches that come before it. We will only consider these matches for graph cut. This is because considering all matches is very computationally expensive and most of the matches are very bad anyway.\n\n # We also only consider symmetric links. If tracklet A has tracklet B in its top matches and tracklet B has tracklet A in its top matches then we consider the link between A and B for graph cut. This is because if the link is not symmetric then it is likely a bad link and we want to avoid it.\n\n all_fm = [] # all forward matches\n all_rm = [] # all reverse matches\n for n in range(m_mat.shape[0]):\n cur_ord = np.argsort(m_mat[n, :])\n f_sel_ndx = ss[cur_ord] > ee[n]\n f_sel = cur_ord[f_sel_ndx][:10]\n all_fm.append([f_sel, m_mat[n, f_sel]])\n\n r_sel_ndx = ee[cur_ord] < ss[n]\n r_sel = cur_ord[r_sel_ndx][:10]\n all_rm.append([r_sel, m_mat[n, r_sel]])\n\n match_rm = [[] for _ in range(m_mat.shape[0])]\n match_fm = [[] for _ in range(m_mat.shape[0])]\n\n for n in range(m_mat.shape[0]):\n for mf, mfv in zip(*all_fm[n]):\n if n in all_rm[mf][0]:\n match_fm[n].append((mf, mfv))\n\n for mr, mrv in zip(*all_rm[n]):\n if n in all_fm[mr][0]:\n match_rm[n].append((mr, mrv))\n\n for n in range(m_mat.shape[0]):\n for ix, curm in enumerate(match_fm[n]):\n mf, mfv = curm[:]\n for jx, curr in enumerate(match_rm[mf]):\n rf, rfv = curr[:3]\n if rf == n:\n match_fm[n][ix] = (mf, mfv, ix - jx)\n # we also keep track of how well the matches align in their order i.e, ix-jx. We don't want to link tracklets that are very good matches but are very misaligned in their order because that is likely a bad link.\n\n return match_fm, match_rm, m_mat\n\ndef fwd_motion_estimate(last_pos,frame_gap):\n return last_pos[..., -1] + np.diff(last_pos, axis=-1)[..., 0] * frame_gap\n\ndef bwd_motion_estimate(first_pos,frame_gap):\n return first_pos[..., 0] - np.diff(first_pos, axis=-1)[..., 0] * frame_gap\n\n\ndef k_way_graph_cut(unary_costs, pairwise_similarity, n_labels,init_labels=None,label_wt=None):\n \"\"\"\n unary_costs: n x k matrix, cost of assigning node i to label k\n pairwise_similarity: n x n matrix\n n_labels: number of labels/segments\n \"\"\"\n\n from gco import cut_general_graph\n\n n = unary_costs.shape[0]\n\n # Build edge list from similarity matrix\n edges = []\n edge_weights = []\n for i in range(n):\n for j in range(i + 1, n):\n if pairwise_similarity[i, j] != 0:\n edges.append((i, j))\n edge_weights.append(pairwise_similarity[i, j])\n\n edges = np.array(edges, dtype=np.int32)\n edge_weights = np.array(edge_weights, dtype=np.float64)\n\n # Pairwise label cost (Potts model: 0 if same label, 1 if different)\n # This gets multiplied by edge weights\n if label_wt is None:\n label_cost = (1 - np.eye(n_labels)).astype(np.float64)\n else:\n label_cost = label_wt\n\n # Unary must be int32 for pygco, scale up\n unary_int = (unary_costs * 1000).astype(np.int32)\n edge_weights_int = (edge_weights * 1000).astype(np.int32)\n label_cost_int = (label_cost * 1) #.astype(np.int32) # Already 0/1\n\n labels = cut_general_graph(\n edges,\n edge_weights,\n unary_costs,\n label_cost_int,\n #down_weight_factor=10,\n init_labels=init_labels,\n algorithm='expansion' #'expansion' # or 'swap'\n )\n\n return labels\n\n\ndef _tracklet_bboxes(trk):\n \"\"\"Per-frame bounding boxes for every tracklet, walking each tracklet's own\n contiguous data rather than reassembling whole frames.\n Returns (frames, targets, boxes): parallel arrays with one row per\n (frame, target) the target is actually present in, boxes as\n [x_min, x_max, y_min, y_max] padded by 10% of the extent on each side.\"\"\"\n ss, ee = trk.get_startendframes()\n first_frame, last_frame = int(np.min(ss)), int(np.max(ee))\n\n frame_cols, tgt_cols, box_cols = [], [], []\n with warnings.catch_warnings():\n warnings.simplefilter('ignore', RuntimeWarning) # all-NaN slices are expected\n for itgt in range(trk.ntargets):\n data = trk.pTrk.data[itgt]\n if data is None:\n continue\n x_min = np.nanmin(data[:, 0, :], axis=0)\n x_max = np.nanmax(data[:, 0, :], axis=0)\n y_min = np.nanmin(data[:, 1, :], axis=0)\n y_max = np.nanmax(data[:, 1, :], axis=0)\n\n # A frame counts as present iff some landmark has a non-NaN x coordinate.\n present = np.nonzero(~np.isnan(x_min))[0]\n frames = int(ss[itgt]) + present\n keep = (frames >= first_frame) & (frames < last_frame)\n present, frames = present[keep], frames[keep]\n if present.size == 0:\n continue\n\n x_len = x_max[present] - x_min[present]\n y_len = y_max[present] - y_min[present]\n frame_cols.append(frames)\n tgt_cols.append(np.full(frames.size, itgt))\n box_cols.append(np.stack([x_min[present] - x_len * 0.1,\n x_max[present] + x_len * 0.1,\n y_min[present] - y_len * 0.1,\n y_max[present] + y_len * 0.1], axis=1))\n\n if not frame_cols:\n return np.zeros(0, dtype=int), np.zeros(0, dtype=int), np.zeros((0, 4))\n return (np.concatenate(frame_cols), np.concatenate(tgt_cols),\n np.concatenate(box_cols, axis=0))\n\n\ndef _max_pairwise_overlap(boxes):\n \"\"\"Largest overlap of each box with any other box in the same frame,\n as a fraction of the smaller of the two areas.\"\"\"\n int_x_min = np.maximum(boxes[:, 0:1], boxes[:, 0]) # (n, n)\n int_x_max = np.minimum(boxes[:, 1:2], boxes[:, 1])\n int_y_min = np.maximum(boxes[:, 2:3], boxes[:, 2])\n int_y_max = np.minimum(boxes[:, 3:4], boxes[:, 3])\n int_area = np.maximum(0, int_x_max - int_x_min) * np.maximum(0, int_y_max - int_y_min)\n\n areas = (boxes[:, 1] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 2])\n min_area = np.minimum(areas[:, np.newaxis], areas[np.newaxis, :]) # (n, n)\n\n overlaps = np.where(min_area > 0, int_area / min_area, 0.0)\n np.fill_diagonal(overlaps, 0.0) # exclude self-overlap\n return overlaps.max(axis=1)\n\n\ndef get_overlap_value(trk):\n \"\"\"Compute mean per-target pairwise bbox overlap across all frames.\n\n Works tracklet-first: each tracklet's bboxes come from one pass over its own\n contiguous data, and the boxes are then grouped by frame. The obvious\n frame-first version instead calls trk.getframe() per frame, which rebuilds an\n (nlandmarks, d, 1, ntargets) array and loops over every tracklet in Python to\n recover the handful actually present. On a ratcity trk (19k tracklets, 72k\n frames, ~12 targets present per frame) that is 75x slower for identical\n output, and it was the reason this step could run for hours.\"\"\"\n frames, targets, boxes = _tracklet_bboxes(trk)\n\n overlaps = [[] for _ in range(trk.ntargets)]\n if frames.size == 0:\n return np.array([np.nan] * trk.ntargets)\n\n order = np.argsort(frames, kind='stable')\n frames, targets, boxes = frames[order], targets[order], boxes[order]\n\n group_ends = np.searchsorted(frames, np.unique(frames), side='right')\n group_start = 0\n for group_end in group_ends:\n frame_targets = targets[group_start:group_end]\n if frame_targets.size == 1:\n # Nothing to overlap with; matches zeroing the diagonal of a 1x1 matrix.\n overlaps[frame_targets[0]].append(0.0)\n else:\n max_overlap = _max_pairwise_overlap(boxes[group_start:group_end])\n for i, tgt in enumerate(frame_targets):\n overlaps[tgt].append(max_overlap[i])\n group_start = group_end\n\n return np.array([np.mean(o) if o else np.nan for o in overlaps])\n\ndef get_id_cluster_centers(linked_trks,pred_map,preds,dist_diag,close_thresh,occ_thresh=0.2,num_animals=None):\n # compute the centers of the clusters of tracklets that we will use for graph cut. We want to find clusters of tracklets that are close in the embedding space and have low overlap and low occlusion. We will use these clusters to compute the unary component for graph-cut\n\n tlen_sel = np.zeros(len(pred_map))\n oa_sel = np.zeros(len(pred_map))\n occ_sel = np.zeros(len(pred_map))\n\n len_mov = []\n for ndx, trk in tqdm(enumerate(linked_trks), total=len(linked_trks),\n desc='Computing tracklet overlap and occlusion'):\n ss, ee = trk.get_startendframes()\n overlaps = get_overlap_value(trk)\n len_mov.append(max(ee) - min(ss))\n n_trk = trk.ntargets\n tlen = ee - ss + 1\n\n cur_pred_sel = pred_map[:, 0] == ndx\n\n occ_counts = []\n conf_counts = []\n for tndx in range(trk.ntargets):\n occ_frac = np.mean(trk.pTrkTag.data[tndx])\n occ_counts.append(occ_frac)\n conf_counts.append(np.mean(trk.pTrkConf.data[tndx]))\n\n occ_counts = np.array(occ_counts)\n\n tlen_sel[cur_pred_sel] = tlen[pred_map[cur_pred_sel, 1]]\n oa_sel[cur_pred_sel] = overlaps[pred_map[cur_pred_sel, 1]]\n occ_sel[cur_pred_sel] = occ_counts[pred_map[cur_pred_sel, 1]]\n\n\n # Use the pre-computed diagonal (dist_diag) to filter tracklets, then compute the\n # distance matrix only for the selected subset instead of the full N×N matrix.\n t_sel = np.where((tlen_sel > 50) & (oa_sel < 0.4) & (occ_sel < occ_thresh) & (dist_diag < close_thresh))[0]\n\n dist_mat_sub = get_id_dist_mat(preds[t_sel])\n dist_mat_sub = (dist_mat_sub+dist_mat_sub.T)/2 # symmetrize to avoid numerical issues\n np.fill_diagonal(dist_mat_sub, 0)\n distArray = ssd.squareform(dist_mat_sub)\n Z = linkage(distArray, 'average')\n # plt.figure(); dn = dendrogram(Z); xde = plt.xticks(fontsize=8)\n\n if num_animals is not None and num_animals > 0:\n # known number of animals -- pick that many clusters instead of cutting at a fixed distance\n F = fcluster(Z, t=num_animals, criterion='maxclust')\n else:\n F = fcluster(Z, t=1.0, criterion='distance')\n\n g_sz = []\n for rf in range(max(F)):\n gs = np.where(F == (rf + 1))[0]\n cur_sz = 0\n cur_occ = 0\n for gxx in gs:\n cur_sz += tlen_sel[t_sel[gxx]]\n g_sz.append(cur_sz)\n\n g_sz = np.array(g_sz)\n\n cluster_centers = []\n sel_clus = []\n for i in range(1, np.max(F) + 1):\n if num_animals is None and g_sz[i - 1] / sum(len_mov) < 0.05:\n # small cluster, ignore\n continue\n cur_sel = t_sel[np.where(F == i)[0]] # np.where(F==i)[0] #\n curm = np.zeros(preds.shape[2])\n tot_len = 0\n for cc in cur_sel:\n cur_m, cur_t = pred_map[cc, :]\n frac_occ = np.mean(linked_trks[cur_m].pTrkTag.data[cur_t])\n # pTrkTag is occlusion, so frac_occ is the fraction of landmark observations\n # occluded and HIGHER IS WORSE -- see this function's docstring (\"low overlap\n # and low occlusion\") and the t_sel filter above, which admits a tracklet only\n # when occ_sel < occ_thresh. The weight must therefore be (1 - frac_occ):\n # this used to weight by frac_occ itself, inverting the preference. Because\n # t_sel has already capped frac_occ at occ_thresh (0.2), the old multiplier\n # sat in [0, 0.2) -- so a perfectly clean tracklet got ~zero weight while a\n # borderline one just under the threshold dominated its cluster's centre.\n weight = tlen_sel[cc] * (1 - frac_occ)\n curm += preds[cc].mean(axis=0) * weight\n tot_len += weight\n # The two non-normal branches below are defensive only: t_sel admits a tracklet\n # only when occ_sel < occ_thresh and tlen_sel > 50, so every weight here is\n # >= 50*(1-occ_thresh) > 0, and a cluster index i in 1..max(F) always has at\n # least one member. They guard against a future caller raising occ_thresh to\n # 1.0, where fully-occluded members would contribute zero weight and divide by\n # zero, yielding a NaN centre that then propagates silently through the graph cut.\n if tot_len > 0:\n curm /= tot_len\n elif len(cur_sel) > 0:\n logging.warning(f'cluster {i} has no usable weight (total {tot_len}); '\n f'falling back to an unweighted centre')\n curm = np.mean([preds[cc].mean(axis=0) for cc in cur_sel], axis=0)\n else:\n logging.warning(f'cluster {i} is empty; skipping it')\n continue\n cluster_centers.append(curm)\n sel_clus.append([i, np.where(F == i)[0]])\n\n cluster_centers = np.array(cluster_centers)\n\n return cluster_centers\n\n\ndef get_graph_cut_unary(trk, pred_map, preds, cluster_centers, unary_wt, mov_ndx):\n # compute the unary costs for graph cut. The unary cost for assigning a tracklet to a cluster is the average distance of the tracklet's embedding to the cluster center. We also add a constant cost for assigning a tracklet to the \"outlier\" cluster (cluster 0) to avoid assigning everything to the same cluster. The unary cost is also weighted by the fraction of frames in the tracklet that are occluded, so that tracklets that are mostly occluded are more likely to be assigned to the outlier cluster.\n\n ss, ee = trk.get_startendframes()\n n_trk = trk.ntargets\n\n unary_vals = np.zeros((n_trk, len(cluster_centers) + 1))\n base_unary = np.ones(len(cluster_centers) + 1) + 0.1 / unary_wt\n base_unary[0] = 1.\n for n in range(n_trk):\n n_ndx = np.where(np.all(pred_map == [mov_ndx, n], axis=1))[0]\n if n_ndx.size > 0:\n n_ndx = n_ndx[0]\n for cndx, center in enumerate(cluster_centers):\n unary_vals[n, cndx + 1] = np.linalg.norm(preds[n_ndx][:, None] - center[None, None, :], axis=-1).mean()\n unary_vals[n, 0] = 2.\n occ_frac = np.mean(trk.pTrkTag.data[n])\n unary_vals[n, :] = unary_vals[n, :] * (1 - occ_frac) + base_unary * occ_frac\n # unary_vals_low[nndx,:] = unary_vals[nndx,:]*(1-occ_frac) + base_unary_low*occ_frac\n else:\n unary_vals[n, :] = base_unary.copy()\n\n return unary_vals\n\ndef get_graph_cut_pairwise(trk, match_fm):\n n_trk = trk.ntargets\n ss,ee = trk.get_startendframes()\n tlen = ee - ss + 1\n pairwise = np.zeros((n_trk, n_trk))\n done_count = np.zeros(n_trk)\n for n in range(n_trk):\n count = 0\n for curm in match_fm[n]:\n if np.abs(curm[2]) > 4:\n continue\n\n if count >= 10: # ideally this shouldn't happen\n break\n mndx = curm[0]\n if done_count[mndx] >= 20:\n continue\n min_len = min(tlen[n], tlen[mndx])\n len_div = min(1, min_len / 3.0)\n m_val = curm[1] / len_div\n pairwise[n, mndx] = max(0, 4 - m_val)\n pairwise[mndx, n] = pairwise[n, mndx]\n count += 1\n done_count[mndx] += 1\n\n return pairwise\n\ndef get_graph_cut_group(trk, cluster_centers, pred_map, preds, mov_ndx,maxcosts):\n\n n_iters = 5\n unary_wt = 10\n\n ss, ee = trk.get_startendframes()\n tlen = ee - ss + 1\n n_trk = trk.ntargets\n\n unary_vals = get_graph_cut_unary(trk, pred_map, preds, cluster_centers, unary_wt, mov_ndx)\n\n match_fm, match_rm, m_mat = get_motion_links(trk, maxcosts=maxcosts)\n pairwise = get_graph_cut_pairwise(trk, match_fm)\n\n init_labels = np.argmin(unary_vals, axis=1)\n nclusters = cluster_centers.shape[0]\n\n # vv = np.zeros((nclusters + 1, dist_centers.shape[1]))\n # vv[1:, :] = dist_centers.copy()\n # lbl_wt = np.linalg.norm(vv[:, None, :] - vv[None, :, :], axis=-1)\n lbl_wt = None\n\n all_labels = []\n labels = k_way_graph_cut(unary_vals * unary_wt, pairwise, nclusters + 1,\n init_labels=init_labels, label_wt=lbl_wt)\n all_labels.append(labels.copy())\n\n count_neg = np.zeros(n_trk)\n new_labels = labels.copy()\n all_moves = []\n for iter in range(n_iters):\n # print(f\"Iteration {iter}\")\n # print(\"Skiping cluster 0\")\n\n all_ov = []\n moves = []\n\n if iter > 5:\n # special for i==0, where we try to move as many nodes as possible to other clusters\n hh = np.where(labels == 0)[0]\n hh = hh[np.argsort(tlen[hh])[::-1]] # try to move longer tracklets first\n for node_to_change in hh[:200]:\n cluster_order = np.argsort(unary_vals[node_to_change, :])\n for new_cl in cluster_order:\n if new_cl == 0:\n continue\n hh_new = np.where(labels == new_cl)[0]\n has_overlap = False\n for curh in hh_new:\n m = node_to_change\n n = curh\n overlap = max(0, min(ee[m] + 1, ee[n] + 1) - max(ss[m], ss[n]))\n ov_val = overlap / tlen[m]\n if overlap > 0.1:\n has_overlap = True\n break\n if not has_overlap:\n print(\n f\"Moving node {node_to_change} ({tlen[node_to_change]})from cluster {0} to {new_cl}\")\n new_labels[node_to_change] = new_cl\n break\n\n # if overlapping tracklets are in the same cluster, then move one of them to a different cluster. We will move the tracklet that has a higher unary cost for the current cluster, because that tracklet is more likely to belong to a different cluster. We will move it to the cluster that has the lowest unary cost for that tracklet and also has no overlap with it. We will only consider moving tracklets that have less than 20 negative pairwise edges, because if a tracklet has too many negative edges then it is likely a bad tracklet and we don't want to move it.\n for i in range(1, nclusters + 1):\n hh = np.where(labels == i)[0]\n ovv = []\n for curh1 in hh:\n for curh2 in hh:\n if curh1 <= curh2:\n continue\n m = curh1\n n = curh2\n # if m in pred_map_orig[:,1] and n in pred_map_orig[:,1]:\n overlap = max(0, min(ee[m] + 1, ee[n] + 1) - max(ss[m], ss[n]))\n ov_val = overlap / min(tlen[m], tlen[n]) # max(overlap/min(tlen[m],tlen[n]),overlap/max_ov)\n if ov_val > 0.05 and pairwise[curh1, curh2] >= 0:\n ovv.append((curh1, curh2, ov_val, min(tlen[m], tlen[n])))\n\n topk = sorted(ovv, key=lambda x: x[3], reverse=True)\n count = 0\n for curh1, curh2, ovv, tl in topk:\n if (i == 0) and (tlen[curh1] < 2 and tlen[curh2] < 2):\n continue\n if count > 20:\n break\n min_len = min(tlen[curh1], tlen[curh2])\n # if 55 in [curh1,curh2]:\n # print(f\"Removing pair with overlap {ovv} between {curh1} and {curh2}\")\n min_len1 = min(25.0, min_len)\n pairwise[curh1, curh2] = -min(min_len1, ovv * min_len)\n pairwise[curh2, curh1] = -min(min_len1, ovv * min_len)\n count_neg[curh1] += 1\n count_neg[curh2] += 1\n count += 1\n\n if np.mean(trk.pTrkTag.data[curh1]) - np.mean(trk.pTrkTag.data[curh2]) > 0.5:\n node_to_change = curh1\n elif np.mean(trk.pTrkTag.data[curh2]) - np.mean(trk.pTrkTag.data[curh1]) > 0.5:\n node_to_change = curh2\n else:\n if unary_vals[curh1, i] < unary_vals[curh2, i]:\n node_to_change = curh2\n else:\n node_to_change = curh1\n # if i==0:\n # # for cluster 0, move the opposite tracklet\n # node_to_change = curh1+curh2 - node_to_change\n\n # move node_to_change to a cluster that has no overlap with it\n cluster_order = np.argsort(unary_vals[node_to_change, :])\n for new_cl in cluster_order:\n if new_cl == i:\n continue\n hh_new = np.where(labels == new_cl)[0]\n has_overlap = False\n for curh in hh_new:\n m = node_to_change\n n = curh\n overlap = max(0, min(ee[m] + 1, ee[n] + 1) - max(ss[m], ss[n]))\n ov_val = overlap / tlen[m]\n if overlap > 0.1:\n has_overlap = True\n break\n if not has_overlap:\n # print(\n # f\"Moving node {node_to_change} ({tlen[node_to_change]})from cluster {i} to {new_cl} to avoid overlap between {curh1} and {curh2}\")\n new_labels[node_to_change] = new_cl\n moves.append((node_to_change, i, new_cl))\n break\n\n all_ov.append(topk)\n all_moves.append(moves)\n\n labels = k_way_graph_cut(unary_vals * unary_wt, pairwise, nclusters + 1,\n init_labels=init_labels, label_wt=lbl_wt)\n all_labels.append(labels.copy())\n\n return labels\n\n\ndef _best_group_link_cost(trk_idx, group_set, link_costs_mov,tlen,tlen_mov):\n \"\"\"Return the minimum linking cost from trk_idx to/from any other tracklet in group_set.\n Returns np.inf if no connections to group peers exist in link_costs.\"\"\"\n best = np.inf\n\n if tlen[trk_idx]/tlen_mov > 0.8:\n best = 0.0 # if the tracklet is long enough, then we don't need to worry about linking it to other tracklets in the group\n return best\n\n for matches in link_costs_mov[trk_idx]: # [start_matches, end_matches]\n if len(matches) > 0:\n for entry in matches:\n peer = int(entry[0])\n if peer in group_set and peer != trk_idx:\n best = min(best, entry[1]) # entry[1] is the spatial cost\n return best\n\n\ndef save_detected_identities(detected_identities_file, cluster_centers, id_model_file=None, mov_files=None, trk_files=None):\n \"\"\"Save the detected identities (the graph-cut ID cluster centers), along with the ID model,\n movies and detection trk files used to compute them.\"\"\"\n out_dict = {'detected_identities': np.array(cluster_centers),\n 'id_model_file': _normalize_path(id_model_file),\n 'mov_files': _normalize_path_list(mov_files),\n 'trk_files': _normalize_path_list(trk_files),\n 'created': time.strftime('%Y-%m-%d %H:%M:%S')}\n out_dir = os.path.dirname(os.path.abspath(detected_identities_file))\n if out_dir and not os.path.exists(out_dir):\n os.makedirs(out_dir, exist_ok=True)\n with open(detected_identities_file, 'wb') as f:\n pickle.dump(out_dict, f)\n logging.info(f'Saved {len(out_dict[\"detected_identities\"])} detected identities to {detected_identities_file}')\n\n\ndef load_detected_identities(detected_identities_file, id_model_file=None, mov_files=None, trk_files=None):\n \"\"\"Load the detected identities saved by save_detected_identities.\n\n Warns if the ID model (or the movies/detection trk files) used to compute the saved identities\n differ from the ones being used now. Returns (cluster_centers, saved_dict).\"\"\"\n with open(detected_identities_file, 'rb') as f:\n in_dict = pickle.load(f)\n\n if 'detected_identities' not in in_dict:\n raise ValueError(f'{detected_identities_file} does not look like a detected identities file '\n f'(no detected_identities entry)')\n cluster_centers = np.array(in_dict['detected_identities'])\n\n saved_model = in_dict.get('id_model_file', None)\n cur_model = _normalize_path(id_model_file)\n if cur_model is not None and saved_model is not None and saved_model != cur_model:\n logging.warning(f'The ID model being used ({cur_model}) does not match the ID model used to compute the '\n f'identities in {detected_identities_file} ({saved_model}). The saved identities are '\n f'embeddings from the earlier model and are unlikely to be meaningful for the current model.')\n\n for name, cur_files in (('movie', mov_files), ('detection trk', trk_files)):\n saved_files = in_dict.get('mov_files' if name == 'movie' else 'trk_files', None)\n cur_files = _normalize_path_list(cur_files)\n if cur_files is not None and saved_files is not None and set(saved_files) != set(cur_files):\n logging.info(f'The {name} files used to compute the identities in {detected_identities_file} '\n f'({saved_files}) differ from the current {name} files ({cur_files}).')\n\n return cluster_centers, in_dict\n\n\ndef _normalize_path(path):\n \"\"\"Return an absolute, normalized version of path for storage/comparison. None passes through.\"\"\"\n return None if path is None else os.path.normpath(os.path.abspath(path))\n\n\ndef _normalize_path_list(paths):\n \"\"\"Return the normalized version of a list of paths. None passes through.\"\"\"\n return None if paths is None else [_normalize_path(p) for p in paths]\n\n\ndef group_graph_cut(linked_trks,pred_map,preds,dist_diag, close_thresh,maxcosts_all,link_costs_arr,conf=None,num_animals=None,\n detected_identities_file=None,id_model_file=None,mov_files=None,trk_files=None):\n\n occ_thresh = conf.link_id_cluster_occ_thresh if conf is not None else 0.2\n cluster_centers = None\n if detected_identities_file is not None and os.path.exists(detected_identities_file):\n cluster_centers, _ = load_detected_identities(detected_identities_file, id_model_file=id_model_file, mov_files=mov_files, trk_files=trk_files)\n if cluster_centers.ndim != 2 or cluster_centers.shape[1] != preds.shape[2]:\n raise ValueError(f'The identities in {detected_identities_file} have shape {cluster_centers.shape}, which is '\n f'incompatible with the ID embeddings of size {preds.shape[2]}')\n logging.info(f'Using {cluster_centers.shape[0]} identities loaded from {detected_identities_file}')\n if num_animals is not None and num_animals > 0 and cluster_centers.shape[0] != num_animals:\n logging.warning(f'{detected_identities_file} has {cluster_centers.shape[0]} identities, but the number of '\n f'animals was specified as {num_animals}. Using the saved identities.')\n\n if cluster_centers is None:\n cluster_centers = get_id_cluster_centers(linked_trks,pred_map,preds,dist_diag,close_thresh,occ_thresh=occ_thresh,num_animals=num_animals)\n if detected_identities_file is not None:\n save_detected_identities(detected_identities_file, cluster_centers, id_model_file=id_model_file, mov_files=mov_files, trk_files=trk_files)\n\n nclusters = cluster_centers.shape[0]\n\n all_labels = []\n groups = [[] for _ in range(nclusters)]\n\n pred_map_out = []\n for mov_ndx, trk in enumerate(linked_trks):\n labels = get_graph_cut_group(trk, cluster_centers, pred_map, preds, mov_ndx, maxcosts_all[mov_ndx])\n all_labels.append(labels.copy())\n\n ss, ee = linked_trks[mov_ndx].get_startendframes()\n\n if False:\n f, ax = plt.subplots(nclusters + 1, 1, figsize=(10, 30))\n fracs = []\n for i in range(nclusters + 1):\n hh = np.where(labels == i)[0]\n occ1 = np.zeros(max(ee) + 1)\n for h in hh:\n occ1[ss[h]:ee[h] + 1] += 1\n ax[i].plot(occ1)\n ax[i].set_title(f\"Cluster {i}, n={len(hh)} tot:{np.sum(occ1 > 0)} frac:{np.sum(occ1 > 0) / max(ee):.2f}\",\n fontsize=8, pad=-12)\n if i < nclusters:\n ax[i].set_xticks([])\n else:\n ax[i].tick_params(axis='x', labelsize=6)\n fracs.append(np.sum(occ1 > 0) / max(ee))\n f.tight_layout()\n\n tlen = ee - ss + 1\n tlen_mov = max(ee)-min(ss)+1\n no_group = np.where(labels == 0)[0]\n for i in range(1, nclusters + 1):\n hh = np.where(labels == i)[0]\n occ1 = np.zeros(max(ee) + 1)\n for h in hh:\n occ1[ss[h]:ee[h] + 1] += 1\n\n # Sort worst-connected tracklets first so they are dropped first; well-connected\n # ones arrive later and find their frames empty\n # Tiebreaker: longer tracklets come later among equal link scores.\n hh_set = set(hh.tolist())\n link_scores = np.array([_best_group_link_cost(h, hh_set, link_costs_arr[mov_ndx],tlen,tlen_mov) for h in hh])\n hh = hh[np.lexsort((tlen[hh],-link_scores))]\n keep = np.ones(len(hh), dtype=bool)\n\n ndxh = 0\n did_break = False\n for ndxh, curh in enumerate(hh):\n # select only one for overlapping tracklets\n if np.all(occ1 <= 1):\n did_break = True\n break\n m = curh\n n = np.where(occ1[ss[m]:ee[m] + 1] > 1)[0]\n if len(n) / tlen[m] > 0.05:\n print(f\"Overlap found in cluster {i} for tracklet {curh} with length {tlen[m]} and overlap {len(n)} frames\")\n occ1[ss[m]:ee[m] + 1][n] -= 1\n keep[ndxh] = False\n else:\n pred_map_out.append([mov_ndx, curh])\n groups[i-1].append(len(pred_map_out)-1)\n\n if did_break:\n for ndxh2 in range(ndxh, len(hh)):\n pred_map_out.append([mov_ndx, hh[ndxh2]])\n groups[i - 1].append(len(pred_map_out) - 1)\n\n cur_no_group = [curh for curh, kk in zip(hh, keep) if not kk]\n no_group = np.concatenate((no_group, cur_no_group))\n\n\n pred_map_out = np.array(pred_map_out)\n return groups, pred_map_out, all_labels, cluster_centers\n\ndef test_assign_ids_data():\n \"\"\"\n test_assign_ids_data:\n loads data from a trkfile and runs assign_ids, stitch, delete_short, and unique on them\n :return:\n \"\"\"\n \n matplotlib.use('TkAgg')\n plt.ion()\n \n trkfile = '/groups/branson/home/kabram/temp/roian_multi/200918_m170234vocpb_m170234_odor_m170232_f0180322_full_min2.trk.part'\n outtrkfile = '/groups/branson/bransonlab/apt/tmp/200918_m170234vocpb_m170234_odor_m170232_f0180322_full_min2_kbstitched_tracklet.trk'\n \n #trkfile = '/groups/branson/home/kabram/temp/roian_multi/200918_m170234vocpb_m170234_odor_m170232_f0180322_full1.trk.part'\n #outtrkfile = '/groups/branson/bransonlab/apt/tmp/200918_m170234vocpb_m170234_odor_m170232_f0180322_full1_kbstitched_v2.trk'\n \n # parameters\n params = {}\n params['verbose'] = 1\n params['maxframes_missed'] = 10\n params['maxframes_delete'] = 10\n params['maxcost_prctile'] = 95.\n params['maxcost_mult'] = 1.25\n params['maxcost_framesfit'] = 3\n params['maxcost_heuristic'] = 'secondorder'\n nframes_test = np.inf\n \n showanimation = False\n \n trk = TrkFile.Trk(trkfile)\n T = np.minimum(np.inf, trk.T)\n # p should be d x nlandmarks x maxnanimals x T, while pTrk is nlandmarks x d x T x maxnanimals\n # p = np.transpose(trk['pTrk'],(1,0,3,2))\n nframes_test = int(np.minimum(T, nframes_test))\n params['maxcost'] = estimate_maxcost(trk, prctile=params['maxcost_prctile'], mult=params['maxcost_mult'],heuristic=params['maxcost_heuristic'])\n params['maxcost_missed'] = estimate_maxcost_missed(trk, params['maxcost_framesfit'],prctile=params['maxcost_prctile'], mult=params['maxcost_mult'],heuristic=params['maxcost_heuristic'])\n logging.info('maxcost set to %f' % params['maxcost'])\n logging.info('maxcost_missed set to ' + str(params['maxcost_missed']))\n ids, costs = assign_ids(trk, params, T=nframes_test)\n if isinstance(ids, np.ndarray):\n nids_original = np.max(ids)+1\n else:\n _, nids_original = ids.get_min_max_val()\n nids_original = nids_original+1\n \n ids, isdummy = stitch(trk, ids, params)\n ids, ids_short = delete_short(ids, isdummy, params)\n _, ids = ids.unique()\n trk.apply_ids(ids)\n \n # save to file\n trk.save(outtrkfile)\n # TrkFile.save_trk(outtrkfile,newtrk)\n \n plt.figure()\n nids = trk.ntargets\n # nids = newtrk['pTrk'].shape[3]\n logging.info('%d ids in %d frames, removed %d ids' % (nids, nframes_test, nids_original-nids))\n nidsplot = int(np.minimum(nids, np.inf))\n minp, maxp = trk.get_min_max_val()\n minp = np.min(minp)\n maxp = np.max(maxp)\n startframes, endframes = trk.get_startendframes()\n \n hax = []\n for d in range(trk.d):\n hax.append(plt.subplot(1, trk.d, d+1))\n hax[d].set_title('coord %d' % d)\n \n for id in range(nidsplot):\n \n logging.info('Target %d, %d frames (%d to %d)' % (id, endframes[id]-startframes[id]+1, startframes[id], endframes[id]))\n \n ts = np.arange(startframes[id], endframes[id]+1, dtype=int)\n n = ts.size\n p = trk.gettargetframe(id, ts).reshape((trk.nlandmarks, trk.d, n))\n mu = np.nanmean(p, axis=0)\n idxnan = np.where(np.all(np.isnan(mu), axis=0))[0]\n for d in range(trk.d):\n h, = hax[d].plot(ts, mu[d, :], '.-')\n if d == 0:\n color = h.get_color()\n hax[d].plot(ts[0], mu[d, 0], 'o', color=color, mfc=color)\n hax[d].plot(ts[-1], mu[d, -1], 's', color=color, mfc=color)\n if idxnan.size > 0:\n hax[d].plot(ts[idxnan], np.zeros(idxnan.size), 'x', color=color)\n plt.show(block=True)\n \n if showanimation:\n \n colors = mixed_colormap(nids)\n colors[:, :4] *= .75\n plt.figure()\n h = [None, ] * nids\n htrail = [None, ] * nids\n hax = plt.gca()\n hax.set_ylim((minp, maxp))\n hax.set_xlim((minp, maxp))\n traillen = 50\n trail = np.zeros((trk.d, traillen, trk.ntargets))\n trail[:] = np.nan\n plt.show(block=False)\n \n T0 = np.nanmin(startframes)\n for t in range(T0, np.nanmax(endframes)+1):\n p = trk.getframe(t)\n isrealidx = trk.real_idx(p).flatten()\n mu = np.nanmean(p, axis=0).reshape((trk.d, trk.ntargets))\n off = t-T0\n if off < traillen:\n trail[:, off, :] = mu\n else:\n trail = np.append(trail[:, 1:, :], mu.reshape((trk.d, 1, nids)), axis=1)\n for id in range(nids):\n if t > endframes[id] or t < startframes[id]:\n if htrail[id] is not None:\n htrail[id].remove()\n htrail[id] = None\n else:\n if htrail[id] is None:\n htrail[id], = plt.plot(trail[0, :, id], trail[1, :, id], '-', color=colors[id, :] * .5+np.ones(4) * .5)\n else:\n htrail[id].set_data(trail[0, :, id], trail[1, :, id])\n \n for id in np.where(isrealidx)[0]:\n if h[id] is None:\n h[id], = plt.plot(p[:, 0, :, id].flatten(), p[:, 1, :, id].flatten(), '.-', color=colors[id, :])\n else:\n h[id].set_data(p[:, 0, :, id].flatten(), p[:, 1, :, id].flatten())\n for id in np.where(~isrealidx)[0]:\n if h[id] is not None:\n h[id].remove()\n h[id] = None\n plt.pause(.01)\n\n\ndef test_estimate_maxcost():\n \n matplotlib.use('TkAgg')\n plt.ion()\n \n trkfile = '/groups/branson/home/kabram/temp/roian_multi/200918_m170234vocpb_m170234_odor_m170232_f0180322_full1.trk.part'\n \n # parameters\n params = {}\n params['verbose'] = 1\n params['maxframes_missed'] = 10\n params['maxframes_delete'] = 10\n params['maxcost_prctile'] = 95.\n params['maxcost_mult'] = 1.25\n params['maxcost_framesfit'] = 3\n \n trk = TrkFile.Trk(trkfile=trkfile)\n # frames should be consecutive\n # assert np.all(np.diff(trk['pTrkFrm'], axis=1) == 1), 'pTrkFrm should be consecutive frames'\n # p should be d x nlandmarks x maxnanimals x T, while pTrk is nlandmarks x d x T x maxnanimals\n # p = np.transpose(trk['pTrk'], (1, 0, 3, 2))\n \n maxcost0 = estimate_maxcost(trk, prctile=params['maxcost_prctile'], mult=params['maxcost_mult'])\n maxcost1 = estimate_maxcost_missed(trk, params['maxcost_framesfit'],\n prctile=params['maxcost_prctile'], mult=params['maxcost_mult'])\n maxcost = np.append(np.atleast_1d(maxcost0), maxcost1.flatten())\n \n plt.figure()\n plt.plot(np.arange(maxcost.size)+1, maxcost, 'o-')\n plt.show(block=True)\n \ndef test_recognize_ids():\n \n matplotlib.use('tkAgg')\n plt.ion()\n \n # locations of data\n expdir = '/groups/branson/bransonlab/apt/experiments/data/200918_m170234vocpb_m170234_odor_m170232_f0180322_allframes'\n rawtrkfile = os.path.join(expdir,'rawtrk.trk')\n outtrkfile = os.path.join(expdir,'kbstiched_debug.trk')\n\n trxfile = os.path.join(expdir,'trx.mat')\n dell2ellfile = os.path.join(expdir,'perframe/dell2ell.mat')\n moviefile = os.path.join(expdir,'movie.mjpg')\n movieidxfile = os.path.join(expdir,'index.txt')\n\n # landmarks to use for calculating a centroid to compare to motr tracking\n bodylandmarks = np.array([0,1])\n plotlandmarkorder = np.array([0,2,3,0,1])\n\n # max ell2ell distance for motr tracking to be considered close\n distthresh = 10\n # fill holes of size < close_diameter\n close_diameter = 5\n # whether to use a normalized version of idcosts such that total costs sum to 1 for each prediction\n # I think this makes more sense, but it makes setting parameters harder...\n normalize_idcosts = True\n\n # debugging - how many frames to test\n nframes_test = np.inf\n\n # big number, means don't assign here\n BIGCOST = 100000.\n \n # whether to plot an animation of tracking results\n showanimation = False\n\n # whether to plot\n plot_debug_input = False\n \n # parameters for matching\n params = {}\n # printing debugging output\n params['verbose'] = 1\n # weight of the movement cost (weight of j cost is 1)\n if normalize_idcosts:\n params['weight_movement'] = 1./100.\n else:\n params['weight_movement'] = 1.\n # cost of setting a target to not be detected\n params['cost_missing'] = 50.*params['weight_movement']\n # cost of having a prediction that is not assigned to a target\n params['cost_extra'] = 50.*params['weight_movement']\n # if a target is not detected, we use its last know location for matching.\n # if it's been more than maxframes_missed frames since last location, then\n # don't use this past location in assigning ids\n params['maxframes_missed'] = 20\n \n # load in unlinked data\n trk = TrkFile.Trk(trkfile=rawtrkfile)\n if not trk.issparse:\n trk.convert2sparse()\n npts = trk.size[0]\n d = trk.size[1]\n \n # load in motr tracking\n trx = TrkFile.load_trx(trxfile)\n # load in pre-computed dell2ell data\n dell2ell = TrkFile.load_perframedata(dell2ellfile)\n\n # frames tracked by APT\n T0,T1 = trk.get_frame_range()\n T1 = int(np.minimum(T1,T0+nframes_test-1))\n T = T1-T0+1\n ntargets = trk.ntargets\n\n # there are 2 targets, so we only need one of dell2ell\n assert len(trx['x']) == 2\n dell2ell = dell2ell[0][T0-trx['startframes'][0]:T1-trx['startframes'][0]+1]\n isclose = dell2ell <= distthresh\n if close_diameter > 0:\n se_close = np.ones(close_diameter)\n isclose = scipy.ndimage.morphology.binary_closing(isclose,se_close)\n\n # APT issue where same prediction returned twice sometimes -- remove duplicates\n ndup = 0\n for t in range(T0,T1+1):\n x = trk.getframe(t)\n D = np.sum(np.abs(x.reshape((npts*d,1,ntargets))-x.reshape((npts*d,ntargets,1))),axis=0)\n D[np.tril_indices(ntargets,k=0)] = np.inf\n (i,j) = np.where(D<=.1)\n if i.size > 0:\n x[:,:,:,j] = trk.defaultval\n trk.setframe(x,t)\n ndup += 1\n print('%d duplicate values found and removed'%ndup)\n\n if plot_debug_input:\n # plot trx and trk info to make sure they line up in time\n plt.figure()\n t = T0\n p = trk.getframe(t)\n for i in range(trk.ntargets):\n plt.plot(p[:,0,0,i],p[:,1,0,i],'r.')\n if (t >= trx['startframes'][i]) and (t <= trx['endframes'][i]):\n plt.plot(trx['x'][i][t-trx['startframes'][i]],trx['y'][i][t-trx['startframes'][i]],'o')\n ax = plt.gca()\n ax.set_aspect('equal')\n\n # plot dell2ell\n plt.figure()\n plt.plot(np.where(isclose)[0]-trx['startframes'][0]+T0,dell2ell[isclose],'.')\n plt.plot(np.where(~isclose)[0]-trx['startframes'][0]+T0,dell2ell[~isclose],'.')\n plt.legend(['close','far'])\n plt.xlabel('Frame')\n plt.ylabel('ell2ell distance (mm)')\n \n # compute motr-based assignment costs\n idcosts = [None,]*T\n for t in range(T0,T1+1):\n i = t - T0\n pcurr = trk.getframe(t)\n idxreal = trk.real_idx(pcurr)\n pcurr = pcurr[:,:,idxreal]\n npred = pcurr.shape[2]\n if isclose[i]:\n idcosts[i] = np.zeros((ntargets,npred))\n continue\n center = np.reshape(np.mean(pcurr[bodylandmarks,:,:],axis=0),[2,1,npred])\n trxpos = np.zeros((2,ntargets,1))\n trxpos[:] = np.nan\n ntrxcurr = 0\n for j in range(ntargets):\n if (t >= trx['startframes'][j]) and (t <= trx['endframes'][j]):\n trxpos[0, j, 0] = trx['x'][j][t-trx['startframes'][j]]\n trxpos[1, j, 0] = trx['y'][j][t-trx['startframes'][j]]\n ntrxcurr+=1\n D = np.sqrt(np.sum(np.square(center-trxpos),axis=0)) # ntargets x npred\n badidx = np.isnan(D)\n if normalize_idcosts:\n z = np.nansum(D,axis=0)\n z[z<=0.] = 1.\n D = D/z\n D[badidx] = BIGCOST\n idcosts[i] = D\n\n if plot_debug_input:\n maxnpred = trk.ntargets\n y = np.zeros((T,maxnpred))\n y[:] = np.nan\n for t in range(T0,T1+1):\n i = t - T0\n y[i,:idcosts[i].shape[1]] = idcosts[i][0,:]-idcosts[i][1,:]\n \n fig, ax = plt.subplots(3, 1, sharex=True, sharey=False)\n for i in range(maxnpred):\n ax[0].plot(y[:,i],'.',label='Prediction %d'%i)\n \n for i in range(len(trx['x'])):\n t0 = np.maximum(T0,trx['startframes'][i])\n t1 = np.minimum(T1,trx['endframes'][i])\n ax[1].plot(np.arange(t0-T0,t1+1-T0),trx['x'][i][t0-trx['startframes'][i]:t1+1-trx['startframes'][i]],'x',label='Motr target %d'%i)\n ax[2].plot(np.arange(t0-T0,t1+1-T0),trx['y'][i][t0-trx['startframes'][i]:t1+1-trx['startframes'][i]],'x',label='Motr target %d'%i)\n \n for i in range(maxnpred):\n p = trk.gettargetframe(i,np.arange(T0,T1+1,dtype=int))\n center = np.mean(p[bodylandmarks,:,:,:],axis=0)\n ax[1].plot(center[0,:,0],'.',label='Prediction %d'%i)\n ax[2].plot(center[1,:,0],'.',label='Prediction %d'%i)\n \n \n ax[0].title.set_text('j cost difference')\n ax[1].title.set_text('x-coordinate')\n ax[2].title.set_text('y-coordinate')\n ax[0].legend(loc='upper right')\n ax[1].legend(loc='upper right')\n ax[2].legend(loc='upper right')\n \n # compute id assignment\n nframes_test = int(np.minimum(T, nframes_test))\n ids, costs, stats = assign_recognize_ids(trk, idcosts, params, T=nframes_test)\n \n # apply to tracking\n trk.apply_ids(ids)\n\n # save linked tracking to output\n trk.save(outtrkfile)\n\n # plot some visualization of the results\n idxextra = np.where(stats['nextra']>0)[0]\n idxmissing = np.where(stats['nmissing']>0)[0]\n idxboth = np.where(np.logical_and(stats['nmissing']>0,stats['nextra']>0))[0]\n \n trk0 = TrkFile.Trk(trkfile=rawtrkfile)\n if not trk0.issparse:\n trk0.convert2sparse()\n \n fig, ax = plt.subplots(trk.ntargets+1, 1, sharex=True, sharey=False)\n ax[0].plot(costs,'.',label='Total cost',zorder=10)\n ax[0].plot(stats['cost_movement'],'.',label='Movement cost',zorder=20)\n ax[0].plot(np.where(~isclose)[0],stats['cost_id'][~isclose],'.',label='Id cost, not close',zorder=30)\n ax[0].title.set_text('Cost')\n\n for i in range(trk0.ntargets):\n t0 = np.maximum(T0,trx['startframes'][i])\n t1 = np.minimum(T1,trx['endframes'][i])\n p0 = trk0.gettargetframe(i,np.arange(t0,t1+1,dtype=int))\n center0 = np.mean(p0[bodylandmarks,:,:,:],axis=0)\n ax[1].plot(center0[0,:,0],'o',label='Raw %d'%i,zorder=5)\n ax[2].plot(center0[1,:,0],'o',label='Raw %d'%i,zorder=5)\n \n for i in range(trk.ntargets):\n t0 = np.maximum(T0,trx['startframes'][i])\n t1 = np.minimum(T1,trx['endframes'][i])\n p = trk.gettargetframe(i,np.arange(t0,t1+1,dtype=int))\n center = np.mean(p[bodylandmarks,:,:,:],axis=0)\n trxx = trx['x'][i][t0-trx['startframes'][i]:t1+1-trx['startframes'][i]]\n trxy = trx['y'][i][t0-trx['startframes'][i]:t1+1-trx['startframes'][i]]\n if T <= 2000: #plotting slow\n ax[1].plot(np.tile(np.arange(t0-T0,t1+1-T0).reshape(1,t1-t0+1),(trk.ntargets,1)),np.concatenate((trxx.reshape((1,t1-t0+1)),center[0,:,:].reshape(1,t1-t0+1)),axis=0),'k.-')\n ax[2].plot(np.tile(np.arange(t0-T0,t1+1-T0).reshape(1,t1-t0+1),(trk.ntargets,1)),np.concatenate((trxy.reshape((1,t1-t0+1)),center[1,:,:].reshape(1,t1-t0+1)),axis=0),'k.-')\n ax[1].plot(np.arange(t0-T0,t1+1-T0),trxx,'+-',label='Motr %d'%i,zorder=10)\n ax[2].plot(np.arange(t0-T0,t1+1-T0),trxy,'+-',label='Motr %d'%i,zorder=10)\n\n ax[1].plot(center[0,:,0],'.-',label='Linked %d'%i,zorder=20)\n ax[2].plot(center[1,:,0],'.-',label='Linked %d'%i,zorder=20)\n \n ax[1].title.set_text('x-coordinate')\n ax[2].title.set_text('y-coordinate')\n for i in range(3):\n box = ax[i].get_position()\n ax[i].set_position([box.x0, box.y0, box.width * 0.8, box.height])\n # Put a legend to the right of the current axis\n #if i > 0:\n ax[i].legend(loc='center left', bbox_to_anchor=(1, 0.5))\n ylim = np.array(ax[i].get_ylim()).reshape((2,1))\n ax[i].plot(np.tile(idxextra,(2,1)),np.tile(ylim,(1,idxextra.size)),'c-',zorder=0)\n ax[i].plot(np.tile(idxmissing,(2,1)),np.tile(ylim,(1,idxmissing.size)),'m-',zorder=0)\n ax[i].plot(np.tile(idxboth,(2,1)),np.tile(ylim,(1,idxboth.size)),'k-',zorder=0)\n ax[i].set_ylim(ylim)\n\n \n plt.show(block=True)\n if showanimation:\n minp, maxp = trk.get_min_max_val()\n minp = np.min(minp)\n maxp = np.max(maxp)\n \n colors = mixed_colormap(ntargets)\n colors[:, :4] *= .75\n plt.figure()\n h = [None, ] * ntargets\n htrail = [None, ] * ntargets\n hax = plt.gca()\n hax.set_ylim((minp, maxp))\n hax.set_xlim((minp, maxp))\n traillen = 50\n trail = np.zeros((trk.d, traillen, trk.ntargets))\n trail[:] = np.nan\n sf,ef = trk.get_startendframes()\n \n for t in range(T0, T1+1):\n p = trk.getframe(t)\n isrealidx = trk.real_idx(p).flatten()\n mu = np.nanmean(p, axis=0).reshape((trk.d, trk.ntargets))\n off = t-T0\n if off < traillen:\n trail[:, off, :] = mu\n else:\n trail = np.append(trail[:, 1:, :], mu.reshape((trk.d, 1, ntargets)), axis=1)\n for j in range(ntargets):\n if t > ef[j] or t < sf[j]:\n if htrail[j] is not None:\n htrail[j].remove()\n htrail[j] = None\n else:\n if htrail[j] is None:\n htrail[j], = plt.plot(trail[0, :, j], trail[1, :, j], '-', color=colors[j, :] * .5+np.ones(4) * .5)\n else:\n htrail[j].set_data(trail[0, :, j], trail[1, :, j])\n \n for j in np.where(isrealidx)[0]:\n if h[j] is None:\n h[j], = plt.plot(p[plotlandmarkorder, 0, :, j].flatten(), p[plotlandmarkorder, 1, :, j].flatten(), '.-', color=colors[j, :])\n else:\n h[j].set_data(p[plotlandmarkorder, 0, :, j].flatten(), p[plotlandmarkorder, 1, :, j].flatten())\n for j in np.where(~isrealidx)[0]:\n if h[j] is not None:\n h[j].remove()\n h[j] = None\n hax.title.set_text('Frame %d'%t)\n plt.pause(.001)\n \n \n print('finished')\n \n\ndef generate_id_report(linked_trk, trx, out_trk_file, mov_file, conf, n_ids=None,n_ex=25, seed=42):\n \"\"\"\n Generate a PDF ID-tracking report for one movie.\n\n Each page covers one trajectory:\n - Detection density histogram overlaid on a background frame\n - Frame-coverage bar chart with sampled-frame markers\n - Grid of n_ex example cropped instances sorted by frame number,\n with pose landmarks overlaid (green = visible, red = occluded)\n and frame number shown above each image\n \"\"\"\n import math\n import multiResData\n from matplotlib.backends.backend_pdf import PdfPages\n import matplotlib.gridspec as gridspec\n\n out_pdf = os.path.splitext(out_trk_file)[0] + '_id_report.pdf'\n\n ntargets = linked_trk.ntargets\n nlandmarks = linked_trk.nlandmarks\n has_tag = linked_trk.pTrkTag is not None\n if n_ids is None:\n n_ids = ntargets\n else:\n n_ids = min(n_ids, ntargets)\n print(f'Generating ID report for {out_trk_file}: '\n f'{ntargets} targets, {nlandmarks} landmarks (reporting first {n_ids})')\n\n start_frames, end_frames = linked_trk.get_startendframes()\n\n all_t_info = list(zip(np.arange(ntargets), start_frames, end_frames))\n valid_t_info = [(int(idx), int(ss), int(ee))\n for idx, ss, ee in all_t_info if idx < n_ids and ss >= 0 and ee >= ss]\n\n print(f' Reading {n_ex} example images for {len(valid_t_info)} trajectories ...')\n ims_out = read_tracklet_ims([trx, valid_t_info, mov_file, conf, n_ex, seed])\n # ims_out[i] = [ims_array, tgt_idx, ss, ee, frame_list] ims_array: (n_ex, H, W, C)\n ims_map = {entry[1]: entry for entry in ims_out}\n\n # Background frame from the middle of the video\n mid_frame = linked_trk.T // 2 + linked_trk.T0\n cap_bg = cv2.VideoCapture(mov_file)\n cap_bg.set(cv2.CAP_PROP_POS_FRAMES, mid_frame)\n ret_bg, bg_frame_bgr = cap_bg.read()\n cap_bg.release()\n bg_frame = cv2.cvtColor(bg_frame_bgr, cv2.COLOR_BGR2RGB) if ret_bg else None\n\n # ------------------------------------------------------------------ #\n # Helper: all landmark detections for one target (for density plot)\n # ------------------------------------------------------------------ #\n def _get_detections(itgt):\n sf = linked_trk.startframes[itgt]\n ef = linked_trk.endframes[itgt]\n if sf < 0 or ef < sf:\n empty = np.zeros((nlandmarks, 0))\n return empty, empty, np.zeros((nlandmarks, 0), dtype=bool)\n if linked_trk.issparse:\n tgt_data = linked_trk.pTrk.gettarget(itgt)\n else:\n sf0 = sf - linked_trk.T0\n ef0 = ef - linked_trk.T0 + 1\n tgt_data = linked_trk.pTrk[:, :, sf0:ef0, itgt]\n xs = tgt_data[:, 0, :]\n ys = tgt_data[:, 1, :]\n valid = ~(np.isnan(xs) | np.isnan(ys))\n return xs, ys, valid\n\n # ------------------------------------------------------------------ #\n # Helper: frame-coverage segments for one target\n # ------------------------------------------------------------------ #\n def _coverage_segments(itgt):\n xs_c, _, valid_c = _get_detections(itgt)\n sf_c = linked_trk.startframes[itgt]\n frame_has_det = np.any(valid_c, axis=0)\n valid_frame_indices = np.where(frame_has_det)[0] + sf_c\n if len(valid_frame_indices) == 0:\n return []\n segs = []\n seg_start = int(valid_frame_indices[0])\n prev_fr = seg_start\n for fr in valid_frame_indices[1:]:\n fr = int(fr)\n if fr > prev_fr + 1:\n segs.append((seg_start, prev_fr - seg_start + 1))\n seg_start = fr\n prev_fr = fr\n segs.append((seg_start, prev_fr - seg_start + 1))\n return segs\n\n # ------------------------------------------------------------------ #\n # Helper: landmarks + occlusion at a single frame for one target\n # ------------------------------------------------------------------ #\n def _get_frame_data(itgt, fr):\n \"\"\"Return (locs_xy (n_lm, 2) in world 0-indexed coords, occ bool or None).\"\"\"\n locs = linked_trk.pTrk.gettargetframe(itgt, fr) # (n_lm, 2, 1, 1)\n locs_xy = locs[:, :, 0, 0] # (n_lm, 2)\n occ_val = None\n if has_tag:\n try:\n occ_data = linked_trk.pTrkTag.gettargetframe(itgt, fr) # (1, 1)\n occ_val = bool(occ_data.flat[0])\n except Exception:\n pass\n return locs_xy, occ_val\n\n # ------------------------------------------------------------------ #\n # Helper: transform world landmarks into crop-image coordinates.\n # Replicates the affine transform used in multiResData.crop_patch_trx.\n # trx_i['x'] / ['y'] are stored 1-indexed (Matlab convention from\n # get_trx_info), so we subtract 1 to get 0-indexed crop-centre coords.\n # ------------------------------------------------------------------ #\n def _locs_to_crop(locs_xy, itgt, fr):\n sf = linked_trk.startframes[itgt]\n fi = int(fr - sf)\n trx_i = trx[itgt]\n if trx_i is None:\n return None\n cx = float(trx_i['x'][0, fi]) - 1.0 # 0-indexed\n cy = float(trx_i['y'][0, fi]) - 1.0\n theta_orig = float(trx_i['theta'][0, fi])\n a_val = float(trx_i['a'][0, fi])\n theta_crop = theta_orig + math.pi / 2 # matches crop_patch_trx\n\n psz_x = conf.imsz[1]\n psz_y = conf.imsz[0]\n\n if getattr(conf, 'multi_scale_by_bbox', False):\n dx = abs(a_val * 4 * math.sin(theta_orig))\n dy = abs(a_val * 4 * math.cos(theta_orig))\n bbox = [cx - dx, cy - dy, cx + dx, cy + dy]\n scale = multiResData.get_scale_bbox(bbox, [psz_x, psz_y]) / conf.multi_pad\n else:\n scale = 1.0\n\n T_mat = np.array([[1, 0, 0],\n [0, 1, 0],\n [-cx + psz_x / 2 - 0.5, -cy + psz_y / 2 - 0.5, 1]],\n dtype=np.float64)\n if getattr(conf, 'trx_align_theta', True):\n R1 = cv2.getRotationMatrix2D((psz_x / 2 - 0.5, psz_y / 2 - 0.5),\n theta_crop * 180.0 / math.pi, scale)\n else:\n R1 = cv2.getRotationMatrix2D((psz_x / 2 - 0.5, psz_y / 2 - 0.5), 0.0, scale)\n R = np.eye(3)\n R[:, :2] = R1.T\n A = (T_mat @ R)[:, :2].T.astype(np.float32)\n\n valid = ~(np.isnan(locs_xy[:, 0]) | np.isnan(locs_xy[:, 1]))\n result = np.full((nlandmarks, 2), np.nan)\n if np.any(valid):\n pts = locs_xy[valid].reshape(-1, 1, 2).astype(np.float32)\n result[valid] = cv2.transform(pts, A).reshape(-1, 2)\n return result\n\n # ------------------------------------------------------------------ #\n # Figure geometry\n # ------------------------------------------------------------------ #\n dpi = 100\n im_h_px = conf.imsz[0]\n im_w_px = conf.imsz[1]\n fig_w_in = 20.0\n ims_per_row = max(1, int(fig_w_in * dpi / im_w_px))\n n_im_rows = int(np.ceil(n_ex / ims_per_row))\n im_col_w_in = fig_w_in / ims_per_row\n im_row_h_in = im_col_w_in * (im_h_px / im_w_px)\n total_im_h_in = im_row_h_in * n_im_rows\n analysis_h_in = 3.0\n\n title_name = os.path.splitext(os.path.basename(out_trk_file))[0]\n\n if bg_frame is not None:\n frame_w_px = bg_frame.shape[1]\n frame_h_px = bg_frame.shape[0]\n else:\n frame_w_px, frame_h_px = 800, 600\n\n tgt_colors = cm.tab10(np.linspace(0, 1, max(ntargets, 1), endpoint=False))\n\n # ------------------------------------------------------------------ #\n # One PDF page per trajectory\n # ------------------------------------------------------------------ #\n with PdfPages(out_pdf) as pdf:\n for itgt in range(n_ids):\n fig = plt.figure(figsize=(fig_w_in, analysis_h_in + total_im_h_in), dpi=dpi)\n outer_gs = gridspec.GridSpec(\n 2, 1, figure=fig,\n height_ratios=[analysis_h_in, total_im_h_in],\n hspace=0.3,\n )\n fig.suptitle(\n f'{title_name} — Trajectory {itgt} '\n f'(frames {int(start_frames[itgt])}–{int(end_frames[itgt])})',\n fontsize=11,\n )\n\n # --- Row 0: density + coverage ---\n top_gs = gridspec.GridSpecFromSubplotSpec(1, 2, subplot_spec=outer_gs[0], wspace=0.3)\n ax_dn = fig.add_subplot(top_gs[0])\n ax_cov = fig.add_subplot(top_gs[1])\n\n xs_d, ys_d, valid_d = _get_detections(itgt)\n all_x = xs_d[valid_d].flatten()\n all_y = ys_d[valid_d].flatten()\n if len(all_x) > 0:\n if bg_frame is not None:\n ax_dn.imshow(bg_frame, alpha=0.4)\n ax_dn.his"}}},{"rowIdx":570453,"cells":{"sha256":{"kind":"string","value":"cd9122470d799b730d009cc51884d2c66a9976b06fffe000d92cf90f57e535c7"},"language":{"kind":"string","value":"Python"},"size":{"kind":"number","value":200041,"string":"200,041"},"lines":{"kind":"number","value":4488,"string":"4,488"},"content":{"kind":"string","value":"import os\nimport sys\n\n# set QT_API environment variable\nos.environ[\"QT_API\"] = \"pyqt5\"\n\n# qt libraries\nimport qtpy\nfrom qtpy.QtCore import *\nfrom qtpy.QtWidgets import *\nfrom qtpy.QtGui import *\n\nimport pyqtgraph as pg\nimport pandas as pd\nimport napari\nfrom napari.utils.colormaps import Colormap, AVAILABLE_COLORMAPS\nimport re\nimport cv2\nimport math\nimport locale\nimport time\nfrom datetime import datetime\nimport itertools\nimport numpy as np\nfrom scipy.spatial import Delaunay\nimport shutil\nfrom control._def import *\nfrom PIL import Image, ImageDraw, ImageFont\n\n\nclass WrapperWindow(QMainWindow):\n def __init__(self, content_widget, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.setCentralWidget(content_widget)\n self.hide()\n\n def closeEvent(self, event):\n self.hide()\n event.ignore()\n\n def closeForReal(self, event):\n super().closeEvent(event)\n\n\nclass CollapsibleGroupBox(QGroupBox):\n def __init__(self, title):\n super(CollapsibleGroupBox,self).__init__(title)\n self.setCheckable(True)\n self.setChecked(True)\n self.higher_layout = QVBoxLayout()\n self.content = QVBoxLayout()\n #self.content.setAlignment(Qt.AlignTop)\n self.content_widget = QWidget()\n self.content_widget.setLayout(self.content)\n self.higher_layout.addWidget(self.content_widget)\n self.setLayout(self.higher_layout)\n self.toggled.connect(self.toggle_content)\n\n def toggle_content(self,state):\n self.content_widget.setVisible(state)\n\n\nclass ConfigEditorForAcquisitions(QDialog):\n def __init__(self, configManager, only_z_offset=True):\n super().__init__()\n\n self.config = configManager\n\n self.only_z_offset=only_z_offset\n\n self.scroll_area = QScrollArea()\n self.scroll_area.setWidgetResizable(True)\n self.scroll_area_widget = QWidget()\n self.scroll_area_layout = QVBoxLayout()\n self.scroll_area_widget.setLayout(self.scroll_area_layout)\n self.scroll_area.setWidget(self.scroll_area_widget)\n\n self.save_config_button = QPushButton(\"Save Config\")\n self.save_config_button.clicked.connect(self.save_config)\n self.save_to_file_button = QPushButton(\"Save to File\")\n self.save_to_file_button.clicked.connect(self.save_to_file)\n self.load_config_button = QPushButton(\"Load Config from File\")\n self.load_config_button.clicked.connect(lambda: self.load_config_from_file(None))\n\n layout = QVBoxLayout()\n layout.addWidget(self.scroll_area)\n layout.addWidget(self.save_config_button)\n layout.addWidget(self.save_to_file_button)\n layout.addWidget(self.load_config_button)\n\n self.config_value_widgets = {}\n\n self.setLayout(layout)\n self.setWindowTitle(\"Configuration Editor\")\n self.init_ui(only_z_offset)\n\n def init_ui(self, only_z_offset=None):\n if only_z_offset is None:\n only_z_offset = self.only_z_offset\n self.groups = {}\n for section in self.config.configurations:\n if not only_z_offset:\n group_box = CollapsibleGroupBox(section.name)\n else:\n group_box = QGroupBox(section.name)\n\n group_layout = QVBoxLayout()\n\n section_value_widgets = {}\n\n self.groups[str(section.id)] = group_box\n\n for option in section.__dict__.keys():\n if option.startswith('_') and option.endswith('_options'):\n continue\n if option == 'id':\n continue\n if only_z_offset and option != 'z_offset':\n continue\n option_value = str(getattr(section, option))\n option_name = QLabel(option)\n option_layout = QHBoxLayout()\n option_layout.addWidget(option_name)\n if f'_{option}_options' in list(section.__dict__.keys()):\n option_value_list = getattr(section,f'_{option}_options')\n values = option_value_list.strip('[]').split(',')\n for i in range(len(values)):\n values[i] = values[i].strip()\n if option_value not in values:\n values.append(option_value)\n combo_box = QComboBox()\n combo_box.addItems(values)\n combo_box.setCurrentText(option_value)\n option_layout.addWidget(combo_box)\n section_value_widgets[option] = combo_box\n else:\n option_input = QLineEdit(option_value)\n option_layout.addWidget(option_input)\n section_value_widgets[option] = option_input\n group_layout.addLayout(option_layout)\n\n self.config_value_widgets[str(section.id)] = section_value_widgets\n if not only_z_offset:\n group_box.content.addLayout(group_layout)\n else:\n group_box.setLayout(group_layout)\n\n self.scroll_area_layout.addWidget(group_box)\n\n def save_config(self):\n for section in self.config.configurations:\n for option in section.__dict__.keys():\n if option.startswith(\"_\") and option.endswith(\"_options\"):\n continue\n old_val = getattr(section,option)\n if option == 'id':\n continue\n elif option == 'camera_sn':\n option_name_in_xml = 'CameraSN'\n else:\n option_name_in_xml = option.replace(\"_\",\" \").title().replace(\" \",\"\")\n try:\n widget = self.config_value_widgets[str(section.id)][option]\n except KeyError:\n continue\n if type(widget) is QLineEdit:\n self.config.update_configuration(section.id, option_name_in_xml, widget.text())\n else:\n self.config.update_configuration(section.id, option_name_in_xml, widget.currentText())\n self.config.configurations = []\n self.config.read_configurations()\n\n def save_to_file(self):\n self.save_config()\n file_path, _ = QFileDialog.getSaveFileName(self, \"Save Acquisition Config File\", '', \"XML Files (*.xml);;All Files (*)\")\n if file_path:\n self.config.write_configuration(file_path)\n\n def load_config_from_file(self,only_z_offset=None):\n file_path, _ = QFileDialog.getOpenFileName(self, \"Load Acquisition Config File\", '', \"XML Files (*.xml);;All Files (*)\")\n if file_path:\n self.config.config_filename = file_path\n self.config.configurations = []\n self.config.read_configurations()\n # Clear and re-initialize the UI\n self.scroll_area_widget.deleteLater()\n self.scroll_area_widget = QWidget()\n self.scroll_area_layout = QVBoxLayout()\n self.scroll_area_widget.setLayout(self.scroll_area_layout)\n self.scroll_area.setWidget(self.scroll_area_widget)\n self.init_ui(only_z_offset)\n\n\nclass ConfigEditor(QDialog):\n def __init__(self, config):\n super().__init__()\n\n self.config = config\n\n self.scroll_area = QScrollArea()\n self.scroll_area.setWidgetResizable(True)\n self.scroll_area_widget = QWidget()\n self.scroll_area_layout = QVBoxLayout()\n self.scroll_area_widget.setLayout(self.scroll_area_layout)\n self.scroll_area.setWidget(self.scroll_area_widget)\n\n self.save_config_button = QPushButton(\"Save Config\")\n self.save_config_button.clicked.connect(self.save_config)\n self.save_to_file_button = QPushButton(\"Save to File\")\n self.save_to_file_button.clicked.connect(self.save_to_file)\n self.load_config_button = QPushButton(\"Load Config from File\")\n self.load_config_button.clicked.connect(self.load_config_from_file)\n\n layout = QVBoxLayout()\n layout.addWidget(self.scroll_area)\n layout.addWidget(self.save_config_button)\n layout.addWidget(self.save_to_file_button)\n layout.addWidget(self.load_config_button)\n\n self.config_value_widgets = {}\n\n self.setLayout(layout)\n self.setWindowTitle(\"Configuration Editor\")\n self.init_ui()\n\n def init_ui(self):\n self.groups = {}\n for section in self.config.sections():\n group_box = CollapsibleGroupBox(section)\n group_layout = QVBoxLayout()\n\n section_value_widgets = {}\n\n self.groups[section] = group_box\n\n for option in self.config.options(section):\n if option.startswith('_') and option.endswith('_options'):\n continue\n option_value = self.config.get(section, option)\n option_name = QLabel(option)\n option_layout = QHBoxLayout()\n option_layout.addWidget(option_name)\n if f'_{option}_options' in self.config.options(section):\n option_value_list = self.config.get(section,f'_{option}_options')\n values = option_value_list.strip('[]').split(',')\n for i in range(len(values)):\n values[i] = values[i].strip()\n if option_value not in values:\n values.append(option_value)\n combo_box = QComboBox()\n combo_box.addItems(values)\n combo_box.setCurrentText(option_value)\n option_layout.addWidget(combo_box)\n section_value_widgets[option] = combo_box\n else:\n option_input = QLineEdit(option_value)\n option_layout.addWidget(option_input)\n section_value_widgets[option] = option_input\n group_layout.addLayout(option_layout)\n\n self.config_value_widgets[section] = section_value_widgets\n group_box.content.addLayout(group_layout)\n self.scroll_area_layout.addWidget(group_box)\n\n def save_config(self):\n for section in self.config.sections():\n for option in self.config.options(section):\n if option.startswith(\"_\") and option.endswith(\"_options\"):\n continue\n old_val = self.config.get(section, option)\n widget = self.config_value_widgets[section][option]\n if type(widget) is QLineEdit:\n self.config.set(section, option, widget.text())\n else:\n self.config.set(section, option, widget.currentText())\n if old_val != self.config.get(section,option):\n print(self.config.get(section,option))\n\n def save_to_file(self):\n self.save_config()\n file_path, _ = QFileDialog.getSaveFileName(self, \"Save Config File\", '', \"INI Files (*.ini);;All Files (*)\")\n if file_path:\n with open(file_path, 'w') as configfile:\n self.config.write(configfile)\n\n def load_config_from_file(self):\n file_path, _ = QFileDialog.getOpenFileName(self, \"Load Config File\", '', \"INI Files (*.ini);;All Files (*)\")\n if file_path:\n self.config.read(file_path)\n # Clear and re-initialize the UI\n self.scroll_area_widget.deleteLater()\n self.scroll_area_widget = QWidget()\n self.scroll_area_layout = QVBoxLayout()\n self.scroll_area_widget.setLayout(self.scroll_area_layout)\n self.scroll_area.setWidget(self.scroll_area_widget)\n self.init_ui()\n\n\nclass ConfigEditorBackwardsCompatible(ConfigEditor):\n def __init__(self, config, original_filepath, main_window):\n super().__init__(config)\n self.original_filepath = original_filepath\n self.main_window = main_window\n\n self.apply_exit_button = QPushButton(\"Apply and Exit\")\n self.apply_exit_button.clicked.connect(self.apply_and_exit)\n\n self.layout().addWidget(self.apply_exit_button)\n\n def apply_and_exit(self):\n self.save_config()\n with open(self.original_filepath, 'w') as configfile:\n self.config.write(configfile)\n try:\n self.main_window.close()\n except:\n pass\n self.close()\n\n\nclass SpinningDiskConfocalWidget(QWidget):\n def __init__(self, xlight, config_manager=None):\n super(SpinningDiskConfocalWidget,self).__init__()\n\n self.config_manager = config_manager\n\n self.xlight = xlight\n\n self.init_ui()\n\n self.dropdown_emission_filter.setCurrentText(str(self.xlight.get_emission_filter()))\n self.dropdown_dichroic.setCurrentText(str(self.xlight.get_dichroic()))\n\n self.dropdown_emission_filter.currentIndexChanged.connect(self.set_emission_filter)\n self.dropdown_dichroic.currentIndexChanged.connect(self.set_dichroic)\n\n self.disk_position_state = self.xlight.get_disk_position()\n\n if self.disk_position_state == 1:\n self.btn_toggle_widefield.setText(\"Switch to Widefield\")\n\n if self.config_manager is not None:\n if self.disk_position_state == 1:\n self.config_manager.config_filename = \"confocal_configurations.xml\"\n else:\n self.config_manager.config_filename = \"widefield_configurations.xml\"\n self.config_manager.configurations = []\n self.config_manager.read_configurations()\n\n self.btn_toggle_widefield.clicked.connect(self.toggle_disk_position)\n self.btn_toggle_motor.clicked.connect(self.toggle_motor)\n\n self.slider_illumination_iris.valueChanged.connect(self.update_illumination_iris)\n self.spinbox_illumination_iris.valueChanged.connect(self.update_illumination_iris)\n self.slider_emission_iris.valueChanged.connect(self.update_emission_iris)\n self.spinbox_emission_iris.valueChanged.connect(self.update_emission_iris)\n self.dropdown_filter_slider.valueChanged.connect(self.set_filter_slider)\n\n def init_ui(self):\n\n emissionFilterLayout = QHBoxLayout()\n emissionFilterLayout.addWidget(QLabel(\"Emission Position\"))\n self.dropdown_emission_filter = QComboBox(self)\n self.dropdown_emission_filter.addItems([str(i+1) for i in range(8)])\n emissionFilterLayout.addWidget(self.dropdown_emission_filter)\n\n dichroicLayout = QHBoxLayout()\n dichroicLayout.addWidget(QLabel(\"Dichroic Position\"))\n self.dropdown_dichroic = QComboBox(self)\n self.dropdown_dichroic.addItems([str(i+1) for i in range(5)])\n dichroicLayout.addWidget(self.dropdown_dichroic)\n\n illuminationIrisLayout = QHBoxLayout()\n illuminationIrisLayout.addWidget(QLabel(\"Illumination Iris\"))\n self.slider_illumination_iris = QSlider(Qt.Horizontal)\n self.slider_illumination_iris.setRange(0, 100)\n self.spinbox_illumination_iris = QSpinBox()\n self.spinbox_illumination_iris.setRange(0, 100)\n self.spinbox_illumination_iris.setKeyboardTracking(False)\n illuminationIrisLayout.addWidget(self.slider_illumination_iris)\n illuminationIrisLayout.addWidget(self.spinbox_illumination_iris)\n\n emissionIrisLayout = QHBoxLayout()\n emissionIrisLayout.addWidget(QLabel(\"Emission Iris\"))\n self.slider_emission_iris = QSlider(Qt.Horizontal)\n self.slider_emission_iris.setRange(0, 100)\n self.spinbox_emission_iris = QSpinBox()\n self.spinbox_emission_iris.setRange(0, 100)\n self.spinbox_emission_iris.setKeyboardTracking(False)\n emissionIrisLayout.addWidget(self.slider_emission_iris)\n emissionIrisLayout.addWidget(self.spinbox_emission_iris)\n\n filterSliderLayout = QHBoxLayout()\n filterSliderLayout.addWidget(QLabel(\"Filter Slider\"))\n #self.dropdown_filter_slider = QComboBox(self)\n #self.dropdown_filter_slider.addItems([\"0\", \"1\", \"2\", \"3\"])\n self.dropdown_filter_slider = QSlider(Qt.Horizontal)\n self.dropdown_filter_slider.setRange(0, 3)\n self.dropdown_filter_slider.setTickPosition(QSlider.TicksBelow)\n self.dropdown_filter_slider.setTickInterval(1)\n filterSliderLayout.addWidget(self.dropdown_filter_slider)\n\n self.btn_toggle_widefield = QPushButton(\"Switch to Confocal\")\n\n self.btn_toggle_motor = QPushButton(\"Disk Motor On\")\n self.btn_toggle_motor.setCheckable(True)\n\n layout = QGridLayout(self)\n\n # row 1\n if self.xlight.has_dichroic_filter_slider:\n layout.addLayout(filterSliderLayout,0,0,1,2)\n layout.addWidget(self.btn_toggle_motor,0,2)\n layout.addWidget(self.btn_toggle_widefield,0,3)\n\n # row 2\n if self.xlight.has_dichroic_filters_wheel:\n layout.addWidget(QLabel(\"Dichroic Filter Wheel\"),1,0)\n layout.addWidget(self.dropdown_dichroic,1,1)\n if self.xlight.has_illumination_iris_diaphragm:\n layout.addLayout(illuminationIrisLayout,1,2,1,2)\n\n # row 3\n if self.xlight.has_emission_filters_wheel:\n layout.addWidget(QLabel(\"Emission Filter Wheel\"),2,0)\n layout.addWidget(self.dropdown_emission_filter,2,1)\n if self.xlight.has_emission_iris_diaphragm:\n layout.addLayout(emissionIrisLayout,2,2,1,2)\n\n layout.setColumnStretch(2,1)\n layout.setColumnStretch(3,1)\n self.setLayout(layout)\n\n\n def disable_all_buttons(self):\n self.dropdown_emission_filter.setEnabled(False)\n self.dropdown_dichroic.setEnabled(False)\n self.btn_toggle_widefield.setEnabled(False)\n self.btn_toggle_motor.setEnabled(False)\n self.slider_illumination_iris.setEnabled(False)\n self.spinbox_illumination_iris.setEnabled(False)\n self.slider_emission_iris.setEnabled(False)\n self.spinbox_emission_iris.setEnabled(False)\n self.dropdown_filter_slider.setEnabled(False)\n\n def enable_all_buttons(self):\n self.dropdown_emission_filter.setEnabled(True)\n self.dropdown_dichroic.setEnabled(True)\n self.btn_toggle_widefield.setEnabled(True)\n self.btn_toggle_motor.setEnabled(True)\n self.slider_illumination_iris.setEnabled(True)\n self.spinbox_illumination_iris.setEnabled(True)\n self.slider_emission_iris.setEnabled(True)\n self.spinbox_emission_iris.setEnabled(True)\n self.dropdown_filter_slider.setEnabled(True)\n\n def toggle_disk_position(self):\n self.disable_all_buttons()\n if self.disk_position_state==1:\n self.disk_position_state = self.xlight.set_disk_position(0)\n self.btn_toggle_widefield.setText(\"Switch to Confocal\")\n else:\n self.disk_position_state = self.xlight.set_disk_position(1)\n self.btn_toggle_widefield.setText(\"Switch to Widefield\")\n if self.config_manager is not None:\n if self.disk_position_state ==1:\n self.config_manager.config_filename = \"confocal_configurations.xml\"\n else:\n self.config_manager.config_filename = \"widefield_configurations.xml\"\n self.config_manager.configurations = []\n self.config_manager.read_configurations()\n self.enable_all_buttons()\n\n def toggle_motor(self):\n self.disable_all_buttons()\n if self.btn_toggle_motor.isChecked():\n self.xlight.set_disk_motor_state(True)\n else:\n self.xlight.set_disk_motor_state(False)\n self.enable_all_buttons()\n\n def set_emission_filter(self, index):\n self.disable_all_buttons()\n selected_pos = self.dropdown_emission_filter.currentText()\n self.xlight.set_emission_filter(selected_pos)\n self.enable_all_buttons()\n\n def set_dichroic(self, index):\n self.disable_all_buttons()\n selected_pos = self.dropdown_dichroic.currentText()\n self.xlight.set_dichroic(selected_pos)\n self.enable_all_buttons()\n\n def update_illumination_iris(self, value):\n self.disable_all_buttons()\n # Update both slider and spinbox to ensure they're in sync\n self.slider_illumination_iris.setValue(value)\n self.spinbox_illumination_iris.setValue(value)\n self.xlight.set_illumination_iris(value)\n self.enable_all_buttons()\n\n def update_emission_iris(self, value):\n self.disable_all_buttons()\n # Update both slider and spinbox to ensure they're in sync\n self.slider_emission_iris.setValue(value)\n self.spinbox_emission_iris.setValue(value)\n self.xlight.set_emission_iris(value)\n self.enable_all_buttons()\n\n def set_filter_slider(self, index):\n self.disable_all_buttons()\n position = str(self.dropdown_filter_slider.value())\n self.xlight.set_filter_slider(position)\n self.enable_all_buttons()\n\n\nclass ObjectivesWidget(QWidget):\n signal_objective_changed = Signal()\n\n def __init__(self, objective_store):\n super(ObjectivesWidget, self).__init__()\n self.objectiveStore = objective_store\n self.init_ui()\n self.dropdown.setCurrentText(self.objectiveStore.current_objective)\n\n def init_ui(self):\n self.dropdown = QComboBox(self)\n self.dropdown.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n self.dropdown.addItems(self.objectiveStore.objectives_dict.keys())\n self.dropdown.currentTextChanged.connect(self.on_objective_changed)\n\n layout = QHBoxLayout()\n layout.addWidget(QLabel(\"Objective Lens\"))\n layout.addWidget(self.dropdown)\n self.setLayout(layout)\n\n def on_objective_changed(self, objective_name):\n self.objectiveStore.set_current_objective(objective_name)\n self.signal_objective_changed.emit()\n\n\nclass FocusMapWidget(QWidget):\n\n def __init__(self, autofocusController, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.autofocusController = autofocusController\n self.init_ui()\n\n def init_ui(self):\n self.btn_add_to_focusmap = QPushButton(\"Add to focus map\")\n self.btn_enable_focusmap = QPushButton(\"Enable focus map\")\n self.btn_clear_focusmap = QPushButton(\"Clear focus map\")\n self.fmap_coord_1 = QLabel(\"Focus Map Point 1: (xxx,yyy,zzz)\")\n self.fmap_coord_2 = QLabel(\"Focus Map Point 2: (xxx,yyy,zzz)\")\n self.fmap_coord_3 = QLabel(\"Focus Map Point 3: (xxx,yyy,zzz)\")\n layout = QVBoxLayout()\n layout.addWidget(self.fmap_coord_1)\n layout.addWidget(self.fmap_coord_2)\n layout.addWidget(self.fmap_coord_3)\n\n button_layout = QHBoxLayout()\n button_layout.addWidget(self.btn_add_to_focusmap)\n button_layout.addWidget(self.btn_clear_focusmap)\n\n layout.addLayout(button_layout)\n\n layout.addWidget(self.btn_enable_focusmap)\n\n self.setLayout(layout)\n\n self.btn_add_to_focusmap.clicked.connect(self.add_to_focusmap)\n self.btn_enable_focusmap.clicked.connect(self.enable_focusmap)\n self.btn_clear_focusmap.clicked.connect(self.clear_focusmap)\n\n def disable_all_buttons(self):\n self.btn_add_to_focusmap.setEnabled(False)\n self.btn_enable_focusmap.setEnabled(False)\n self.btn_clear_focusmap.setEnabled(False)\n\n def enable_all_buttons(self):\n self.btn_add_to_focusmap.setEnabled(True)\n self.btn_enable_focusmap.setEnabled(True)\n self.btn_clear_focusmap.setEnabled(True)\n\n def clear_focusmap(self):\n self.disable_all_buttons()\n self.autofocusController.clear_focus_map()\n self.update_focusmap_display()\n self.btn_enable_focusmap.setText(\"Enable focus map\")\n self.enable_all_buttons()\n\n def update_focusmap_display(self):\n self.fmap_coord_1.setText(\"Focus Map Point 1: (xxx,yyy,zzz)\")\n self.fmap_coord_2.setText(\"Focus Map Point 2: (xxx,yyy,zzz)\")\n self.fmap_coord_3.setText(\"Focus Map Point 3: (xxx,yyy,zzz)\")\n try:\n x,y,z = self.autofocusController.focus_map_coords[0]\n self.fmap_coord_1.setText(f\"Focus Map Point 1: ({x:.3f},{y:.3f},{z:.3f})\")\n except IndexError:\n pass\n try:\n x,y,z = self.autofocusController.focus_map_coords[1]\n self.fmap_coord_2.setText(f\"Focus Map Point 2: ({x:.3f},{y:.3f},{z:.3f})\")\n except IndexError:\n pass\n try:\n x,y,z = self.autofocusController.focus_map_coords[2]\n self.fmap_coord_3.setText(f\"Focus Map Point 3: ({x:.3f},{y:.3f},{z:.3f})\")\n except IndexError:\n pass\n\n def enable_focusmap(self):\n self.disable_all_buttons()\n if self.autofocusController.use_focus_map == False:\n self.autofocusController.set_focus_map_use(True)\n else:\n self.autofocusController.set_focus_map_use(False)\n if self.autofocusController.use_focus_map:\n self.btn_enable_focusmap.setText(\"Disable focus map\")\n else:\n self.btn_enable_focusmap.setText(\"Enable focus map\")\n self.enable_all_buttons()\n\n def add_to_focusmap(self):\n self.disable_all_buttons()\n try:\n self.autofocusController.add_current_coords_to_focus_map()\n except ValueError:\n pass\n self.update_focusmap_display()\n self.enable_all_buttons()\n\n\nclass CameraSettingsWidget(QFrame):\n\n def __init__(self, camera, include_gain_exposure_time = False, include_camera_temperature_setting = False, include_camera_auto_wb_setting = False, main=None, *args, **kwargs):\n\n super().__init__(*args, **kwargs)\n self.camera = camera\n self.add_components(include_gain_exposure_time,include_camera_temperature_setting,include_camera_auto_wb_setting)\n # set frame style\n self.setFrameStyle(QFrame.Panel | QFrame.Raised)\n\n def add_components(self,include_gain_exposure_time,include_camera_temperature_setting,include_camera_auto_wb_setting):\n\n # add buttons and input fields\n self.entry_exposureTime = QDoubleSpinBox()\n self.entry_exposureTime.setMinimum(self.camera.EXPOSURE_TIME_MS_MIN)\n self.entry_exposureTime.setMaximum(self.camera.EXPOSURE_TIME_MS_MAX)\n self.entry_exposureTime.setSingleStep(1)\n self.entry_exposureTime.setValue(20)\n self.camera.set_exposure_time(20)\n\n self.entry_analogGain = QDoubleSpinBox()\n self.entry_analogGain.setMinimum(self.camera.GAIN_MIN)\n self.entry_analogGain.setMaximum(self.camera.GAIN_MAX)\n self.entry_analogGain.setSingleStep(self.camera.GAIN_STEP)\n self.entry_analogGain.setValue(0)\n self.camera.set_analog_gain(0)\n\n self.dropdown_pixelFormat = QComboBox()\n self.dropdown_pixelFormat.addItems(['MONO8','MONO12','MONO14','MONO16','BAYER_RG8','BAYER_RG12'])\n if self.camera.pixel_format is not None:\n self.dropdown_pixelFormat.setCurrentText(self.camera.pixel_format)\n else:\n print(\"setting camera's default pixel format\")\n self.camera.set_pixel_format(DEFAULT_PIXEL_FORMAT)\n self.dropdown_pixelFormat.setCurrentText(DEFAULT_PIXEL_FORMAT)\n self.dropdown_pixelFormat.setSizePolicy(QSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed))\n # to do: load and save pixel format in configurations\n\n self.entry_ROI_offset_x = QSpinBox()\n self.entry_ROI_offset_x.setValue(self.camera.OffsetX)\n self.entry_ROI_offset_x.setSingleStep(8)\n self.entry_ROI_offset_x.setFixedWidth(60)\n self.entry_ROI_offset_x.setMinimum(0)\n self.entry_ROI_offset_x.setMaximum(self.camera.WidthMax)\n self.entry_ROI_offset_x.setKeyboardTracking(False)\n self.entry_ROI_offset_y = QSpinBox()\n self.entry_ROI_offset_y.setValue(self.camera.OffsetY)\n self.entry_ROI_offset_y.setSingleStep(8)\n self.entry_ROI_offset_y.setFixedWidth(60)\n self.entry_ROI_offset_y.setMinimum(0)\n self.entry_ROI_offset_y.setMaximum(self.camera.HeightMax)\n self.entry_ROI_offset_y.setKeyboardTracking(False)\n self.entry_ROI_width = QSpinBox()\n self.entry_ROI_width.setMinimum(16)\n self.entry_ROI_width.setMaximum(self.camera.WidthMax)\n self.entry_ROI_width.setValue(self.camera.Width)\n self.entry_ROI_width.setSingleStep(8)\n self.entry_ROI_width.setFixedWidth(60)\n self.entry_ROI_width.setKeyboardTracking(False)\n self.entry_ROI_height = QSpinBox()\n self.entry_ROI_height.setSingleStep(8)\n self.entry_ROI_height.setMinimum(16)\n self.entry_ROI_height.setMaximum(self.camera.HeightMax)\n self.entry_ROI_height.setValue(self.camera.Height)\n self.entry_ROI_height.setFixedWidth(60)\n self.entry_ROI_height.setKeyboardTracking(False)\n self.entry_temperature = QDoubleSpinBox()\n self.entry_temperature.setMaximum(25)\n self.entry_temperature.setMinimum(-50)\n self.entry_temperature.setDecimals(1)\n self.label_temperature_measured = QLabel()\n # self.label_temperature_measured.setNum(0)\n self.label_temperature_measured.setFrameStyle(QFrame.Panel | QFrame.Sunken)\n\n # connection\n self.entry_exposureTime.valueChanged.connect(self.camera.set_exposure_time)\n self.entry_analogGain.valueChanged.connect(self.camera.set_analog_gain)\n self.dropdown_pixelFormat.currentTextChanged.connect(self.camera.set_pixel_format)\n self.entry_ROI_offset_x.valueChanged.connect(self.set_ROI_offset)\n self.entry_ROI_offset_y.valueChanged.connect(self.set_ROI_offset)\n self.entry_ROI_height.valueChanged.connect(self.set_Height)\n self.entry_ROI_width.valueChanged.connect(self.set_Width)\n\n # layout\n self.camera_layout = QVBoxLayout()\n if include_gain_exposure_time:\n exposure_line = QHBoxLayout()\n exposure_line.addWidget(QLabel('Exposure Time (ms)'))\n exposure_line.addWidget(self.entry_exposureTime)\n self.camera_layout.addLayout(exposure_line)\n gain_line = QHBoxLayout()\n gain_line.addWidget(QLabel('Analog Gain'))\n gain_line.addWidget(self.entry_analogGain)\n self.camera_layout.addLayout(gain_line)\n\n format_line = QHBoxLayout()\n format_line.addWidget(QLabel('Pixel Format'))\n format_line.addWidget(self.dropdown_pixelFormat)\n try:\n current_res = self.camera.resolution\n current_res_string = \"x\".join([str(current_res[0]),str(current_res[1])])\n res_options = [f\"{res[0]} x {res[1]}\" for res in self.camera.res_list]\n self.dropdown_res = QComboBox()\n self.dropdown_res.addItems(res_options)\n self.dropdown_res.setCurrentText(current_res_string)\n\n self.dropdown_res.currentTextChanged.connect(self.change_full_res)\n except AttributeError as ae:\n print(ae)\n self.dropdown_res = QComboBox()\n self.dropdown_res.setEnabled(False)\n pass\n format_line.addWidget(QLabel(\" FOV Resolution\"))\n format_line.addWidget(self.dropdown_res)\n self.camera_layout.addLayout(format_line)\n\n if include_camera_temperature_setting:\n temp_line = QHBoxLayout()\n temp_line.addWidget(QLabel('Set Temperature (C)'))\n temp_line.addWidget(self.entry_temperature)\n temp_line.addWidget(QLabel('Actual Temperature (C)'))\n temp_line.addWidget(self.label_temperature_measured)\n try:\n self.entry_temperature.valueChanged.connect(self.set_temperature)\n self.camera.set_temperature_reading_callback(self.update_measured_temperature)\n except AttributeError:\n pass\n self.camera_layout.addLayout(temp_line)\n\n roi_line = QHBoxLayout()\n roi_line.addWidget(QLabel('Height'))\n roi_line.addWidget(self.entry_ROI_height)\n roi_line.addStretch()\n roi_line.addWidget(QLabel('Y-offset'))\n roi_line.addWidget(self.entry_ROI_offset_y)\n roi_line.addStretch()\n roi_line.addWidget(QLabel('Width'))\n roi_line.addWidget(self.entry_ROI_width)\n roi_line.addStretch()\n roi_line.addWidget(QLabel('X-offset'))\n roi_line.addWidget(self.entry_ROI_offset_x)\n self.camera_layout.addLayout(roi_line)\n\n if DISPLAY_TOUPCAMER_BLACKLEVEL_SETTINGS is True:\n blacklevel_line = QHBoxLayout()\n blacklevel_line.addWidget(QLabel('Black Level'))\n\n self.label_blackLevel = QSpinBox()\n self.label_blackLevel.setMinimum(0)\n self.label_blackLevel.setMaximum(31)\n self.label_blackLevel.valueChanged.connect(self.update_blacklevel)\n self.label_blackLevel.setSuffix(\" \")\n\n blacklevel_line.addWidget(self.label_blackLevel)\n\n self.camera_layout.addLayout(blacklevel_line)\n\n if include_camera_auto_wb_setting:\n is_color = False\n try:\n is_color = self.camera.get_is_color()\n except AttributeError:\n pass\n\n if is_color is True:\n # auto white balance\n self.btn_auto_wb = QPushButton('Auto White Balance')\n self.btn_auto_wb.setCheckable(True)\n self.btn_auto_wb.setChecked(False)\n self.btn_auto_wb.clicked.connect(self.toggle_auto_wb)\n print(self.camera.get_balance_white_auto())\n\n self.camera_layout.addLayout(grid_camera_setting_wb)\n\n self.setLayout(self.camera_layout)\n\n def toggle_auto_wb(self,pressed):\n # 0: OFF 1:CONTINUOUS 2:ONCE\n if pressed:\n self.camera.set_balance_white_auto(1)\n else:\n self.camera.set_balance_white_auto(0)\n\n def set_exposure_time(self,exposure_time):\n self.entry_exposureTime.setValue(exposure_time)\n\n def set_analog_gain(self,analog_gain):\n self.entry_analogGain.setValue(analog_gain)\n\n def set_Width(self):\n width = int(self.entry_ROI_width.value()//8)*8\n self.entry_ROI_width.blockSignals(True)\n self.entry_ROI_width.setValue(width)\n self.entry_ROI_width.blockSignals(False)\n offset_x = (self.camera.WidthMax - self.entry_ROI_width.value())/2\n offset_x = int(offset_x//8)*8\n self.entry_ROI_offset_x.blockSignals(True)\n self.entry_ROI_offset_x.setValue(offset_x)\n self.entry_ROI_offset_x.blockSignals(False)\n self.camera.set_ROI(self.entry_ROI_offset_x.value(),self.entry_ROI_offset_y.value(),self.entry_ROI_width.value(),self.entry_ROI_height.value())\n\n def set_Height(self):\n height = int(self.entry_ROI_height.value()//8)*8\n self.entry_ROI_height.blockSignals(True)\n self.entry_ROI_height.setValue(height)\n self.entry_ROI_height.blockSignals(False)\n offset_y = (self.camera.HeightMax - self.entry_ROI_height.value())/2\n offset_y = int(offset_y//8)*8\n self.entry_ROI_offset_y.blockSignals(True)\n self.entry_ROI_offset_y.setValue(offset_y)\n self.entry_ROI_offset_y.blockSignals(False)\n self.camera.set_ROI(self.entry_ROI_offset_x.value(),self.entry_ROI_offset_y.value(),self.entry_ROI_width.value(),self.entry_ROI_height.value())\n\n def set_ROI_offset(self):\n \tself.camera.set_ROI(self.entry_ROI_offset_x.value(),self.entry_ROI_offset_y.value(),self.entry_ROI_width.value(),self.entry_ROI_height.value())\n\n def set_temperature(self):\n try:\n self.camera.set_temperature(float(self.entry_temperature.value()))\n except AttributeError:\n pass\n\n def update_measured_temperature(self,temperature):\n self.label_temperature_measured.setNum(temperature)\n\n def change_full_res(self, index):\n res_strings = self.dropdown_res.currentText().split(\"x\")\n res_x = int(res_strings[0])\n res_y = int(res_strings[1])\n self.camera.set_resolution(res_x,res_y)\n self.entry_ROI_offset_x.blockSignals(True)\n self.entry_ROI_offset_y.blockSignals(True)\n self.entry_ROI_height.blockSignals(True)\n self.entry_ROI_width.blockSignals(True)\n\n self.entry_ROI_height.setMaximum(self.camera.HeightMax)\n self.entry_ROI_width.setMaximum(self.camera.WidthMax)\n\n self.entry_ROI_offset_x.setMaximum(self.camera.WidthMax)\n self.entry_ROI_offset_y.setMaximum(self.camera.HeightMax)\n\n self.entry_ROI_offset_x.setValue(int(8*self.camera.OffsetX//8))\n self.entry_ROI_offset_y.setValue(int(8*self.camera.OffsetY//8))\n self.entry_ROI_height.setValue(int(8*self.camera.Height//8))\n self.entry_ROI_width.setValue(int(8*self.camera.Width//8))\n\n self.entry_ROI_offset_x.blockSignals(False)\n self.entry_ROI_offset_y.blockSignals(False)\n self.entry_ROI_height.blockSignals(False)\n self.entry_ROI_width.blockSignals(False)\n\n def update_blacklevel(self, blacklevel):\n try:\n self.camera.set_blacklevel(blacklevel)\n except AttributeError:\n pass\n\n\nclass LiveControlWidget(QFrame):\n\n signal_newExposureTime = Signal(float)\n signal_newAnalogGain = Signal(float)\n signal_autoLevelSetting = Signal(bool)\n signal_live_configuration = Signal(object)\n signal_start_live = Signal()\n\n def __init__(self, streamHandler, liveController, configurationManager=None, show_trigger_options=True, show_display_options=False, show_autolevel = False, autolevel=False, stretch=True, main=None, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.liveController = liveController\n self.streamHandler = streamHandler\n self.configurationManager = configurationManager\n self.fps_trigger = 10\n self.fps_display = 10\n self.liveController.set_trigger_fps(self.fps_trigger)\n self.streamHandler.set_display_fps(self.fps_display)\n\n self.triggerMode = TriggerMode.SOFTWARE\n # note that this references the object in self.configurationManager.configurations\n self.currentConfiguration = self.configurationManager.configurations[0]\n\n self.add_components(show_trigger_options,show_display_options,show_autolevel,autolevel,stretch)\n self.setFrameStyle(QFrame.Panel | QFrame.Raised)\n self.update_microscope_mode_by_name(self.currentConfiguration.name)\n\n self.is_switching_mode = False # flag used to prevent from settings being set by twice - from both mode change slot and value change slot; another way is to use blockSignals(True)\n\n def add_components(self,show_trigger_options,show_display_options,show_autolevel,autolevel,stretch):\n # line 0: trigger mode\n self.triggerMode = None\n self.dropdown_triggerManu = QComboBox()\n self.dropdown_triggerManu.addItems([TriggerMode.SOFTWARE,TriggerMode.HARDWARE,TriggerMode.CONTINUOUS])\n sizePolicy = QSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n self.dropdown_triggerManu.setSizePolicy(sizePolicy)\n\n # line 1: fps\n self.entry_triggerFPS = QDoubleSpinBox()\n self.entry_triggerFPS.setMinimum(0.02)\n self.entry_triggerFPS.setMaximum(1000)\n self.entry_triggerFPS.setSingleStep(1)\n self.entry_triggerFPS.setValue(self.fps_trigger)\n self.entry_triggerFPS.setDecimals(0)\n\n # line 2: choose microscope mode / toggle live mode\n self.dropdown_modeSelection = QComboBox()\n for microscope_configuration in self.configurationManager.configurations:\n self.dropdown_modeSelection.addItems([microscope_configuration.name])\n self.dropdown_modeSelection.setCurrentText(self.currentConfiguration.name)\n self.dropdown_modeSelection.setSizePolicy(sizePolicy)\n\n self.btn_live = QPushButton(\"Start Live\")\n self.btn_live.setCheckable(True)\n self.btn_live.setChecked(False)\n self.btn_live.setDefault(False)\n self.btn_live.setStyleSheet(\"background-color: #C2C2FF\")\n self.btn_live.setSizePolicy(sizePolicy)\n\n # line 3: exposure time and analog gain associated with the current mode\n self.entry_exposureTime = QDoubleSpinBox()\n self.entry_exposureTime.setMinimum(self.liveController.camera.EXPOSURE_TIME_MS_MIN)\n self.entry_exposureTime.setMaximum(self.liveController.camera.EXPOSURE_TIME_MS_MAX)\n self.entry_exposureTime.setSingleStep(1)\n self.entry_exposureTime.setSuffix(' ms')\n self.entry_exposureTime.setValue(0)\n self.entry_exposureTime.setSizePolicy(sizePolicy)\n\n self.entry_analogGain = QDoubleSpinBox()\n self.entry_analogGain = QDoubleSpinBox()\n self.entry_analogGain.setMinimum(0)\n self.entry_analogGain.setMaximum(24)\n # self.entry_analogGain.setSuffix('x')\n self.entry_analogGain.setSingleStep(0.1)\n self.entry_analogGain.setValue(0)\n self.entry_analogGain.setSizePolicy(sizePolicy)\n\n self.slider_illuminationIntensity = QSlider(Qt.Horizontal)\n self.slider_illuminationIntensity.setTickPosition(QSlider.TicksBelow)\n self.slider_illuminationIntensity.setMinimum(0)\n self.slider_illuminationIntensity.setMaximum(100)\n self.slider_illuminationIntensity.setValue(100)\n self.slider_illuminationIntensity.setSingleStep(2)\n\n self.entry_illuminationIntensity = QDoubleSpinBox()\n self.entry_illuminationIntensity.setMinimum(0)\n self.entry_illuminationIntensity.setMaximum(100)\n self.entry_illuminationIntensity.setSingleStep(1)\n self.entry_illuminationIntensity.setSuffix('%')\n self.entry_illuminationIntensity.setValue(100)\n\n # line 4: display fps and resolution scaling\n self.entry_displayFPS = QDoubleSpinBox()\n self.entry_displayFPS.setMinimum(1)\n self.entry_displayFPS.setMaximum(240)\n self.entry_displayFPS.setSingleStep(1)\n self.entry_displayFPS.setDecimals(0)\n self.entry_displayFPS.setValue(self.fps_display)\n\n self.slider_resolutionScaling = QSlider(Qt.Horizontal)\n self.slider_resolutionScaling.setTickPosition(QSlider.TicksBelow)\n self.slider_resolutionScaling.setMinimum(10)\n self.slider_resolutionScaling.setMaximum(100)\n self.slider_resolutionScaling.setValue(DEFAULT_DISPLAY_CROP)\n self.slider_resolutionScaling.setSingleStep(10)\n\n self.label_resolutionScaling = QSpinBox()\n self.label_resolutionScaling.setMinimum(10)\n self.label_resolutionScaling.setMaximum(100)\n self.label_resolutionScaling.setValue(self.slider_resolutionScaling.value())\n self.label_resolutionScaling.setSuffix(\" %\")\n self.slider_resolutionScaling.setSingleStep(5)\n\n self.slider_resolutionScaling.valueChanged.connect(lambda v: self.label_resolutionScaling.setValue(round(v)))\n self.label_resolutionScaling.valueChanged.connect(lambda v: self.slider_resolutionScaling.setValue(round(v)))\n\n # autolevel\n self.btn_autolevel = QPushButton('Autolevel')\n self.btn_autolevel.setCheckable(True)\n self.btn_autolevel.setChecked(autolevel)\n\n # Determine the maximum width needed\n self.entry_illuminationIntensity.setMinimumWidth(self.btn_live.sizeHint().width())\n self.btn_autolevel.setMinimumWidth(self.btn_autolevel.sizeHint().width())\n\n max_width = max(\n self.btn_autolevel.minimumWidth(),\n self.entry_illuminationIntensity.minimumWidth()\n )\n\n # Set the fixed width for all three widgets\n self.entry_illuminationIntensity.setFixedWidth(max_width)\n self.btn_autolevel.setFixedWidth(max_width)\n\n # connections\n self.entry_triggerFPS.valueChanged.connect(self.liveController.set_trigger_fps)\n self.entry_displayFPS.valueChanged.connect(self.streamHandler.set_display_fps)\n self.slider_resolutionScaling.valueChanged.connect(self.streamHandler.set_display_resolution_scaling)\n self.slider_resolutionScaling.valueChanged.connect(self.liveController.set_display_resolution_scaling)\n self.dropdown_modeSelection.currentTextChanged.connect(self.update_microscope_mode_by_name)\n self.dropdown_triggerManu.currentIndexChanged.connect(self.update_trigger_mode)\n self.btn_live.clicked.connect(self.toggle_live)\n self.entry_exposureTime.valueChanged.connect(self.update_config_exposure_time)\n self.entry_analogGain.valueChanged.connect(self.update_config_analog_gain)\n self.entry_illuminationIntensity.valueChanged.connect(self.update_config_illumination_intensity)\n self.entry_illuminationIntensity.valueChanged.connect(lambda x: self.slider_illuminationIntensity.setValue(int(x)))\n self.slider_illuminationIntensity.valueChanged.connect(self.entry_illuminationIntensity.setValue)\n self.btn_autolevel.toggled.connect(self.signal_autoLevelSetting.emit)\n\n # layout\n grid_line1 = QHBoxLayout()\n grid_line1.addWidget(QLabel('Live Configuration'))\n grid_line1.addWidget(self.dropdown_modeSelection, 2)\n grid_line1.addWidget(self.btn_live, 1)\n\n grid_line2 = QHBoxLayout()\n grid_line2.addWidget(QLabel('Exposure Time'))\n grid_line2.addWidget(self.entry_exposureTime)\n gain_label = QLabel(' Analog Gain')\n gain_label.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n grid_line2.addWidget(gain_label)\n grid_line2.addWidget(self.entry_analogGain)\n if show_autolevel:\n grid_line2.addWidget(self.btn_autolevel)\n\n grid_line4 = QHBoxLayout()\n grid_line4.addWidget(QLabel('Illumination'))\n grid_line4.addWidget(self.slider_illuminationIntensity)\n grid_line4.addWidget(self.entry_illuminationIntensity)\n\n grid_line0 = QHBoxLayout()\n if show_trigger_options:\n grid_line0.addWidget(QLabel('Trigger Mode'))\n grid_line0.addWidget(self.dropdown_triggerManu)\n grid_line0.addWidget(QLabel('Trigger FPS'))\n grid_line0.addWidget(self.entry_triggerFPS)\n\n grid_line05 = QHBoxLayout()\n show_dislpay_fps = False\n if show_display_options:\n resolution_label = QLabel('Display Resolution')\n resolution_label.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n grid_line05.addWidget(resolution_label)\n grid_line05.addWidget(self.slider_resolutionScaling)\n if show_dislpay_fps:\n grid_line05.addWidget(QLabel('Display FPS'))\n grid_line05.addWidget(self.entry_displayFPS)\n else:\n grid_line05.addWidget(self.label_resolutionScaling)\n\n self.grid = QVBoxLayout()\n if show_trigger_options:\n self.grid.addLayout(grid_line0)\n self.grid.addLayout(grid_line1)\n self.grid.addLayout(grid_line2)\n self.grid.addLayout(grid_line4)\n if show_display_options:\n self.grid.addLayout(grid_line05)\n if not stretch:\n self.grid.addStretch()\n self.setLayout(self.grid)\n\n\n def toggle_live(self,pressed):\n if pressed:\n self.liveController.start_live()\n self.btn_live.setText('Stop Live')\n self.signal_start_live.emit()\n else:\n self.liveController.stop_live()\n self.btn_live.setText('Start Live')\n\n def toggle_autolevel(self,autolevel_on):\n self.btn_autolevel.setChecked(autolevel_on)\n\n def update_camera_settings(self):\n self.signal_newAnalogGain.emit(self.entry_analogGain.value())\n self.signal_newExposureTime.emit(self.entry_exposureTime.value())\n\n def update_microscope_mode_by_name(self,current_microscope_mode_name):\n self.is_switching_mode = True\n # identify the mode selected (note that this references the object in self.configurationManager.configurations)\n self.currentConfiguration = next((config for config in self.configurationManager.configurations if config.name == current_microscope_mode_name), None)\n self.signal_live_configuration.emit(self.currentConfiguration)\n # update the microscope to the current configuration\n self.liveController.set_microscope_mode(self.currentConfiguration)\n # update the exposure time and analog gain settings according to the selected configuration\n self.entry_exposureTime.setValue(self.currentConfiguration.exposure_time)\n self.entry_analogGain.setValue(self.currentConfiguration.analog_gain)\n self.entry_illuminationIntensity.setValue(self.currentConfiguration.illumination_intensity)\n self.is_switching_mode = False\n\n def update_trigger_mode(self):\n self.liveController.set_trigger_mode(self.dropdown_triggerManu.currentText())\n\n def update_config_exposure_time(self,new_value):\n if self.is_switching_mode == False:\n self.currentConfiguration.exposure_time = new_value\n self.configurationManager.update_configuration(self.currentConfiguration.id,'ExposureTime',new_value)\n self.signal_newExposureTime.emit(new_value)\n\n def update_config_analog_gain(self,new_value):\n if self.is_switching_mode == False:\n self.currentConfiguration.analog_gain = new_value\n self.configurationManager.update_configuration(self.currentConfiguration.id,'AnalogGain',new_value)\n self.signal_newAnalogGain.emit(new_value)\n\n def update_config_illumination_intensity(self,new_value):\n if self.is_switching_mode == False:\n self.currentConfiguration.illumination_intensity = new_value\n self.configurationManager.update_configuration(self.currentConfiguration.id,'IlluminationIntensity',new_value)\n self.liveController.set_illumination(self.currentConfiguration.illumination_source, self.currentConfiguration.illumination_intensity)\n\n def set_microscope_mode(self,config):\n # self.liveController.set_microscope_mode(config)\n self.dropdown_modeSelection.setCurrentText(config.name)\n\n def set_trigger_mode(self,trigger_mode):\n self.dropdown_triggerManu.setCurrentText(trigger_mode)\n self.liveController.set_trigger_mode(self.dropdown_triggerManu.currentText())\n\n\nclass PiezoWidget(QFrame):\n def __init__(self, navigationController, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.navigationController = navigationController\n self.slider_value = 0.00\n self.add_components()\n\n def add_components(self):\n # Row 1: Slider and Double Spin Box for direct control\n self.slider = QSlider(Qt.Horizontal, self)\n self.slider.setMinimum(0)\n self.slider.setMaximum(int(OBJECTIVE_PIEZO_RANGE_UM * 100)) # Multiplied by 100 for 0.01 precision\n\n self.spinBox = QDoubleSpinBox(self)\n self.spinBox.setRange(0.0, OBJECTIVE_PIEZO_RANGE_UM)\n self.spinBox.setDecimals(2)\n self.spinBox.setSingleStep(0.01)\n self.spinBox.setSuffix(' μm')\n\n # Row 3: Home Button\n self.home_btn = QPushButton(f\" Set to {OBJECTIVE_PIEZO_HOME_UM} μm \", self)\n\n hbox1 = QHBoxLayout()\n hbox1.addWidget(self.home_btn)\n hbox1.addWidget(self.slider)\n hbox1.addWidget(self.spinBox)\n\n # Row 2: Increment Double Spin Box, Move Up and Move Down Buttons\n self.increment_spinBox = QDoubleSpinBox(self)\n self.increment_spinBox.setRange(0.0, 100.0)\n self.increment_spinBox.setDecimals(2)\n self.increment_spinBox.setSingleStep(1)\n self.increment_spinBox.setValue(1.00)\n self.increment_spinBox.setSuffix(' μm')\n self.move_up_btn = QPushButton(\"Move Up\", self)\n self.move_down_btn = QPushButton(\"Move Down\", self)\n\n hbox2 = QHBoxLayout()\n hbox2.addWidget(self.increment_spinBox)\n hbox2.addWidget(self.move_up_btn)\n hbox2.addWidget(self.move_down_btn)\n\n # Vertical Layout to include all HBoxes\n vbox = QVBoxLayout()\n vbox.addLayout(hbox1)\n vbox.addLayout(hbox2)\n\n self.setLayout(vbox)\n\n # Connect signals and slots\n self.slider.valueChanged.connect(self.update_from_slider)\n self.spinBox.valueChanged.connect(self.update_from_spinBox)\n self.move_up_btn.clicked.connect(lambda: self.adjust_position(True))\n self.move_down_btn.clicked.connect(lambda: self.adjust_position(False))\n self.home_btn.clicked.connect(self.home)\n\n def update_from_slider(self, value):\n self.slider_value = value / 100 # Convert back to float with two decimal places\n self.update_spinBox()\n self.update_piezo_position()\n\n def update_from_spinBox(self, value):\n self.slider_value = value\n self.update_slider()\n self.update_piezo_position()\n\n def update_spinBox(self):\n self.spinBox.blockSignals(True)\n self.spinBox.setValue(self.slider_value)\n self.spinBox.blockSignals(False)\n\n def update_slider(self):\n self.slider.blockSignals(True)\n self.slider.setValue(int(self.slider_value * 100))\n self.slider.blockSignals(False)\n\n def update_piezo_position(self):\n displacement_um = self.slider_value\n self.navigationController.set_piezo_um(displacement_um)\n\n def adjust_position(self, up):\n increment = self.increment_spinBox.value()\n if up:\n self.slider_value = min(OBJECTIVE_PIEZO_RANGE_UM, self.slider_value + increment)\n else:\n self.slider_value = max(0, self.slider_value - increment)\n self.update_spinBox()\n self.update_slider()\n self.update_piezo_position()\n\n def home(self):\n self.slider_value = OBJECTIVE_PIEZO_HOME_UM\n self.update_spinBox()\n self.update_slider()\n self.update_piezo_position()\n\n def update_displacement_um_display(self, displacement):\n self.slider_value = round(displacement, 2)\n self.update_spinBox()\n self.update_slider()\n\n\nclass RecordingWidget(QFrame):\n def __init__(self, streamHandler, imageSaver, main=None, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.imageSaver = imageSaver # for saving path control\n self.streamHandler = streamHandler\n self.base_path_is_set = False\n self.add_components()\n self.setFrameStyle(QFrame.Panel | QFrame.Raised)\n\n def add_components(self):\n self.btn_setSavingDir = QPushButton('Browse')\n self.btn_setSavingDir.setDefault(False)\n self.btn_setSavingDir.setIcon(QIcon('icon/folder.png'))\n\n self.lineEdit_savingDir = QLineEdit()\n self.lineEdit_savingDir.setReadOnly(True)\n self.lineEdit_savingDir.setText('Choose a base saving directory')\n\n self.lineEdit_savingDir.setText(DEFAULT_SAVING_PATH)\n self.imageSaver.set_base_path(DEFAULT_SAVING_PATH)\n\n self.lineEdit_experimentID = QLineEdit()\n\n self.entry_saveFPS = QDoubleSpinBox()\n self.entry_saveFPS.setMinimum(0.02)\n self.entry_saveFPS.setMaximum(1000)\n self.entry_saveFPS.setSingleStep(1)\n self.entry_saveFPS.setValue(1)\n self.streamHandler.set_save_fps(1)\n\n self.entry_timeLimit = QSpinBox()\n self.entry_timeLimit.setMinimum(-1)\n self.entry_timeLimit.setMaximum(60*60*24*30)\n self.entry_timeLimit.setSingleStep(1)\n self.entry_timeLimit.setValue(-1)\n\n self.btn_record = QPushButton(\"Record\")\n self.btn_record.setCheckable(True)\n self.btn_record.setChecked(False)\n self.btn_record.setDefault(False)\n\n grid_line1 = QGridLayout()\n grid_line1.addWidget(QLabel('Saving Path'))\n grid_line1.addWidget(self.lineEdit_savingDir, 0,1)\n grid_line1.addWidget(self.btn_setSavingDir, 0,2)\n\n grid_line2 = QGridLayout()\n grid_line2.addWidget(QLabel('Experiment ID'), 0,0)\n grid_line2.addWidget(self.lineEdit_experimentID,0,1)\n\n grid_line3 = QGridLayout()\n grid_line3.addWidget(QLabel('Saving FPS'), 0,0)\n grid_line3.addWidget(self.entry_saveFPS, 0,1)\n grid_line3.addWidget(QLabel('Time Limit (s)'), 0,2)\n grid_line3.addWidget(self.entry_timeLimit, 0,3)\n\n self.grid = QVBoxLayout()\n self.grid.addLayout(grid_line1)\n self.grid.addLayout(grid_line2)\n self.grid.addLayout(grid_line3)\n self.grid.addWidget(self.btn_record)\n self.setLayout(self.grid)\n\n # add and display a timer - to be implemented\n # self.timer = QTimer()\n\n # connections\n self.btn_setSavingDir.clicked.connect(self.set_saving_dir)\n self.btn_record.clicked.connect(self.toggle_recording)\n self.entry_saveFPS.valueChanged.connect(self.streamHandler.set_save_fps)\n self.entry_timeLimit.valueChanged.connect(self.imageSaver.set_recording_time_limit)\n self.imageSaver.stop_recording.connect(self.stop_recording)\n\n def set_saving_dir(self):\n dialog = QFileDialog()\n save_dir_base = dialog.getExistingDirectory(None, \"Select Folder\")\n self.imageSaver.set_base_path(save_dir_base)\n self.lineEdit_savingDir.setText(save_dir_base)\n self.base_path_is_set = True\n\n def toggle_recording(self,pressed):\n if self.base_path_is_set == False:\n self.btn_record.setChecked(False)\n msg = QMessageBox()\n msg.setText(\"Please choose base saving directory first\")\n msg.exec_()\n return\n if pressed:\n self.lineEdit_experimentID.setEnabled(False)\n self.btn_setSavingDir.setEnabled(False)\n self.imageSaver.start_new_experiment(self.lineEdit_experimentID.text())\n self.streamHandler.start_recording()\n else:\n self.streamHandler.stop_recording()\n self.lineEdit_experimentID.setEnabled(True)\n self.btn_setSavingDir.setEnabled(True)\n\n # stop_recording can be called by imageSaver\n def stop_recording(self):\n self.lineEdit_experimentID.setEnabled(True)\n self.btn_record.setChecked(False)\n self.streamHandler.stop_recording()\n self.btn_setSavingDir.setEnabled(True)\n\n\nclass NavigationWidget(QFrame):\n def __init__(self, navigationController, slidePositionController=None, main=None, widget_configuration = 'full', *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.navigationController = navigationController\n self.slidePositionController = slidePositionController\n self.widget_configuration = widget_configuration\n self.slide_position = None\n self.flag_click_to_move = navigationController.click_to_move\n self.add_components()\n self.setFrameStyle(QFrame.Panel | QFrame.Raised)\n\n def add_components(self):\n x_label = QLabel('X :')\n x_label.setFixedWidth(20)\n self.label_Xpos = QLabel()\n self.label_Xpos.setNum(0)\n self.label_Xpos.setFrameStyle(QFrame.Panel | QFrame.Sunken)\n self.entry_dX = QDoubleSpinBox()\n self.entry_dX.setMinimum(0)\n self.entry_dX.setMaximum(25)\n self.entry_dX.setSingleStep(0.2)\n self.entry_dX.setValue(0)\n self.entry_dX.setDecimals(3)\n self.entry_dX.setSuffix(' mm')\n self.entry_dX.setKeyboardTracking(False)\n self.btn_moveX_forward = QPushButton('Forward')\n self.btn_moveX_forward.setDefault(False)\n self.btn_moveX_backward = QPushButton('Backward')\n self.btn_moveX_backward.setDefault(False)\n\n self.btn_home_X = QPushButton('Home X')\n self.btn_home_X.setDefault(False)\n self.btn_home_X.setEnabled(HOMING_ENABLED_X)\n self.btn_zero_X = QPushButton('Zero X')\n self.btn_zero_X.setDefault(False)\n\n self.checkbox_clickToMove = QCheckBox('Click to Move')\n self.checkbox_clickToMove.setChecked(False)\n self.checkbox_clickToMove.setSizePolicy(QSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed))\n\n y_label = QLabel('Y :')\n y_label.setFixedWidth(20)\n self.label_Ypos = QLabel()\n self.label_Ypos.setNum(0)\n self.label_Ypos.setFrameStyle(QFrame.Panel | QFrame.Sunken)\n self.entry_dY = QDoubleSpinBox()\n self.entry_dY.setMinimum(0)\n self.entry_dY.setMaximum(25)\n self.entry_dY.setSingleStep(0.2)\n self.entry_dY.setValue(0)\n self.entry_dY.setDecimals(3)\n self.entry_dY.setSuffix(' mm')\n\n self.entry_dY.setKeyboardTracking(False)\n self.btn_moveY_forward = QPushButton('Forward')\n self.btn_moveY_forward.setDefault(False)\n self.btn_moveY_backward = QPushButton('Backward')\n self.btn_moveY_backward.setDefault(False)\n\n self.btn_home_Y = QPushButton('Home Y')\n self.btn_home_Y.setDefault(False)\n self.btn_home_Y.setEnabled(HOMING_ENABLED_Y)\n self.btn_zero_Y = QPushButton('Zero Y')\n self.btn_zero_Y.setDefault(False)\n\n z_label = QLabel('Z :')\n z_label.setFixedWidth(20)\n self.label_Zpos = QLabel()\n self.label_Zpos.setNum(0)\n self.label_Zpos.setFrameStyle(QFrame.Panel | QFrame.Sunken)\n self.entry_dZ = QDoubleSpinBox()\n self.entry_dZ.setMinimum(0)\n self.entry_dZ.setMaximum(1000)\n self.entry_dZ.setSingleStep(0.2)\n self.entry_dZ.setValue(0)\n self.entry_dZ.setDecimals(3)\n self.entry_dZ.setSuffix(' μm')\n self.entry_dZ.setKeyboardTracking(False)\n self.btn_moveZ_forward = QPushButton('Forward')\n self.btn_moveZ_forward.setDefault(False)\n self.btn_moveZ_backward = QPushButton('Backward')\n self.btn_moveZ_backward.setDefault(False)\n\n self.btn_home_Z = QPushButton('Home Z')\n self.btn_home_Z.setDefault(False)\n self.btn_home_Z.setEnabled(HOMING_ENABLED_Z)\n self.btn_zero_Z = QPushButton('Zero Z')\n self.btn_zero_Z.setDefault(False)\n\n self.btn_load_slide = QPushButton('Move To Loading Position')\n self.btn_load_slide.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n\n grid_line0 = QGridLayout()\n grid_line0.addWidget(x_label, 0,0)\n grid_line0.addWidget(self.label_Xpos, 0,1)\n grid_line0.addWidget(self.entry_dX, 0,2)\n grid_line0.addWidget(self.btn_moveX_forward, 0,3)\n grid_line0.addWidget(self.btn_moveX_backward, 0,4)\n\n grid_line0.addWidget(y_label, 1,0)\n grid_line0.addWidget(self.label_Ypos, 1,1)\n grid_line0.addWidget(self.entry_dY, 1,2)\n grid_line0.addWidget(self.btn_moveY_forward, 1,3)\n grid_line0.addWidget(self.btn_moveY_backward, 1,4)\n\n grid_line0.addWidget(z_label, 2,0)\n grid_line0.addWidget(self.label_Zpos, 2,1)\n grid_line0.addWidget(self.entry_dZ, 2,2)\n grid_line0.addWidget(self.btn_moveZ_forward, 2,3)\n grid_line0.addWidget(self.btn_moveZ_backward, 2,4)\n\n grid_line3 = QHBoxLayout()\n\n if self.widget_configuration == 'full':\n grid_line3.addWidget(self.btn_home_X)\n grid_line3.addWidget(self.btn_home_Y)\n grid_line3.addWidget(self.btn_home_Z)\n grid_line3.addWidget(self.btn_zero_X)\n grid_line3.addWidget(self.btn_zero_Y)\n grid_line3.addWidget(self.btn_zero_Z)\n else:\n grid_line3.addWidget(self.btn_load_slide, 1)\n grid_line3.addWidget(self.btn_home_Z, 1)\n grid_line3.addWidget(self.btn_zero_Z, 1)\n\n if not ENABLE_CLICK_TO_MOVE_BY_DEFAULT:\n grid_line3.addWidget(self.checkbox_clickToMove, 1)\n\n self.grid = QVBoxLayout()\n self.grid.addLayout(grid_line0)\n self.grid.addLayout(grid_line3)\n self.setLayout(self.grid)\n\n self.entry_dX.valueChanged.connect(self.set_deltaX)\n self.entry_dY.valueChanged.connect(self.set_deltaY)\n self.entry_dZ.valueChanged.connect(self.set_deltaZ)\n\n self.btn_moveX_forward.clicked.connect(self.move_x_forward)\n self.btn_moveX_backward.clicked.connect(self.move_x_backward)\n self.btn_moveY_forward.clicked.connect(self.move_y_forward)\n self.btn_moveY_backward.clicked.connect(self.move_y_backward)\n self.btn_moveZ_forward.clicked.connect(self.move_z_forward)\n self.btn_moveZ_backward.clicked.connect(self.move_z_backward)\n\n self.btn_home_X.clicked.connect(self.home_x)\n self.btn_home_Y.clicked.connect(self.home_y)\n self.btn_home_Z.clicked.connect(self.home_z)\n self.btn_zero_X.clicked.connect(self.zero_x)\n self.btn_zero_Y.clicked.connect(self.zero_y)\n self.btn_zero_Z.clicked.connect(self.zero_z)\n\n self.checkbox_clickToMove.stateChanged.connect(self.navigationController.set_flag_click_to_move)\n\n self.btn_load_slide.clicked.connect(self.switch_position)\n self.btn_load_slide.setStyleSheet(\"background-color: #C2C2FF\")\n\n def toggle_click_to_move(self, started):\n if started:\n self.flag_click_to_move = self.navigationController.get_flag_click_to_move()\n self.setEnabled_all(False)\n self.checkbox_clickToMove.setChecked(False) # should set navigationController.click_to_move to False\n self.navigationController.click_to_move = False\n print(\"set click to move off\")\n else:\n self.setEnabled_all(True)\n self.checkbox_clickToMove.setChecked(self.flag_click_to_move)\n self.navigationController.click_to_move = self.flag_click_to_move\n print(\"restored click to move to\", \"on\" if self.flag_click_to_move else \"off\")\n\n def setEnabled_all(self, enabled):\n self.checkbox_clickToMove.setEnabled(enabled)\n self.btn_home_X.setEnabled(enabled)\n self.btn_zero_X.setEnabled(enabled)\n self.btn_moveX_forward.setEnabled(enabled)\n self.btn_moveX_backward.setEnabled(enabled)\n self.btn_home_Y.setEnabled(enabled)\n self.btn_zero_Y.setEnabled(enabled)\n self.btn_moveY_forward.setEnabled(enabled)\n self.btn_moveY_backward.setEnabled(enabled)\n self.btn_home_Z.setEnabled(enabled)\n self.btn_zero_Z.setEnabled(enabled)\n self.btn_moveZ_forward.setEnabled(enabled)\n self.btn_moveZ_backward.setEnabled(enabled)\n self.btn_load_slide.setEnabled(enabled)\n\n def move_x_forward(self):\n self.navigationController.move_x(self.entry_dX.value())\n def move_x_backward(self):\n self.navigationController.move_x(-self.entry_dX.value())\n def move_y_forward(self):\n self.navigationController.move_y(self.entry_dY.value())\n def move_y_backward(self):\n self.navigationController.move_y(-self.entry_dY.value())\n def move_z_forward(self):\n self.navigationController.move_z(self.entry_dZ.value()/1000)\n def move_z_backward(self):\n self.navigationController.move_z(-self.entry_dZ.value()/1000)\n\n def set_deltaX(self,value):\n mm_per_ustep = self.navigationController.get_mm_per_ustep_X()\n deltaX = round(value/mm_per_ustep)*mm_per_ustep\n self.entry_dX.setValue(deltaX)\n def set_deltaY(self,value):\n mm_per_ustep = self.navigationController.get_mm_per_ustep_Y()\n deltaY = round(value/mm_per_ustep)*mm_per_ustep\n self.entry_dY.setValue(deltaY)\n def set_deltaZ(self,value):\n mm_per_ustep = self.navigationController.get_mm_per_ustep_Z()\n deltaZ = round(value/1000/mm_per_ustep)*mm_per_ustep*1000\n self.entry_dZ.setValue(deltaZ)\n\n def home_x(self):\n msg = QMessageBox()\n msg.setIcon(QMessageBox.Information)\n msg.setText(\"Confirm your action\")\n msg.setInformativeText(\"Click OK to run homing\")\n msg.setWindowTitle(\"Confirmation\")\n msg.setStandardButtons(QMessageBox.Ok | QMessageBox.Cancel)\n msg.setDefaultButton(QMessageBox.Cancel)\n retval = msg.exec_()\n if QMessageBox.Ok == retval:\n self.navigationController.home_x()\n\n def home_y(self):\n msg = QMessageBox()\n msg.setIcon(QMessageBox.Information)\n msg.setText(\"Confirm your action\")\n msg.setInformativeText(\"Click OK to run homing\")\n msg.setWindowTitle(\"Confirmation\")\n msg.setStandardButtons(QMessageBox.Ok | QMessageBox.Cancel)\n msg.setDefaultButton(QMessageBox.Cancel)\n retval = msg.exec_()\n if QMessageBox.Ok == retval:\n self.navigationController.home_y()\n\n def home_z(self):\n msg = QMessageBox()\n msg.setIcon(QMessageBox.Information)\n msg.setText(\"Confirm your action\")\n msg.setInformativeText(\"Click OK to run homing\")\n msg.setWindowTitle(\"Confirmation\")\n msg.setStandardButtons(QMessageBox.Ok | QMessageBox.Cancel)\n msg.setDefaultButton(QMessageBox.Cancel)\n retval = msg.exec_()\n if QMessageBox.Ok == retval:\n self.navigationController.home_z()\n\n def zero_x(self):\n self.navigationController.zero_x()\n\n def zero_y(self):\n self.navigationController.zero_y()\n\n def zero_z(self):\n self.navigationController.zero_z()\n\n def slot_slide_loading_position_reached(self):\n self.slide_position = 'loading'\n self.btn_load_slide.setStyleSheet(\"background-color: #C2FFC2\")\n self.btn_load_slide.setText('Move to Scanning Position')\n self.btn_moveX_forward.setEnabled(False)\n self.btn_moveX_backward.setEnabled(False)\n self.btn_moveY_forward.setEnabled(False)\n self.btn_moveY_backward.setEnabled(False)\n self.btn_moveZ_forward.setEnabled(False)\n self.btn_moveZ_backward.setEnabled(False)\n self.btn_load_slide.setEnabled(True)\n\n def slot_slide_scanning_position_reached(self):\n self.slide_position = 'scanning'\n self.btn_load_slide.setStyleSheet(\"background-color: #C2C2FF\")\n self.btn_load_slide.setText('Move to Loading Position')\n self.btn_moveX_forward.setEnabled(True)\n self.btn_moveX_backward.setEnabled(True)\n self.btn_moveY_forward.setEnabled(True)\n self.btn_moveY_backward.setEnabled(True)\n self.btn_moveZ_forward.setEnabled(True)\n self.btn_moveZ_backward.setEnabled(True)\n self.btn_load_slide.setEnabled(True)\n\n def switch_position(self):\n if self.slide_position != 'loading':\n self.slidePositionController.move_to_slide_loading_position()\n else:\n self.slidePositionController.move_to_slide_scanning_position()\n self.btn_load_slide.setEnabled(False)\n\n def replace_slide_controller(self, slidePositionController):\n self.slidePositionController = slidePositionController\n self.slidePositionController.signal_slide_loading_position_reached.connect(self.slot_slide_loading_position_reached)\n self.slidePositionController.signal_slide_scanning_position_reached.connect(self.slot_slide_scanning_position_reached)\n\n\nclass NavigationBarWidget(QWidget):\n def __init__(self, navigationController=None, slidePositionController=None, add_z_buttons=True,*args, **kwargs):\n super().__init__(*args, **kwargs)\n self.navigationController = navigationController\n self.slidePositionController = slidePositionController\n self.add_z_buttons = add_z_buttons\n self.initUI()\n\n def initUI(self):\n layout = QHBoxLayout()\n layout.setContentsMargins(5, 2, 5, 4) # Reduce vertical margins to make it thinner\n\n # Move to Loading Position button\n self.btn_load_slide = QPushButton('Move To Loading Position')\n self.btn_load_slide.setStyleSheet(\"background-color: #C2C2FF\")\n self.btn_load_slide.clicked.connect(self.switch_position)\n\n # Click to Move checkbox\n self.checkbox_clickToMove = QCheckBox('Click to Move')\n self.checkbox_clickToMove.setChecked(False)\n\n # Home Z and Zero Z\n if self.add_z_buttons:\n if self.slidePositionController is not None:\n layout.addWidget(self.btn_load_slide)\n layout.addSpacing(10)\n\n self.btn_home_Z = QPushButton('Home Z')\n self.btn_home_Z.clicked.connect(self.home_z)\n layout.addWidget(self.btn_home_Z)\n layout.addSpacing(20)\n\n self.btn_zero_Z = QPushButton('Zero Z')\n self.btn_zero_Z.clicked.connect(self.zero_z)\n layout.addWidget(self.btn_zero_Z)\n layout.addSpacing(20)\n\n if self.navigationController is not None:\n layout.addWidget(self.checkbox_clickToMove)\n layout.addSpacing(10)\n\n # X position\n x_label = QLabel('X:')\n self.label_Xpos = QLabel('00.000 mm')\n self.label_Xpos.setFixedWidth(self.label_Xpos.sizeHint().width())\n #self.label_Xpos.setFrameStyle(QFrame.Panel | QFrame.Sunken)\n\n # Y position\n y_label = QLabel('Y:')\n self.label_Ypos = QLabel('00.000 mm')\n self.label_Ypos.setFixedWidth(self.label_Ypos.sizeHint().width())\n #self.label_Ypos.setFrameStyle(QFrame.Panel | QFrame.Sunken)\n\n # Z position\n z_label = QLabel('Z:')\n self.label_Zpos = QLabel('0000.000 μm')\n self.label_Zpos.setFixedWidth(self.label_Zpos.sizeHint().width())\n #self.label_Zpos.setFrameStyle(QFrame.Panel | QFrame.Sunken)\n\n # Add widgets to layout\n layout.addStretch(1)\n layout.addSpacing(10)\n layout.addWidget(x_label)\n layout.addWidget(self.label_Xpos)\n layout.addSpacing(10)\n layout.addWidget(y_label)\n layout.addWidget(self.label_Ypos)\n layout.addSpacing(10)\n layout.addWidget(z_label)\n layout.addWidget(self.label_Zpos)\n layout.addSpacing(10)\n layout.addStretch(1)\n\n self.setLayout(layout)\n self.setFixedHeight(self.sizeHint().height()) # Set fixed height to make it as thin as possible\n self.connect_signals()\n\n def update_x_position(self, x):\n self.label_Xpos.setText(f\"{x:.3f} mm\")\n\n def update_y_position(self, y):\n self.label_Ypos.setText(f\"{y:.3f} mm\")\n\n def update_z_position(self, z):\n self.label_Zpos.setText(f\"{z:.3f} μm\")\n\n def home_z(self):\n msg = QMessageBox()\n msg.setIcon(QMessageBox.Information)\n msg.setText(\"Confirm your action\")\n msg.setInformativeText(\"Click OK to run homing\\n(Sets current Z to 0 μm)\")\n msg.setWindowTitle(\"Confirmation\")\n msg.setStandardButtons(QMessageBox.Ok | QMessageBox.Cancel)\n msg.setDefaultButton(QMessageBox.Cancel)\n retval = msg.exec_()\n if QMessageBox.Ok == retval:\n self.navigationController.home_z()\n\n def zero_z(self):\n msg = QMessageBox()\n msg.setIcon(QMessageBox.Information)\n msg.setText(\"Confirm your action\")\n msg.setInformativeText(\"Click OK to zero\\n(Moves Z to 0 μm)\")\n msg.setWindowTitle(\"Confirmation\")\n msg.setStandardButtons(QMessageBox.Ok | QMessageBox.Cancel)\n msg.setDefaultButton(QMessageBox.Cancel)\n retval = msg.exec_()\n if QMessageBox.Ok == retval:\n self.navigationController.zero_z()\n\n def switch_position(self):\n if self.btn_load_slide.text() == 'Move To Loading Position':\n self.slidePositionController.move_to_slide_loading_position()\n else:\n self.slidePositionController.move_to_slide_scanning_position()\n self.btn_load_slide.setEnabled(False)\n\n def slot_slide_loading_position_reached(self):\n self.btn_load_slide.setText('Move to Scanning Position')\n self.btn_load_slide.setStyleSheet(\"background-color: #C2FFC2\")\n self.btn_load_slide.setEnabled(True)\n\n def slot_slide_scanning_position_reached(self):\n self.btn_load_slide.setText('Move To Loading Position')\n self.btn_load_slide.setStyleSheet(\"background-color: #C2C2FF\")\n self.btn_load_slide.setEnabled(True)\n\n def replace_slide_controller(self, slidePositionController):\n self.slidePositionController = slidePositionController\n self.slidePositionController.signal_slide_loading_position_reached.connect(self.slot_slide_loading_position_reached)\n self.slidePositionController.signal_slide_scanning_position_reached.connect(self.slot_slide_scanning_position_reached)\n\n def connect_signals(self):\n if self.navigationController is not None:\n self.checkbox_clickToMove.stateChanged.connect(self.navigationController.set_flag_click_to_move)\n if self.slidePositionController is not None:\n self.slidePositionController.signal_slide_loading_position_reached.connect(self.slot_slide_loading_position_reached)\n self.slidePositionController.signal_slide_scanning_position_reached.connect(self.slot_slide_scanning_position_reached)\n\n\nclass DACControWidget(QFrame):\n def __init__(self, microcontroller ,*args, **kwargs):\n super().__init__(*args, **kwargs)\n self.microcontroller = microcontroller\n self.add_components()\n self.setFrameStyle(QFrame.Panel | QFrame.Raised)\n\n def add_components(self):\n self.slider_DAC0 = QSlider(Qt.Horizontal)\n self.slider_DAC0.setTickPosition(QSlider.TicksBelow)\n self.slider_DAC0.setMinimum(0)\n self.slider_DAC0.setMaximum(100)\n self.slider_DAC0.setSingleStep(1)\n self.slider_DAC0.setValue(0)\n\n self.entry_DAC0 = QDoubleSpinBox()\n self.entry_DAC0.setMinimum(0)\n self.entry_DAC0.setMaximum(100)\n self.entry_DAC0.setSingleStep(0.1)\n self.entry_DAC0.setValue(0)\n self.entry_DAC0.setKeyboardTracking(False)\n\n self.slider_DAC1 = QSlider(Qt.Horizontal)\n self.slider_DAC1.setTickPosition(QSlider.TicksBelow)\n self.slider_DAC1.setMinimum(0)\n self.slider_DAC1.setMaximum(100)\n self.slider_DAC1.setValue(0)\n self.slider_DAC1.setSingleStep(1)\n\n self.entry_DAC1 = QDoubleSpinBox()\n self.entry_DAC1.setMinimum(0)\n self.entry_DAC1.setMaximum(100)\n self.entry_DAC1.setSingleStep(0.1)\n self.entry_DAC1.setValue(0)\n self.entry_DAC1.setKeyboardTracking(False)\n\n # connections\n self.entry_DAC0.valueChanged.connect(self.set_DAC0)\n self.entry_DAC0.valueChanged.connect(self.slider_DAC0.setValue)\n self.slider_DAC0.valueChanged.connect(self.entry_DAC0.setValue)\n self.entry_DAC1.valueChanged.connect(self.set_DAC1)\n self.entry_DAC1.valueChanged.connect(self.slider_DAC1.setValue)\n self.slider_DAC1.valueChanged.connect(self.entry_DAC1.setValue)\n\n # layout\n grid_line1 = QHBoxLayout()\n grid_line1.addWidget(QLabel('DAC0'))\n grid_line1.addWidget(self.slider_DAC0)\n grid_line1.addWidget(self.entry_DAC0)\n grid_line1.addWidget(QLabel('DAC1'))\n grid_line1.addWidget(self.slider_DAC1)\n grid_line1.addWidget(self.entry_DAC1)\n\n self.grid = QGridLayout()\n self.grid.addLayout(grid_line1,1,0)\n self.setLayout(self.grid)\n\n def set_DAC0(self,value):\n self.microcontroller.analog_write_onboard_DAC(0,round(value*65535/100))\n\n def set_DAC1(self,value):\n self.microcontroller.analog_write_onboard_DAC(1,round(value*65535/100))\n\n\nclass AutoFocusWidget(QFrame):\n signal_autoLevelSetting = Signal(bool)\n\n def __init__(self, autofocusController, main=None, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.autofocusController = autofocusController\n self.add_components()\n self.setFrameStyle(QFrame.Panel | QFrame.Raised)\n\n def add_components(self):\n self.entry_delta = QDoubleSpinBox()\n self.entry_delta.setMinimum(0)\n self.entry_delta.setMaximum(20)\n self.entry_delta.setSingleStep(0.2)\n self.entry_delta.setDecimals(3)\n self.entry_delta.setSuffix(' μm')\n self.entry_delta.setValue(1.524)\n self.entry_delta.setKeyboardTracking(False)\n self.entry_delta.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n self.autofocusController.set_deltaZ(1.524)\n\n self.entry_N = QSpinBox()\n self.entry_N.setMinimum(3)\n self.entry_N.setMaximum(10000)\n self.entry_N.setFixedWidth(self.entry_N.sizeHint().width())\n self.entry_N.setMaximum(20)\n self.entry_N.setSingleStep(1)\n self.entry_N.setValue(10)\n self.entry_N.setKeyboardTracking(False)\n self.entry_N.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n self.autofocusController.set_N(10)\n\n self.btn_autofocus = QPushButton('Autofocus')\n self.btn_autofocus.setDefault(False)\n self.btn_autofocus.setCheckable(True)\n self.btn_autofocus.setChecked(False)\n\n self.btn_autolevel = QPushButton('Autolevel')\n self.btn_autolevel.setCheckable(True)\n self.btn_autolevel.setChecked(False)\n self.btn_autolevel.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n\n # layout\n self.grid = QVBoxLayout()\n grid_line0 = QHBoxLayout()\n grid_line0.addWidget(QLabel('\\u0394 Z'))\n grid_line0.addWidget(self.entry_delta)\n grid_line0.addSpacing(20)\n grid_line0.addWidget(QLabel('# of Z-Planes'))\n grid_line0.addWidget(self.entry_N)\n grid_line0.addSpacing(20)\n grid_line0.addWidget(self.btn_autolevel)\n\n self.grid.addLayout(grid_line0)\n self.grid.addWidget(self.btn_autofocus)\n self.setLayout(self.grid)\n\n # connections\n self.btn_autofocus.toggled.connect(lambda : self.autofocusController.autofocus(False))\n self.btn_autolevel.toggled.connect(self.signal_autoLevelSetting.emit)\n self.entry_delta.valueChanged.connect(self.set_deltaZ)\n self.entry_N.valueChanged.connect(self.autofocusController.set_N)\n self.autofocusController.autofocusFinished.connect(self.autofocus_is_finished)\n\n def set_deltaZ(self,value):\n mm_per_ustep = self.autofocusController.navigationController.get_mm_per_ustep_Z()\n deltaZ = round(value/1000/mm_per_ustep)*mm_per_ustep*1000\n self.entry_delta.setValue(deltaZ)\n self.autofocusController.set_deltaZ(deltaZ)\n\n def autofocus_is_finished(self):\n self.btn_autofocus.setChecked(False)\n\n\nclass FilterControllerWidget(QFrame):\n def __init__(self, filterController, liveController, main=None, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.filterController = filterController\n self.liveController = liveController\n self.add_components()\n self.setFrameStyle(QFrame.Panel | QFrame.Raised)\n\n def add_components(self):\n self.comboBox = QComboBox()\n for i in range(1, 8): # Assuming 7 filter positions\n self.comboBox.addItem(f\"Position {i}\")\n self.checkBox = QCheckBox(\"Disable filter wheel movement on changing Microscope Configuration\", self)\n\n layout = QGridLayout()\n layout.addWidget(QLabel('Filter wheel position:'), 0,0)\n layout.addWidget(self.comboBox, 0,1)\n layout.addWidget(self.checkBox, 2,0)\n\n self.setLayout(layout)\n\n self.comboBox.currentIndexChanged.connect(self.on_selection_change) # Connecting to selection change\n self.checkBox.stateChanged.connect(self.disable_movement_by_switching_channels)\n\n def on_selection_change(self, index):\n # The 'index' parameter is the new index of the combo box\n if index >= 0 and index <= 7: # Making sure the index is valid\n self.filterController.set_emission_filter(index+1)\n\n def disable_movement_by_switching_channels(self, state):\n if state:\n self.liveController.enable_channel_auto_filter_switching = False\n else:\n self.liveController.enable_channel_auto_filter_switching = True\n\n\nclass StatsDisplayWidget(QFrame):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.initUI()\n self.setFrameStyle(QFrame.Panel | QFrame.Raised)\n\n def initUI(self):\n self.layout = QVBoxLayout()\n self.table_widget = QTableWidget()\n self.table_widget.setColumnCount(2)\n self.table_widget.verticalHeader().hide()\n self.table_widget.horizontalHeader().hide()\n self.table_widget.horizontalHeader().setSectionResizeMode(QHeaderView.ResizeToContents)\n self.layout.addWidget(self.table_widget)\n self.setLayout(self.layout)\n\n def display_stats(self, stats):\n print(\"displaying parasite stats\")\n locale.setlocale(locale.LC_ALL, '')\n self.table_widget.setRowCount(len(stats))\n row = 0\n for key, value in stats.items():\n key_item = QTableWidgetItem(str(key))\n value_item = None\n try:\n value_item = QTableWidgetItem(f'{value:n}')\n except:\n value_item = QTableWidgetItem(str(value))\n self.table_widget.setItem(row,0,key_item)\n self.table_widget.setItem(row,1,value_item)\n row+=1\n\n\nclass MultiPointWidget(QFrame):\n\n signal_acquisition_started = Signal(bool)\n signal_acquisition_channels = Signal(list)\n signal_acquisition_z_levels = Signal(int)\n signal_acquisition_shape = Signal(int, int, int, float, float, float)\n signal_stitcher_widget = Signal(bool)\n\n def __init__(self, multipointController, configurationManager = None, main=None, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.multipointController = multipointController\n self.configurationManager = configurationManager\n self.well_selected = False\n self.base_path_is_set = False\n self.add_components()\n self.setFrameStyle(QFrame.Panel | QFrame.Raised)\n\n def add_components(self):\n\n self.btn_setSavingDir = QPushButton('Browse')\n self.btn_setSavingDir.setDefault(False)\n self.btn_setSavingDir.setIcon(QIcon('icon/folder.png'))\n\n self.lineEdit_savingDir = QLineEdit()\n self.lineEdit_savingDir.setReadOnly(True)\n self.lineEdit_savingDir.setText('Choose a base saving directory')\n\n self.lineEdit_savingDir.setText(DEFAULT_SAVING_PATH)\n self.multipointController.set_base_path(DEFAULT_SAVING_PATH)\n self.base_path_is_set = True\n\n self.lineEdit_experimentID = QLineEdit()\n\n self.entry_deltaX = QDoubleSpinBox()\n self.entry_deltaX.setMinimum(0)\n self.entry_deltaX.setMaximum(5)\n self.entry_deltaX.setSingleStep(0.1)\n self.entry_deltaX.setValue(Acquisition.DX)\n self.entry_deltaX.setDecimals(3)\n self.entry_deltaX.setSuffix(' mm')\n self.entry_deltaX.setKeyboardTracking(False)\n\n self.entry_NX = QSpinBox()\n self.entry_NX.setMinimum(1)\n self.entry_NX.setMaximum(50)\n self.entry_NX.setSingleStep(1)\n self.entry_NX.setValue(Acquisition.NX)\n self.entry_NX.setKeyboardTracking(False)\n\n self.entry_deltaY = QDoubleSpinBox()\n self.entry_deltaY.setMinimum(0)\n self.entry_deltaY.setMaximum(5)\n self.entry_deltaY.setSingleStep(0.1)\n self.entry_deltaY.setValue(Acquisition.DX)\n self.entry_deltaY.setDecimals(3)\n self.entry_deltaY.setSuffix(' mm')\n self.entry_deltaY.setKeyboardTracking(False)\n\n self.entry_NY = QSpinBox()\n self.entry_NY.setMinimum(1)\n self.entry_NY.setMaximum(50)\n self.entry_NY.setSingleStep(1)\n self.entry_NY.setValue(Acquisition.NY)\n self.entry_NY.setKeyboardTracking(False)\n\n self.entry_deltaZ = QDoubleSpinBox()\n self.entry_deltaZ.setMinimum(0)\n self.entry_deltaZ.setMaximum(1000)\n self.entry_deltaZ.setSingleStep(0.2)\n self.entry_deltaZ.setValue(Acquisition.DZ)\n self.entry_deltaZ.setDecimals(3)\n self.entry_deltaZ.setSuffix(' μm')\n self.entry_deltaZ.setKeyboardTracking(False)\n\n self.entry_NZ = QSpinBox()\n self.entry_NZ.setMinimum(1)\n self.entry_NZ.setMaximum(2000)\n self.entry_NZ.setSingleStep(1)\n self.entry_NZ.setValue(1)\n self.entry_NZ.setKeyboardTracking(False)\n\n self.entry_dt = QDoubleSpinBox()\n self.entry_dt.setMinimum(0)\n self.entry_dt.setMaximum(12*3600)\n self.entry_dt.setSingleStep(1)\n self.entry_dt.setValue(0)\n self.entry_dt.setSuffix(' s')\n self.entry_dt.setKeyboardTracking(False)\n\n self.entry_Nt = QSpinBox()\n self.entry_Nt.setMinimum(1)\n self.entry_Nt.setMaximum(5000) # @@@ to be changed\n self.entry_Nt.setSingleStep(1)\n self.entry_Nt.setValue(1)\n self.entry_Nt.setKeyboardTracking(False)\n\n self.list_configurations = QListWidget()\n for microscope_configuration in self.configurationManager.configurations:\n self.list_configurations.addItems([microscope_configuration.name])\n self.list_configurations.setSelectionMode(QAbstractItemView.MultiSelection) # ref: https://doc.qt.io/qt-5/qabstractitemview.html#SelectionMode-enum\n\n self.checkbox_withAutofocus = QCheckBox('Contrast AF')\n self.checkbox_withAutofocus.setChecked(MULTIPOINT_CONTRAST_AUTOFOCUS_ENABLE_BY_DEFAULT)\n self.multipointController.set_af_flag(MULTIPOINT_CONTRAST_AUTOFOCUS_ENABLE_BY_DEFAULT)\n\n self.checkbox_genFocusMap = QCheckBox('Focus Map')\n self.checkbox_genFocusMap.setChecked(False)\n\n self.checkbox_withReflectionAutofocus = QCheckBox('Reflection AF')\n self.checkbox_withReflectionAutofocus.setChecked(MULTIPOINT_REFLECTION_AUTOFOCUS_ENABLE_BY_DEFAULT)\n\n self.checkbox_stitchOutput = QCheckBox('Stitch Scans')\n self.checkbox_stitchOutput.setChecked(False)\n\n self.multipointController.set_reflection_af_flag(MULTIPOINT_REFLECTION_AUTOFOCUS_ENABLE_BY_DEFAULT)\n\n self.btn_startAcquisition = QPushButton('Start\\n Acquisition ')\n self.btn_startAcquisition.setStyleSheet(\"background-color: #C2C2FF\")\n self.btn_startAcquisition.setCheckable(True)\n self.btn_startAcquisition.setChecked(False)\n\n # layout\n grid_line0 = QGridLayout()\n grid_line0.addWidget(QLabel('Saving Path'))\n grid_line0.addWidget(self.lineEdit_savingDir, 0,1)\n grid_line0.addWidget(self.btn_setSavingDir, 0,2)\n\n grid_line1 = QGridLayout()\n grid_line1.addWidget(QLabel('Experiment ID'), 0,0)\n grid_line1.addWidget(self.lineEdit_experimentID,0,1)\n\n grid_line2 = QGridLayout()\n grid_line2.addWidget(QLabel('dx'), 0,0)\n grid_line2.addWidget(self.entry_deltaX, 0,1)\n grid_line2.addWidget(QLabel('Nx'), 0,3)\n grid_line2.addWidget(self.entry_NX, 0,4)\n grid_line2.addWidget(QLabel('dy'), 0,6)\n grid_line2.addWidget(self.entry_deltaY, 0,7)\n grid_line2.addWidget(QLabel('Ny'), 0,9)\n grid_line2.addWidget(self.entry_NY, 0,10)\n\n grid_line2.addWidget(QLabel('dz'), 1,0)\n grid_line2.addWidget(self.entry_deltaZ, 1,1)\n grid_line2.addWidget(QLabel('Nz'), 1,3)\n grid_line2.addWidget(self.entry_NZ, 1,4)\n grid_line2.addWidget(QLabel('dt'), 1,6)\n grid_line2.addWidget(self.entry_dt, 1,7)\n grid_line2.addWidget(QLabel('Nt'), 1,9)\n grid_line2.addWidget(self.entry_Nt, 1,10)\n\n grid_line2.setColumnStretch(2, 1)\n grid_line2.setColumnStretch(5, 1)\n grid_line2.setColumnStretch(8, 1)\n\n grid_af = QGridLayout()\n grid_af.addItem(QSpacerItem(7, 1, QSizePolicy.Fixed, QSizePolicy.Minimum), 0, 0)\n grid_af.addWidget(self.checkbox_withAutofocus,0,1)\n if SUPPORT_LASER_AUTOFOCUS:\n grid_af.addWidget(self.checkbox_withReflectionAutofocus,1,1)\n grid_af.addWidget(self.checkbox_genFocusMap,2,1)\n if ENABLE_STITCHER:\n grid_af.addWidget(self.checkbox_stitchOutput,3,1)\n grid_af.addItem(QSpacerItem(6, 1, QSizePolicy.Fixed, QSizePolicy.Minimum), 0, 2)\n\n grid_line3 = QHBoxLayout()\n grid_line3.addWidget(self.list_configurations, 2)\n # grid_line3.addWidget(self.checkbox_withAutofocus)\n grid_line3.addLayout(grid_af, 1)\n grid_line3.addWidget(self.btn_startAcquisition, 1)\n\n self.grid = QGridLayout()\n self.grid.addLayout(grid_line0,0,0)\n self.grid.addLayout(grid_line1,1,0)\n self.grid.addLayout(grid_line2,2,0)\n self.grid.addLayout(grid_line3,3,0)\n self.setLayout(self.grid)\n\n # add and display a timer - to be implemented\n # self.timer = QTimer()\n\n # connections\n self.entry_deltaX.valueChanged.connect(self.set_deltaX)\n self.entry_deltaY.valueChanged.connect(self.set_deltaY)\n self.entry_deltaZ.valueChanged.connect(self.set_deltaZ)\n self.entry_dt.valueChanged.connect(self.multipointController.set_deltat)\n self.entry_NX.valueChanged.connect(self.multipointController.set_NX)\n self.entry_NY.valueChanged.connect(self.multipointController.set_NY)\n self.entry_NZ.valueChanged.connect(self.multipointController.set_NZ)\n self.entry_NZ.valueChanged.connect(self.signal_acquisition_z_levels.emit)\n self.entry_Nt.valueChanged.connect(self.multipointController.set_Nt)\n self.checkbox_withAutofocus.stateChanged.connect(self.multipointController.set_af_flag)\n self.checkbox_withReflectionAutofocus.stateChanged.connect(self.multipointController.set_reflection_af_flag)\n self.checkbox_genFocusMap.stateChanged.connect(self.multipointController.set_gen_focus_map_flag)\n self.checkbox_stitchOutput.toggled.connect(self.display_stitcher_widget)\n self.btn_setSavingDir.clicked.connect(self.set_saving_dir)\n self.btn_startAcquisition.clicked.connect(self.toggle_acquisition)\n self.multipointController.acquisitionFinished.connect(self.acquisition_is_finished)\n self.list_configurations.itemSelectionChanged.connect(self.emit_selected_channels)\n\n def set_deltaX(self,value):\n mm_per_ustep = self.multipointController.navigationController.get_mm_per_ustep_X()\n deltaX = round(value/mm_per_ustep)*mm_per_ustep\n self.entry_deltaX.setValue(deltaX)\n self.multipointController.set_deltaX(deltaX)\n\n def set_deltaY(self,value):\n mm_per_ustep = self.multipointController.navigationController.get_mm_per_ustep_Y()\n deltaY = round(value/mm_per_ustep)*mm_per_ustep\n self.entry_deltaY.setValue(deltaY)\n self.multipointController.set_deltaY(deltaY)\n\n def set_deltaZ(self,value):\n mm_per_ustep = self.multipointController.navigationController.get_mm_per_ustep_Z()\n deltaZ = round(value/1000/mm_per_ustep)*mm_per_ustep*1000\n self.entry_deltaZ.setValue(deltaZ)\n self.multipointController.set_deltaZ(deltaZ)\n\n def set_saving_dir(self):\n dialog = QFileDialog()\n save_dir_base = dialog.getExistingDirectory(None, \"Select Folder\")\n self.multipointController.set_base_path(save_dir_base)\n self.lineEdit_savingDir.setText(save_dir_base)\n self.base_path_is_set = True\n\n def set_well_selected(self, selected):\n self.well_selected = selected\n\n def emit_selected_channels(self):\n selected_channels = [item.text() for item in self.list_configurations.selectedItems()]\n print(selected_channels)\n self.signal_acquisition_channels.emit(selected_channels)\n\n def toggle_acquisition(self,pressed):\n if self.base_path_is_set == False:\n self.btn_startAcquisition.setChecked(False)\n msg = QMessageBox()\n msg.setText(\"Please choose base saving directory first\")\n msg.exec_()\n return\n if self.well_selected == False and self.multipointController.scanCoordinates.format != 0:\n self.btn_startAcquisition.setChecked(False)\n msg = QMessageBox()\n msg.setText(\"Please select a well to scan first\")\n msg.exec_()\n return\n if not self.list_configurations.selectedItems(): # no channel selected\n self.btn_startAcquisition.setChecked(False)\n msg = QMessageBox()\n msg.setText(\"Please select at least one imaging channel first\")\n msg.exec_()\n return\n if pressed:\n # @@@ to do: add a widgetManger to enable and disable widget\n # @@@ to do: emit signal to widgetManager to disable other widgets\n self.setEnabled_all(False)\n\n # set parameters\n if self.multipointController.scanCoordinates is not None:\n self.multipointController.scanCoordinates.grid_skip_positions = []\n self.multipointController.set_deltaX(self.entry_deltaX.value())\n self.multipointController.set_deltaY(self.entry_deltaY.value())\n self.multipointController.set_deltaZ(self.entry_deltaZ.value())\n self.multipointController.set_deltat(self.entry_dt.value())\n self.multipointController.set_NX(self.entry_NX.value())\n self.multipointController.set_NY(self.entry_NY.value())\n self.multipointController.set_NZ(self.entry_NZ.value())\n self.multipointController.set_Nt(self.entry_Nt.value())\n self.multipointController.set_af_flag(self.checkbox_withAutofocus.isChecked())\n self.multipointController.set_reflection_af_flag(self.checkbox_withReflectionAutofocus.isChecked())\n self.multipointController.set_base_path(self.lineEdit_savingDir.text())\n self.multipointController.set_selected_configurations((item.text() for item in self.list_configurations.selectedItems()))\n self.multipointController.start_new_experiment(self.lineEdit_experimentID.text())\n\n # emit acquisition data\n self.signal_acquisition_started.emit(True)\n self.signal_acquisition_shape.emit(self.entry_NX.value(),\n self.entry_NY.value(),\n self.entry_NZ.value(),\n self.entry_deltaX.value(),\n self.entry_deltaY.value(),\n self.entry_deltaZ.value())\n\n self.multipointController.run_acquisition()\n else:\n self.multipointController.request_abort_aquisition()\n self.setEnabled_all(True)\n\n def acquisition_is_finished(self):\n self.signal_acquisition_started.emit(False)\n self.btn_startAcquisition.setChecked(False)\n self.setEnabled_all(True)\n\n def setEnabled_all(self,enabled,exclude_btn_startAcquisition=True):\n self.btn_setSavingDir.setEnabled(enabled)\n self.lineEdit_savingDir.setEnabled(enabled)\n self.lineEdit_experimentID.setEnabled(enabled)\n self.entry_deltaX.setEnabled(enabled)\n self.entry_NX.setEnabled(enabled)\n self.entry_deltaY.setEnabled(enabled)\n self.entry_NY.setEnabled(enabled)\n self.entry_deltaZ.setEnabled(enabled)\n self.entry_NZ.setEnabled(enabled)\n self.entry_dt.setEnabled(enabled)\n self.entry_Nt.setEnabled(enabled)\n self.list_configurations.setEnabled(enabled)\n self.checkbox_withAutofocus.setEnabled(enabled)\n self.checkbox_withReflectionAutofocus.setEnabled(enabled)\n self.checkbox_genFocusMap.setEnabled(enabled)\n self.checkbox_stitchOutput.setEnabled(enabled)\n if exclude_btn_startAcquisition is not True:\n self.btn_startAcquisition.setEnabled(enabled)\n\n def display_stitcher_widget(self, checked):\n self.signal_stitcher_widget.emit(checked)\n\n def disable_the_start_aquisition_button(self):\n self.btn_startAcquisition.setEnabled(False)\n\n def enable_the_start_aquisition_button(self):\n self.btn_startAcquisition.setEnabled(True)\n\n\nclass MultiPointWidget2(QFrame):\n\n signal_acquisition_started = Signal(bool)\n signal_acquisition_channels = Signal(list)\n signal_acquisition_z_levels = Signal(int)\n signal_acquisition_shape = Signal(int, int, int, float, float, float)\n signal_stitcher_widget = Signal(bool)\n\n def __init__(self, navigationController, navigationViewer, multipointController, configurationManager = None, main=None, scanCoordinates=None, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.last_used_locations = None\n self.last_used_location_ids = None\n self.multipointController = multipointController\n self.configurationManager = configurationManager\n self.navigationController = navigationController\n self.navigationViewer = navigationViewer\n self.scanCoordinates = scanCoordinates\n self.base_path_is_set = False\n self.location_list = np.empty((0, 3), dtype=float)\n self.location_ids = np.empty((0,), dtype='<U20')\n self.add_components()\n self.setFrameStyle(QFrame.Panel | QFrame.Raised)\n self.acquisition_in_place=False\n\n def add_components(self):\n\n self.btn_setSavingDir = QPushButton('Browse')\n self.btn_setSavingDir.setDefault(False)\n self.btn_setSavingDir.setIcon(QIcon('icon/folder.png'))\n\n self.lineEdit_savingDir = QLineEdit()\n self.lineEdit_savingDir.setReadOnly(True)\n self.lineEdit_savingDir.setText('Choose a base saving directory')\n\n self.lineEdit_savingDir.setText(DEFAULT_SAVING_PATH)\n self.multipointController.set_base_path(DEFAULT_SAVING_PATH)\n self.base_path_is_set = True\n\n self.lineEdit_experimentID = QLineEdit()\n self.lineEdit_experimentID.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n self.lineEdit_experimentID.setFixedWidth(96)\n\n self.dropdown_location_list = QComboBox()\n self.dropdown_location_list.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n self.btn_add = QPushButton('Add')\n self.btn_remove = QPushButton('Remove')\n self.btn_previous = QPushButton('Previous')\n self.btn_next = QPushButton('Next')\n self.btn_clear = QPushButton('Clear')\n\n self.btn_load_last_executed = QPushButton('Prev Used Locations')\n\n self.btn_export_locations = QPushButton('Export Location List')\n self.btn_import_locations = QPushButton('Import Location List')\n\n # editable points table\n self.table_location_list = QTableWidget()\n self.table_location_list.setColumnCount(4)\n header_labels = ['x', 'y', 'z', 'ID']\n self.table_location_list.setHorizontalHeaderLabels(header_labels)\n self.btn_show_table_location_list = QPushButton('Edit') # Open / Edit\n\n self.entry_deltaX = QDoubleSpinBox()\n self.entry_deltaX.setMinimum(0)\n self.entry_deltaX.setMaximum(1000)\n self.entry_deltaX.setDecimals(3)\n self.entry_deltaX.setSuffix(' mm')\n self.entry_deltaX.setMinimumWidth(self.entry_deltaX.sizeHint().width())\n self.entry_deltaX.setMaximum(5)\n self.entry_deltaX.setSingleStep(0.1)\n self.entry_deltaX.setValue(Acquisition.DX)\n self.entry_deltaX.setKeyboardTracking(False)\n #self.entry_deltaX.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n\n self.entry_NX = QSpinBox()\n self.entry_NX.setMinimum(1)\n self.entry_NX.setMaximum(1000)\n self.entry_NX.setMinimumWidth(self.entry_NX.sizeHint().width())\n self.entry_NX.setMaximum(50)\n self.entry_NX.setSingleStep(1)\n self.entry_NX.setValue(1)\n self.entry_NX.setKeyboardTracking(False)\n self.entry_NX.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n\n self.entry_deltaY = QDoubleSpinBox()\n self.entry_deltaY.setMinimum(0)\n self.entry_deltaY.setMaximum(1000)\n self.entry_deltaY.setDecimals(3)\n self.entry_deltaY.setSuffix(' mm')\n self.entry_deltaY.setMinimumWidth(self.entry_deltaY.sizeHint().width())\n self.entry_deltaY.setMaximum(5)\n self.entry_deltaY.setSingleStep(0.1)\n self.entry_deltaY.setValue(Acquisition.DX)\n self.entry_deltaY.setKeyboardTracking(False)\n #self.entry_deltaY.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n\n self.entry_NY = QSpinBox()\n self.entry_NY.setMinimum(1)\n self.entry_NY.setMaximum(1000)\n self.entry_NY.setMinimumWidth(self.entry_NX.sizeHint().width())\n self.entry_NY.setMaximum(50)\n self.entry_NY.setSingleStep(1)\n self.entry_NY.setValue(1)\n self.entry_NY.setKeyboardTracking(False)\n self.entry_NY.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n\n self.entry_deltaZ = QDoubleSpinBox()\n self.entry_deltaZ.setMinimum(0)\n self.entry_deltaZ.setMaximum(1000)\n self.entry_deltaZ.setSingleStep(0.2)\n self.entry_deltaZ.setValue(Acquisition.DZ)\n self.entry_deltaZ.setDecimals(3)\n self.entry_deltaZ.setSuffix(' μm')\n self.entry_deltaZ.setKeyboardTracking(False)\n #self.entry_deltaZ.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n\n self.entry_NZ = QSpinBox()\n self.entry_NZ.setMinimum(1)\n self.entry_NZ.setMaximum(2000)\n self.entry_NZ.setSingleStep(1)\n self.entry_NZ.setValue(1)\n self.entry_NZ.setKeyboardTracking(False)\n\n self.entry_dt = QDoubleSpinBox()\n self.entry_dt.setMinimum(0)\n self.entry_dt.setMaximum(12*3600)\n self.entry_dt.setSingleStep(1)\n self.entry_dt.setValue(0)\n self.entry_dt.setSuffix(' s')\n self.entry_dt.setKeyboardTracking(False)\n #self.entry_dt.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n\n self.entry_Nt = QSpinBox()\n self.entry_Nt.setMinimum(1)\n self.entry_Nt.setMaximum(5000) # @@@ to be changed\n self.entry_Nt.setSingleStep(1)\n self.entry_Nt.setValue(1)\n self.entry_Nt.setKeyboardTracking(False)\n\n self.list_configurations = QListWidget()\n for microscope_configuration in self.configurationManager.configurations:\n self.list_configurations.addItems([microscope_configuration.name])\n self.list_configurations.setSelectionMode(QAbstractItemView.MultiSelection) # ref: https://doc.qt.io/qt-5/qabstractitemview.html#SelectionMode-enum\n\n self.checkbox_withAutofocus = QCheckBox('Contrast AF')\n self.checkbox_withAutofocus.setChecked(MULTIPOINT_CONTRAST_AUTOFOCUS_ENABLE_BY_DEFAULT)\n self.multipointController.set_af_flag(MULTIPOINT_CONTRAST_AUTOFOCUS_ENABLE_BY_DEFAULT)\n\n self.checkbox_withReflectionAutofocus = QCheckBox('Reflection AF')\n self.checkbox_withReflectionAutofocus.setChecked(MULTIPOINT_REFLECTION_AUTOFOCUS_ENABLE_BY_DEFAULT)\n self.multipointController.set_reflection_af_flag(MULTIPOINT_REFLECTION_AUTOFOCUS_ENABLE_BY_DEFAULT)\n\n self.checkbox_genFocusMap = QCheckBox('Focus Map')\n self.checkbox_genFocusMap.setChecked(False)\n\n self.checkbox_usePiezo = QCheckBox('Piezo Z-Stack')\n self.checkbox_usePiezo.setChecked(MULTIPOINT_USE_PIEZO_FOR_ZSTACKS)\n\n self.checkbox_stitchOutput = QCheckBox('Stitch Scans')\n self.checkbox_stitchOutput.setChecked(False)\n\n self.checkbox_set_z_range = QCheckBox('Set Z-range')\n self.checkbox_set_z_range.toggled.connect(self.toggle_z_range_controls)\n\n # Add new components for Z-range\n self.entry_minZ = QDoubleSpinBox()\n self.entry_minZ.setMinimum(SOFTWARE_POS_LIMIT.Z_NEGATIVE * 1000) # Convert to μm\n self.entry_minZ.setMaximum(SOFTWARE_POS_LIMIT.Z_POSITIVE * 1000) # Convert to μm\n self.entry_minZ.setSingleStep(1) # Step by 1 μm\n self.entry_minZ.setValue(self.navigationController.z_pos_mm * 1000) # Set to current position\n self.entry_minZ.setSuffix(\" μm\")\n #self.entry_minZ.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n self.set_minZ_button = QPushButton('Set')\n self.set_minZ_button.clicked.connect(self.set_z_min)\n\n self.entry_maxZ = QDoubleSpinBox()\n self.entry_maxZ.setMinimum(SOFTWARE_POS_LIMIT.Z_NEGATIVE * 1000) # Convert to μm\n self.entry_maxZ.setMaximum(SOFTWARE_POS_LIMIT.Z_POSITIVE * 1000) # Convert to μm\n self.entry_maxZ.setSingleStep(1) # Step by 1 μm\n self.entry_maxZ.setValue(self.navigationController.z_pos_mm * 1000) # Set to current position\n self.entry_maxZ.setSuffix(\" μm\")\n #self.entry_maxZ.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n self.set_maxZ_button = QPushButton('Set')\n self.set_maxZ_button.clicked.connect(self.set_z_max)\n\n self.combobox_z_stack = QComboBox()\n self.combobox_z_stack.addItems(['From Bottom (Z-min)', 'From Center', 'From Top (Z-max)'])\n\n self.btn_startAcquisition = QPushButton('Start\\n Acquisition ')\n self.btn_startAcquisition.setStyleSheet(\"background-color: #C2C2FF\")\n self.btn_startAcquisition.setCheckable(True)\n self.btn_startAcquisition.setChecked(False)\n self.btn_startAcquisition.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n\n self.progress_label = QLabel('Region -/-')\n self.progress_bar = QProgressBar()\n self.eta_label = QLabel('--:--:--')\n self.progress_bar.setVisible(False)\n self.progress_label.setVisible(False)\n self.eta_label.setVisible(False)\n self.eta_timer = QTimer()\n\n # layout\n grid_line0 = QHBoxLayout()\n grid_line0.addWidget(QLabel('Saving Path'))\n grid_line0.addWidget(self.lineEdit_savingDir)\n grid_line0.addWidget(self.btn_setSavingDir)\n grid_line0.addWidget(QLabel('ID'))\n grid_line0.addWidget(self.lineEdit_experimentID)\n\n grid_line1 = QGridLayout()\n temp3 = QHBoxLayout()\n temp3.addWidget(QLabel('Location List'))\n temp3.addWidget(self.dropdown_location_list)\n grid_line1.addLayout(temp3, 0, 0, 1, 3) # Span across all columns except the last\n grid_line1.addWidget(self.btn_show_table_location_list, 0, 3) # Align with other buttons\n\n # Make all buttons span 2 columns for consistent width\n grid_line1.addWidget(self.btn_add, 1, 0)\n grid_line1.addWidget(self.btn_remove,1, 1)\n grid_line1.addWidget(self.btn_next,1, 2)\n grid_line1.addWidget(self.btn_clear, 1, 3)\n\n grid_line1.addWidget(self.btn_import_locations, 2, 0, 1, 2)\n grid_line1.addWidget(self.btn_export_locations, 2, 2, 1, 2)\n\n # Adjust the placement of labels and entries\n grid_line2 = QGridLayout()\n grid_line2.addWidget(QLabel('dx'), 3, 0)\n grid_line2.addWidget(self.entry_deltaX, 3, 1)\n grid_line2.addWidget(QLabel('Nx'), 3, 3)\n grid_line2.addWidget(self.entry_NX, 3, 4)\n\n grid_line2.addWidget(QLabel('dy'), 3, 6)\n grid_line2.addWidget(self.entry_deltaY, 3, 7)\n grid_line2.addWidget(QLabel('Ny'), 3, 9)\n grid_line2.addWidget(self.entry_NY, 3, 10)\n\n grid_line2.addWidget(QLabel('dz'), 4, 0)\n grid_line2.addWidget(self.entry_deltaZ, 4, 1)\n grid_line2.addWidget(QLabel('Nz'), 4, 3)\n grid_line2.addWidget(self.entry_NZ, 4, 4)\n\n grid_line2.addWidget(QLabel('dt'), 4, 6)\n grid_line2.addWidget(self.entry_dt, 4, 7)\n grid_line2.addWidget(QLabel('Nt'), 4, 9)\n grid_line2.addWidget(self.entry_Nt, 4, 10)\n\n self.z_min_layout = QHBoxLayout()\n self.z_min_layout.addWidget(self.set_minZ_button)\n self.z_min_layout.addWidget(QLabel('Z-min'))\n self.z_min_layout.addWidget(self.entry_minZ)\n\n self.z_max_layout = QHBoxLayout()\n self.z_max_layout.addWidget(self.set_maxZ_button)\n self.z_max_layout.addWidget(QLabel('Z-max'))\n self.z_max_layout.addWidget(self.entry_maxZ)\n\n grid_line2.addLayout(self.z_min_layout, 5, 0, 1, 5) # hide this in toggle\n grid_line2.addLayout(self.z_max_layout, 5, 6, 1, 5) # hide this in toggle\n\n #grid_line2.addLayout(self.z_range_layout, 5, 0, 1, 10)\n\n grid_af = QVBoxLayout()\n grid_af.addWidget(self.checkbox_withAutofocus)\n if SUPPORT_LASER_AUTOFOCUS:\n grid_af.addWidget(self.checkbox_withReflectionAutofocus)\n grid_af.addWidget(self.checkbox_genFocusMap)\n if ENABLE_OBJECTIVE_PIEZO:\n grid_af.addWidget(self.checkbox_usePiezo)\n grid_af.addWidget(self.checkbox_set_z_range)\n if ENABLE_STITCHER:\n grid_af.addWidget(self.checkbox_stitchOutput)\n\n grid_line2.addWidget(self.list_configurations,6,0,1,5)\n grid_line2.addLayout(grid_af,6,6,1,2)\n grid_line2.addWidget(self.btn_startAcquisition,6,8,1,3)\n\n grid_line2.setColumnStretch(2, 1)\n grid_line2.setColumnStretch(5, 1)\n grid_line2.setColumnStretch(8, 1)\n\n # Row : Progress Bar\n row_progress_layout = QHBoxLayout()\n row_progress_layout.addWidget(self.progress_label)\n row_progress_layout.addWidget(self.progress_bar)\n row_progress_layout.addWidget(self.eta_label)\n\n self.grid = QGridLayout()\n self.grid.addLayout(grid_line0,0,0)\n self.grid.addLayout(grid_line1,1,0)\n self.grid.addLayout(grid_line2,2,0)\n #self.grid.addLayout(grid_line3,4,0)\n self.grid.addLayout(row_progress_layout,3,0)\n self.setLayout(self.grid)\n\n # add and display a timer - to be implemented\n # self.timer = QTimer()\n\n # connections\n self.entry_deltaX.valueChanged.connect(self.set_deltaX)\n self.entry_deltaY.valueChanged.connect(self.set_deltaY)\n self.entry_deltaZ.valueChanged.connect(self.set_deltaZ)\n self.entry_dt.valueChanged.connect(self.multipointController.set_deltat)\n self.entry_NX.valueChanged.connect(self.multipointController.set_NX)\n self.entry_NY.valueChanged.connect(self.multipointController.set_NY)\n self.entry_NZ.valueChanged.connect(self.multipointController.set_NZ)\n self.entry_NZ.valueChanged.connect(self.signal_acquisition_z_levels.emit)\n self.entry_Nt.valueChanged.connect(self.multipointController.set_Nt)\n self.checkbox_genFocusMap.toggled.connect(self.multipointController.set_gen_focus_map_flag)\n self.checkbox_withAutofocus.toggled.connect(self.multipointController.set_af_flag)\n self.checkbox_withReflectionAutofocus.toggled.connect(self.multipointController.set_reflection_af_flag)\n self.checkbox_usePiezo.toggled.connect(self.multipointController.set_use_piezo)\n self.checkbox_stitchOutput.toggled.connect(self.display_stitcher_widget)\n self.btn_setSavingDir.clicked.connect(self.set_saving_dir)\n self.btn_startAcquisition.clicked.connect(self.toggle_acquisition)\n self.multipointController.acquisitionFinished.connect(self.acquisition_is_finished)\n self.list_configurations.itemSelectionChanged.connect(self.emit_selected_channels)\n #self.combobox_z_stack.currentIndexChanged.connect(self.signal_z_stacking.emit)\n\n self.multipointController.signal_acquisition_progress.connect(self.update_acquisition_progress)\n self.multipointController.signal_region_progress.connect(self.update_region_progress)\n self.signal_acquisition_started.connect(self.display_progress_bar)\n self.eta_timer.timeout.connect(self.update_eta_display)\n\n self.btn_add.clicked.connect(self.add_location)\n self.btn_remove.clicked.connect(self.remove_location)\n self.btn_previous.clicked.connect(self.previous)\n self.btn_next.clicked.connect(self.next)\n self.btn_clear.clicked.connect(self.clear)\n self.btn_load_last_executed.clicked.connect(self.load_last_used_locations)\n self.btn_export_locations.clicked.connect(self.export_location_list)\n self.btn_import_locations.clicked.connect(self.import_location_list)\n\n self.table_location_list.cellClicked.connect(self.cell_was_clicked)\n self.table_location_list.cellChanged.connect(self.cell_was_changed)\n self.btn_show_table_location_list.clicked.connect(self.table_location_list.show)\n self.dropdown_location_list.currentIndexChanged.connect(self.go_to)\n\n self.shortcut = QShortcut(QKeySequence(\";\"), self)\n self.shortcut.activated.connect(self.btn_add.click)\n\n self.toggle_z_range_controls(False)\n self.multipointController.set_use_piezo(self.checkbox_usePiezo.isChecked())\n\n def toggle_z_range_controls(self, state):\n is_visible = bool(state)\n\n # Hide/show widgets in z_min_layout\n for i in range(self.z_min_layout.count()):\n widget = self.z_min_layout.itemAt(i).widget()\n if widget is not None:\n widget.setVisible(is_visible)\n widget = self.z_max_layout.itemAt(i).widget()\n if widget is not None:\n widget.setVisible(is_visible)\n\n # Enable/disable NZ entry based on the inverse of is_visible\n self.entry_NZ.setEnabled(not is_visible)\n\n if not is_visible:\n try:\n self.entry_minZ.valueChanged.disconnect(self.update_z_max)\n self.entry_maxZ.valueChanged.disconnect(self.update_z_min)\n self.entry_minZ.valueChanged.disconnect(self.update_Nz)\n self.entry_maxZ.valueChanged.disconnect(self.update_Nz)\n self.entry_deltaZ.valueChanged.disconnect(self.update_Nz)\n except:\n pass\n # When Z-range is not specified, set Z-min and Z-max to current Z position\n current_z = self.navigationController.z_pos_mm * 1000\n self.entry_minZ.setValue(current_z)\n self.entry_maxZ.setValue(current_z)\n else:\n self.entry_minZ.valueChanged.connect(self.update_z_max)\n self.entry_maxZ.valueChanged.connect(self.update_z_min)\n self.entry_minZ.valueChanged.connect(self.update_Nz)\n self.entry_maxZ.valueChanged.connect(self.update_Nz)\n self.entry_deltaZ.valueChanged.connect(self.update_Nz)\n\n # Update the layout\n self.grid.update()\n self.updateGeometry()\n self.update()\n\n def init_z(self, z_pos_mm=None):\n if z_pos_mm is None:\n z_pos_mm = self.navigationController.z_pos_mm\n\n # block entry update signals\n self.entry_minZ.blockSignals(True)\n self.entry_maxZ.blockSignals(True)\n\n # set entry range values bith to current z pos\n self.entry_minZ.setValue(z_pos_mm*1000)\n self.entry_maxZ.setValue(z_pos_mm*1000)\n print(\"init z-level flexible:\", self.entry_minZ.value())\n\n # reallow updates from entry sinals (signal enforces min <= max when we update either entry)\n self.entry_minZ.blockSignals(False)\n self.entry_maxZ.blockSignals(False)\n\n def set_z_min(self):\n z_value = self.navigationController.z_pos_mm * 1000 # Convert to μm\n self.entry_minZ.setValue(z_value)\n \n def set_z_max(self):\n z_value = self.navigationController.z_pos_mm * 1000 # Convert to μm\n self.entry_maxZ.setValue(z_value)\n\n def update_z_min(self, z_pos_um):\n if z_pos_um < self.entry_minZ.value():\n self.entry_minZ.setValue(z_pos_um)\n\n def update_z_max(self, z_pos_um):\n if z_pos_um > self.entry_maxZ.value():\n self.entry_maxZ.setValue(z_pos_um)\n\n def update_Nz(self):\n z_min = self.entry_minZ.value()\n z_max = self.entry_maxZ.value()\n dz = self.entry_deltaZ.value()\n nz = math.ceil((z_max - z_min) / dz) + 1\n self.entry_NZ.setValue(nz)\n\n def update_region_progress(self, current_fov, num_fovs):\n self.progress_bar.setMaximum(num_fovs)\n self.progress_bar.setValue(current_fov)\n\n if self.acquisition_start_time is not None and current_fov > 0:\n elapsed_time = time.time() - self.acquisition_start_time\n Nt = self.entry_Nt.value()\n dt = self.entry_dt.value()\n\n # Calculate total processed FOVs and total FOVs\n processed_fovs = (self.current_region - 1) * num_fovs + current_fov + self.current_time_point * self.num_regions * num_fovs\n total_fovs = self.num_regions * num_fovs * Nt\n remaining_fovs = total_fovs - processed_fovs\n\n # Calculate ETA\n fov_per_second = processed_fovs / elapsed_time\n self.eta_seconds = remaining_fovs / fov_per_second + (Nt - 1 - self.current_time_point) * dt if fov_per_second > 0 else 0\n self.update_eta_display()\n\n # Start or restart the timer\n self.eta_timer.start(1000) # Update every 1000 ms (1 second)\n\n def update_acquisition_progress(self, current_region, num_regions, current_time_point):\n self.current_region = current_region\n self.current_time_point = current_time_point\n\n if self.current_region == 1 and self.current_time_point == 0: # First region\n self.acquisition_start_time = time.time()\n self.num_regions = num_regions\n\n progress_parts = []\n # Update timepoint progress if there are multiple timepoints and the timepoint has changed\n if self.entry_Nt.value() > 1:\n progress_parts.append(f\"Time {current_time_point + 1}/{self.entry_Nt.value()}\")\n\n # Update region progress if there are multiple regions\n if num_regions > 1:\n progress_parts.append(f\"Region {current_region}/{num_regions}\")\n\n # Set the progress label text, ensuring it's not empty\n progress_text = \" \".join(progress_parts)\n self.progress_label.setText(progress_text if progress_text else \"Progress\")\n\n self.progress_bar.setValue(0)\n\n def update_eta_display(self):\n if self.eta_seconds > 0:\n self.eta_seconds -= 1 # Decrease by 1 second\n hours, remainder = divmod(int(self.eta_seconds), 3600)\n minutes, seconds = divmod(remainder, 60)\n if hours > 0:\n eta_str = f\"{hours:02d}:{minutes:02d}:{seconds:02d}\"\n else:\n eta_str = f\"{minutes:02d}:{seconds:02d}\"\n self.eta_label.setText(f\"{eta_str}\")\n else:\n self.eta_timer.stop()\n self.eta_label.setText(\"00:00\")\n\n def display_progress_bar(self, show):\n self.progress_label.setVisible(show)\n self.progress_bar.setVisible(show)\n self.eta_label.setVisible(show)\n if show:\n self.progress_bar.setValue(0)\n self.progress_label.setText(\"Region 0/0\")\n self.eta_label.setText(\"--:--\")\n self.acquisition_start_time = None\n else:\n self.eta_timer.stop()\n\n def set_deltaX(self,value):\n mm_per_ustep = self.multipointController.navigationController.get_mm_per_ustep_X()\n deltaX = round(value/mm_per_ustep)*mm_per_ustep\n self.entry_deltaX.setValue(deltaX)\n self.multipointController.set_deltaX(deltaX)\n\n def set_deltaY(self,value):\n mm_per_ustep = self.multipointController.navigationController.get_mm_per_ustep_Y()\n deltaY = round(value/mm_per_ustep)*mm_per_ustep\n self.entry_deltaY.setValue(deltaY)\n self.multipointController.set_deltaY(deltaY)\n\n def set_deltaZ(self,value):\n mm_per_ustep = self.multipointController.navigationController.get_mm_per_ustep_Z()\n deltaZ = round(value/1000/mm_per_ustep)*mm_per_ustep*1000\n self.entry_deltaZ.setValue(deltaZ)\n self.multipointController.set_deltaZ(deltaZ)\n\n def set_saving_dir(self):\n dialog = QFileDialog()\n save_dir_base = dialog.getExistingDirectory(None, \"Select Folder\")\n self.multipointController.set_base_path(save_dir_base)\n self.lineEdit_savingDir.setText(save_dir_base)\n self.base_path_is_set = True\n\n def emit_selected_channels(self):\n selected_channels = [item.text() for item in self.list_configurations.selectedItems()]\n self.signal_acquisition_channels.emit(selected_channels)\n\n def display_stitcher_widget(self, checked):\n self.signal_stitcher_widget.emit(checked)\n\n def toggle_acquisition(self,pressed):\n if self.base_path_is_set == False:\n self.btn_startAcquisition.setChecked(False)\n msg = QMessageBox()\n msg.setText(\"Please choose base saving directory first\")\n msg.exec_()\n return\n if not self.list_configurations.selectedItems(): # no channel selected\n self.btn_startAcquisition.setChecked(False)\n msg = QMessageBox()\n msg.setText(\"Please select at least one imaging channel first\")\n msg.exec_()\n return\n if pressed:\n # @@@ to do: add a widgetManger to enable and disable widget\n # @@@ to do: emit signal to widgetManager to disable other widgets\n # clear skip positions\n if hasattr(self.multipointController, 'scanCoordinates') and self.multipointController.scanCoordinates:\n self.multipointController.scanCoordinates.grid_skip_positions = []\n\n # add the current location to the location list if the list is empty\n if len(self.location_list) == 0:\n self.add_location()\n self.acquisition_in_place = True\n self.multipointController.location_list = self.location_list\n\n if self.checkbox_set_z_range.isChecked():\n # Set Z-range (convert from μm to mm)\n minZ = self.entry_minZ.value() / 1000\n maxZ = self.entry_maxZ.value() / 1000\n self.multipointController.set_z_range(minZ, maxZ)\n\n self.setEnabled_all(False)\n # set parameters\n self.multipointController.set_deltaX(self.entry_deltaX.value())\n self.multipointController.set_deltaY(self.entry_deltaY.value())\n self.multipointController.set_deltaZ(self.entry_deltaZ.value())\n self.multipointController.set_deltat(self.entry_dt.value())\n self.multipointController.set_NX(self.entry_NX.value())\n self.multipointController.set_NY(self.entry_NY.value())\n self.multipointController.set_NZ(self.entry_NZ.value())\n self.multipointController.set_Nt(self.entry_Nt.value())\n self.multipointController.set_use_piezo(self.checkbox_usePiezo.isChecked())\n self.multipointController.set_af_flag(self.checkbox_withAutofocus.isChecked())\n self.multipointController.set_reflection_af_flag(self.checkbox_withReflectionAutofocus.isChecked())\n self.multipointController.set_base_path(self.lineEdit_savingDir.text())\n self.multipointController.set_selected_configurations((item.text() for item in self.list_configurations.selectedItems()))\n self.multipointController.start_new_experiment(self.lineEdit_experimentID.text())\n\n # emit signals\n self.signal_acquisition_started.emit(True)\n self.signal_acquisition_shape.emit(self.entry_NX.value(),\n self.entry_NY.value(),\n self.entry_NZ.value(),\n self.entry_deltaX.value(),\n self.entry_deltaY.value(),\n self.entry_deltaZ.value())\n\n self.multipointController.run_acquisition(location_list=self.location_list)\n else:\n self.multipointController.request_abort_aquisition()\n self.setEnabled_all(True)\n\n def load_last_used_locations(self):\n if self.last_used_locations is None or len(self.last_used_locations) == 0:\n return\n self.clear_only_location_list()\n\n for row, row_ind in zip(self.last_used_locations, self.last_used_location_ids):\n x = row[0]\n y = row[1]\n z = row[2]\n name = row_ind[0]\n if not np.any(np.all(self.location_list[:, :2] == [x, y], axis=1)):\n location_str = 'x:' + str(round(x,3)) + 'mm y:' + str(round(y,3)) + 'mm z:' + str(round(1000*z,1)) + 'μm'\n self.dropdown_location_list.addItem(location_str)\n self.location_list = np.vstack((self.location_list, [[x,y,z]]))\n self.location_ids = np.append(self.location_ids, name)\n self.table_location_list.insertRow(self.table_location_list.rowCount())\n self.table_location_list.setItem(self.table_location_list.rowCount()-1,0, QTableWidgetItem(str(round(x,3))))\n self.table_location_list.setItem(self.table_location_list.rowCount()-1,1, QTableWidgetItem(str(round(y,3))))\n self.table_location_list.setItem(self.table_location_list.rowCount()-1,2, QTableWidgetItem(str(round(z*1000,1))))\n self.table_location_list.setItem(self.table_location_list.rowCount()-1,3, QTableWidgetItem(name))\n index = self.dropdown_location_list.count() - 1\n self.dropdown_location_list.setCurrentIndex(index)\n print(self.location_list)\n self.navigationViewer.register_fov_to_image(x,y)\n else:\n print(\"Duplicate values not added based on x and y.\")\n #to-do: update z coordinate\n\n def acquisition_is_finished(self):\n if not self.acquisition_in_place:\n self.last_used_locations = self.location_list.copy()\n self.last_used_location_ids = self.location_ids.copy()\n else:\n self.clear_only_location_list()\n self.acquisition_in_place = False\n self.signal_acquisition_started.emit(False)\n self.btn_startAcquisition.setChecked(False)\n self.setEnabled_all(True)\n\n def setEnabled_all(self,enabled,exclude_btn_startAcquisition=True):\n self.btn_setSavingDir.setEnabled(enabled)\n self.lineEdit_savingDir.setEnabled(enabled)\n self.lineEdit_experimentID.setEnabled(enabled)\n self.entry_deltaX.setEnabled(enabled)\n self.entry_NX.setEnabled(enabled)\n self.entry_deltaY.setEnabled(enabled)\n self.entry_NY.setEnabled(enabled)\n self.entry_deltaZ.setEnabled(enabled)\n self.entry_NZ.setEnabled(enabled)\n self.entry_dt.setEnabled(enabled)\n self.entry_Nt.setEnabled(enabled)\n self.list_configurations.setEnabled(enabled)\n self.checkbox_genFocusMap.setEnabled(enabled)\n self.checkbox_withAutofocus.setEnabled(enabled)\n self.checkbox_withReflectionAutofocus.setEnabled(enabled)\n self.checkbox_stitchOutput.setEnabled(enabled)\n if exclude_btn_startAcquisition is not True:\n self.btn_startAcquisition.setEnabled(enabled)\n\n def disable_the_start_aquisition_button(self):\n self.btn_startAcquisition.setEnabled(False)\n\n def enable_the_start_aquisition_button(self):\n self.btn_startAcquisition.setEnabled(True)\n\n def add_location(self):\n x = self.navigationController.x_pos_mm\n y = self.navigationController.y_pos_mm\n z = self.navigationController.z_pos_mm\n name = ''\n if self.scanCoordinates is not None:\n name = self.create_point_id()\n\n if not np.any(np.all(self.location_list[:, :2] == [round(x,3), round(y,3)], axis=1)):\n location_str = 'x:' + str(round(x,3)) + 'mm y:' + str(round(y,3)) + 'mm z:' + str(round(1000*z,1)) + 'μm'\n self.dropdown_location_list.addItem(location_str)\n index = self.dropdown_location_list.count() - 1\n self.dropdown_location_list.setCurrentIndex(index)\n self.location_list = np.vstack((self.location_list, [[x,y,z]]))\n print(self.location_list)\n self.location_ids = np.append(self.location_ids, name)\n self.table_location_list.insertRow(self.table_location_list.rowCount())\n self.table_location_list.setItem(self.table_location_list.rowCount()-1,0, QTableWidgetItem(str(round(x,3))))\n self.table_location_list.setItem(self.table_location_list.rowCount()-1,1, QTableWidgetItem(str(round(y,3))))\n self.table_location_list.setItem(self.table_location_list.rowCount()-1,2, QTableWidgetItem(str(round(1000*z,1))))\n self.table_location_list.setItem(self.table_location_list.rowCount()-1,3, QTableWidgetItem(name))\n self.navigationViewer.register_fov_to_image(x,y)\n else:\n print(\"Duplicate values not added based on x and y.\")\n #to-do: update z coordinate\n\n def create_point_id(self):\n self.scanCoordinates.get_selected_wells()\n if len(self.scanCoordinates.name) == 0:\n print('Select a well first.')\n return None\n\n name = self.scanCoordinates.name[0]\n location_split_names = [int(x.split('-')[1]) for x in self.location_ids if x.split('-')[0] == name]\n if len(location_split_names) > 0:\n new_id = f'{name}-{np.max(location_split_names)+1}'\n else:\n new_id = f'{name}-0'\n return new_id\n\n def remove_location(self):\n index = self.dropdown_location_list.currentIndex()\n if index >=0:\n self.dropdown_location_list.removeItem(index)\n self.table_location_list.removeRow(index)\n x = self.location_list[index,0]\n y = self.location_list[index,1]\n z = self.location_list[index,2]\n self.navigationViewer.deregister_fov_to_image(x,y)\n self.location_list = np.delete(self.location_list, index, axis=0)\n self.location_ids = np.delete(self.location_ids, index, axis=0)\n if len(self.location_list) == 0:\n self.navigationViewer.clear_slide()\n print(self.location_list)\n\n def next(self):\n index = self.dropdown_location_list.currentIndex()\n # max_index = self.dropdown_location_list.count() - 1\n # index = min(index + 1, max_index)\n num_regions = self.dropdown_location_list.count()\n index = (index + 1) % (num_regions)\n self.dropdown_location_list.setCurrentIndex(index)\n x = self.location_list[index,0]\n y = self.location_list[index,1]\n z = self.location_list[index,2]\n self.navigationController.move_x_to(x)\n self.navigationController.move_y_to(y)\n self.navigationController.move_z_to(z)\n\n def previous(self):\n index = self.dropdown_location_list.currentIndex()\n index = max(index - 1, 0)\n self.dropdown_location_list.setCurrentIndex(index)\n x = self.location_list[index,0]\n y = self.location_list[index,1]\n z = self.location_list[index,2]\n self.navigationController.move_x_to(x)\n self.navigationController.move_y_to(y)\n self.navigationController.move_z_to(z)\n\n def clear(self):\n self.location_list = np.empty((0, 3), dtype=float)\n self.location_ids = np.empty((0,), dtype=str)\n self.dropdown_location_list.clear()\n # self.navigationViewer.clear_slide()\n self.navigationViewer.clear_overlay()\n self.table_location_list.setRowCount(0)\n\n def clear_only_location_list(self):\n self.location_list = np.empty((0,3),dtype=float)\n self.location_ids = np.empty((0,),dtype=str)\n self.dropdown_location_list.clear()\n self.table_location_list.setRowCount(0)\n\n def go_to(self,index):\n if index != -1:\n if index < len(self.location_list): # to avoid giving errors when adding new points\n x = self.location_list[index,0]\n y = self.location_list[index,1]\n z = self.location_list[index,2]\n self.navigationController.move_x_to(x)\n self.navigationController.move_y_to(y)\n self.navigationController.move_z_to(z)\n self.table_location_list.selectRow(index)\n\n def cell_was_clicked(self,row,column):\n\n self.dropdown_location_list.setCurrentIndex(row)\n\n def cell_was_changed(self,row,column):\n x= self.location_list[row,0]\n y= self.location_list[row,1]\n self.navigationViewer.deregister_fov_to_image(x,y)\n\n val_edit = self.table_location_list.item(row,column).text()\n if column < 2:\n val_edit = float(val_edit)\n self.location_list[row,column] = val_edit\n elif column == 2:\n z = float(val_edit)/1000\n self.location_list[row,column] = z\n else:\n self.location_ids[row] = val_edit\n\n self.navigationViewer.register_fov_to_image(self.location_list[row,0], self.location_list[row,1])\n location_str = 'x:' + str(round(self.location_list[row,0],3)) + 'mm y:' + str(round(self.location_list[row,1],3)) + 'mm z:' + str(round(1000*(self.location_list[row,2]),3)) + 'μm'\n self.dropdown_location_list.setItemText(row, location_str)\n self.go_to(row)\n\n def keyPressEvent(self, event):\n if event.key() == Qt.Key_A and event.modifiers() == Qt.ControlModifier:\n self.add_location()\n else:\n super().keyPressEvent(event)\n\n def _update_z(self,index,z_mm):\n self.location_list[index,2] = z_mm\n location_str = 'x:' + str(round(self.location_list[index,0],3)) + 'mm y:' + str(round(self.location_list[index,1],3)) + 'mm z:' + str(round(1000*z_mm,1)) + 'μm'\n self.dropdown_location_list.setItemText(index, location_str)\n\n def export_location_list(self):\n file_path, _ = QFileDialog.getSaveFileName(self, \"Export Location List\", '', \"CSV Files (*.csv);;All Files (*)\")\n if file_path:\n location_list_df = pd.DataFrame(self.location_list,columns=['x (mm)','y (mm)', 'z (um)'])\n location_list_df['ID'] = self.location_ids\n location_list_df['i'] = 0\n location_list_df['j'] = 0\n location_list_df['k'] = 0\n location_list_df.to_csv(file_path,index=False,header=True)\n\n def import_location_list(self):\n file_path, _ = QFileDialog.getOpenFileName(self, \"Import Location List\", '', \"CSV Files (*.csv);;All Files (*)\")\n if file_path:\n location_list_df = pd.read_csv(file_path)\n location_list_df_relevant = None\n try:\n location_list_df_relevant = location_list_df[['x (mm)', 'y (mm)', 'z (um)']]\n except KeyError:\n print(\"Improperly formatted location list being imported\")\n return\n if 'ID' in location_list_df.columns:\n location_list_df_relevant['ID'] = location_list_df['ID'].astype(str)\n else:\n location_list_df_relevant['ID'] = 'None'\n self.clear_only_location_list()\n for index, row in location_list_df_relevant.iterrows():\n x = row['x (mm)']\n y = row['y (mm)']\n z = row['z (um)']\n name = row['ID']\n if not np.any(np.all(self.location_list[:, :2] == [x, y], axis=1)):\n location_str = 'x:' + str(round(x,3)) + 'mm y:' + str(round(y,3)) + 'mm z:' + str(round(1000*z,1)) + 'μm'\n self.dropdown_location_list.addItem(location_str)\n index = self.dropdown_location_list.count() - 1\n self.dropdown_location_list.setCurrentIndex(index)\n self.location_list = np.vstack((self.location_list, [[x,y,z]]))\n self.location_ids = np.append(self.location_ids, name)\n self.table_location_list.insertRow(self.table_location_list.rowCount())\n self.table_location_list.setItem(self.table_location_list.rowCount()-1,0, QTableWidgetItem(str(round(x,3))))\n self.table_location_list.setItem(self.table_location_list.rowCount()-1,1, QTableWidgetItem(str(round(y,3))))\n self.table_location_list.setItem(self.table_location_list.rowCount()-1,2, QTableWidgetItem(str(round(1000*z,1))))\n self.table_location_list.setItem(self.table_location_list.rowCount()-1,3, QTableWidgetItem(name))\n self.navigationViewer.register_fov_to_image(x,y)\n else:\n print(\"Duplicate values not added based on x and y.\")\n print(self.location_list)\n\n\nclass MultiPointWidgetGrid(QFrame):\n\n signal_acquisition_started = Signal(bool)\n signal_acquisition_channels = Signal(list)\n signal_acquisition_z_levels = Signal(int)\n signal_acquisition_shape = Signal(int, int, int, float, float, float)\n signal_update_navigation_viewer = Signal()\n signal_stitcher_widget = Signal(bool)\n signal_z_stacking = Signal(int)\n signal_draw_shape = Signal(bool)\n\n def __init__(self, navigationController, navigationViewer, multipointController, objectiveStore, configurationManager, scanCoordinates, napariMosaicWidget=None, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.objectiveStore = objectiveStore\n self.multipointController = multipointController\n self.navigationController = navigationController\n self.navigationViewer = navigationViewer\n self.scanCoordinates = scanCoordinates\n self.configurationManager = configurationManager\n if napariMosaicWidget is None:\n self.performance_mode = True\n else:\n self.napariMosaicWidget = napariMosaicWidget\n self.performance_mode = False\n self.acquisition_pattern = ACQUISITION_PATTERN\n self.fov_pattern = FOV_PATTERN\n self.base_path_is_set = False\n self.well_selected = False\n self.use_coordinate_acquisition = True\n self.num_regions = 0\n self.region_coordinates = {}\n self.region_fov_coordinates_dict = {}\n self.acquisition_start_time = None\n self.manual_shape = None\n self.eta_seconds = 0\n self.add_components()\n self.setFrameStyle(QFrame.Panel | QFrame.Raised)\n self.set_default_scan_size()\n\n def add_components(self):\n\n self.entry_well_coverage = QDoubleSpinBox()\n self.entry_well_coverage.setRange(1, 999.99)\n self.entry_well_coverage.setValue(100)\n self.entry_well_coverage.setSuffix(\"%\")\n btn_width = self.entry_well_coverage.sizeHint().width()\n\n self.btn_setSavingDir = QPushButton('Browse')\n self.btn_setSavingDir.setDefault(False)\n self.btn_setSavingDir.setIcon(QIcon('icon/folder.png'))\n self.btn_setSavingDir.setFixedWidth(btn_width)\n\n self.lineEdit_savingDir = QLineEdit()\n self.lineEdit_savingDir.setText(DEFAULT_SAVING_PATH)\n self.multipointController.set_base_path(DEFAULT_SAVING_PATH)\n self.base_path_is_set = True\n\n self.lineEdit_experimentID = QLineEdit()\n\n # Update scan size entry\n self.entry_scan_size = QDoubleSpinBox()\n self.entry_scan_size.setRange(0.1, 100)\n self.entry_scan_size.setValue(1)\n self.entry_scan_size.setSuffix(\" mm\")\n\n self.entry_overlap = QDoubleSpinBox()\n self.entry_overlap.setRange(0, 99)\n self.entry_overlap.setValue(10)\n self.entry_overlap.setSuffix(\"%\")\n self.entry_overlap.setFixedWidth(btn_width)\n\n # Add z-min and z-max entries\n self.entry_minZ = QDoubleSpinBox()\n self.entry_minZ.setMinimum(SOFTWARE_POS_LIMIT.Z_NEGATIVE * 1000) # Convert to μm\n self.entry_minZ.setMaximum(SOFTWARE_POS_LIMIT.Z_POSITIVE * 1000) # Convert to μm\n self.entry_minZ.setSingleStep(1) # Step by 1 μm\n self.entry_minZ.setValue(self.navigationController.z_pos_mm * 1000) # Set to minimum\n self.entry_minZ.setSuffix(\" μm\")\n #self.entry_minZ.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n\n self.set_minZ_button = QPushButton('Set')\n self.set_minZ_button.clicked.connect(self.set_z_min)\n\n self.entry_maxZ = QDoubleSpinBox()\n self.entry_maxZ.setMinimum(SOFTWARE_POS_LIMIT.Z_NEGATIVE * 1000) # Convert to μm\n self.entry_maxZ.setMaximum(SOFTWARE_POS_LIMIT.Z_POSITIVE * 1000) # Convert to μm\n self.entry_maxZ.setSingleStep(1) # Step by 1 μm\n self.entry_maxZ.setValue(self.navigationController.z_pos_mm * 1000) # Set to maximum\n self.entry_maxZ.setSuffix(\" μm\")\n #self.entry_maxZ.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n\n self.set_maxZ_button = QPushButton('Set')\n self.set_maxZ_button.clicked.connect(self.set_z_max)\n\n self.entry_deltaZ = QDoubleSpinBox()\n self.entry_deltaZ.setMinimum(0)\n self.entry_deltaZ.setMaximum(1000)\n self.entry_deltaZ.setSingleStep(0.2)\n self.entry_deltaZ.setValue(Acquisition.DZ)\n self.entry_deltaZ.setDecimals(3)\n #self.entry_deltaZ.setEnabled(False)\n self.entry_deltaZ.setSuffix(\" μm\")\n self.entry_deltaZ.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n\n self.entry_NZ = QSpinBox()\n self.entry_NZ.setMinimum(1)\n self.entry_NZ.setMaximum(2000)\n self.entry_NZ.setSingleStep(1)\n self.entry_NZ.setValue(1)\n self.entry_NZ.setEnabled(False)\n\n self.entry_dt = QDoubleSpinBox()\n self.entry_dt.setMinimum(0)\n self.entry_dt.setMaximum(24*3600)\n self.entry_dt.setSingleStep(1)\n self.entry_dt.setValue(0)\n self.entry_dt.setSuffix(\" s\")\n self.entry_dt.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n\n self.entry_Nt = QSpinBox()\n self.entry_Nt.setMinimum(1)\n self.entry_Nt.setMaximum(5000)\n self.entry_Nt.setSingleStep(1)\n self.entry_Nt.setValue(1)\n\n self.combobox_z_stack = QComboBox()\n self.combobox_z_stack.addItems(['From Bottom (Z-min)', 'From Center', 'From Top (Z-max)'])\n self.combobox_z_stack.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n\n self.list_configurations = QListWidget()\n for microscope_configuration in self.configurationManager.configurations:\n self.list_configurations.addItems([microscope_configuration.name])\n self.list_configurations.setSelectionMode(QAbstractItemView.MultiSelection)\n\n # Add a combo box for shape selection\n self.combobox_shape = QComboBox()\n if self.performance_mode:\n self.combobox_shape.addItems(['Square', 'Circle'])\n else:\n self.combobox_shape.addItems(['Square', 'Circle', 'Manual'])\n self.combobox_shape.model().item(2).setEnabled(False)\n self.combobox_shape.setFixedWidth(btn_width)\n #self.combobox_shape.currentTextChanged.connect(self.on_shape_changed)\n\n self.checkbox_genFocusMap = QCheckBox('Focus Map')\n #self.checkbox_genFocusMap = QCheckBox('AF Map')\n self.checkbox_genFocusMap.setChecked(False)\n\n self.checkbox_withAutofocus = QCheckBox('Contrast AF')\n self.checkbox_withAutofocus.setChecked(MULTIPOINT_CONTRAST_AUTOFOCUS_ENABLE_BY_DEFAULT)\n self.multipointController.set_af_flag(MULTIPOINT_CONTRAST_AUTOFOCUS_ENABLE_BY_DEFAULT)\n\n self.checkbox_withReflectionAutofocus = QCheckBox('Reflection AF')\n self.checkbox_withReflectionAutofocus.setChecked(MULTIPOINT_REFLECTION_AUTOFOCUS_ENABLE_BY_DEFAULT)\n self.multipointController.set_reflection_af_flag(MULTIPOINT_REFLECTION_AUTOFOCUS_ENABLE_BY_DEFAULT)\n\n self.checkbox_usePiezo = QCheckBox('Piezo Z-Stack')\n self.checkbox_usePiezo.setChecked(MULTIPOINT_USE_PIEZO_FOR_ZSTACKS)\n\n self.checkbox_set_z_range = QCheckBox('Set Z-range')\n self.checkbox_set_z_range.toggled.connect(self.toggle_z_range_controls)\n\n # Add a checkbox for coordinate-based acquisition\n self.checkbox_useCoordinateAcquisition = QCheckBox('Use Coordinates')\n self.checkbox_useCoordinateAcquisition.setChecked(self.use_coordinate_acquisition)\n self.checkbox_useCoordinateAcquisition.stateChanged.connect(lambda state: setattr(self, 'use_coordinate_acquisition', bool(state)))\n\n self.checkbox_stitchOutput = QCheckBox('Stitch Scans')\n self.checkbox_stitchOutput.setChecked(False)\n\n self.btn_startAcquisition = QPushButton('Start\\n Acquisition ')\n self.btn_startAcquisition.setStyleSheet(\"background-color: #C2C2FF\")\n self.btn_startAcquisition.setCheckable(True)\n self.btn_startAcquisition.setChecked(False)\n #self.btn_startAcquisition.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n\n self.progress_label = QLabel('Region -/-')\n self.progress_bar = QProgressBar()\n self.eta_label = QLabel('--:--:--')\n self.progress_bar.setVisible(False)\n self.progress_label.setVisible(False)\n self.eta_label.setVisible(False)\n self.eta_timer = QTimer()\n\n # Main layout\n main_layout = QVBoxLayout()\n self.setLayout(main_layout)\n\n # Saving Path\n saving_path_layout = QHBoxLayout()\n saving_path_layout.addWidget(QLabel('Saving Path'))\n saving_path_layout.addWidget(self.lineEdit_savingDir)\n saving_path_layout.addWidget(self.btn_setSavingDir)\n main_layout.addLayout(saving_path_layout)\n\n # Experiment ID and Scan Shape\n row_1_layout = QHBoxLayout()\n row_1_layout.addWidget(QLabel('Experiment ID'))\n row_1_layout.addWidget(self.lineEdit_experimentID)\n row_1_layout.addWidget(QLabel('Well Shape'))\n row_1_layout.addWidget(self.combobox_shape)\n main_layout.addLayout(row_1_layout)\n\n # Well Coverage, Scan Size, and Overlap\n row_4_layout = QHBoxLayout()\n row_4_layout.addWidget(QLabel('Size'))\n row_4_layout.addWidget(self.entry_scan_size)\n #row_4_layout.addStretch(1)\n row_4_layout.addWidget(QLabel('FOV Overlap'))\n row_4_layout.addWidget(self.entry_overlap)\n #row_4_layout.addStretch(1)\n row_4_layout.addWidget(QLabel('Well Coverage'))\n row_4_layout.addWidget(self.entry_well_coverage)\n main_layout.addLayout(row_4_layout)\n\n grid = QGridLayout()\n\n # dz and Nz\n dz_layout = QHBoxLayout()\n dz_layout.addWidget(QLabel('dz'))\n dz_layout.addWidget(self.entry_deltaZ)\n dz_layout.addWidget(QLabel('Nz'))\n dz_layout.addWidget(self.entry_NZ)\n grid.addLayout(dz_layout, 0, 0)\n\n # dt and Nt\n dt_layout = QHBoxLayout()\n dt_layout.addWidget(QLabel('dt'))\n dt_layout.addWidget(self.entry_dt)\n dt_layout.addWidget(QLabel('Nt'))\n dt_layout.addWidget(self.entry_Nt)\n grid.addLayout(dt_layout, 0, 2)\n\n # Z-min\n self.z_min_layout = QHBoxLayout()\n self.z_min_layout.addWidget(self.set_minZ_button)\n min_label = QLabel('Z-min')\n min_label.setAlignment(Qt.AlignCenter | Qt.AlignVCenter)\n self.z_min_layout.addWidget(min_label)\n self.z_min_layout.addWidget(self.entry_minZ)\n grid.addLayout(self.z_min_layout, 1, 0)\n\n # Z-max\n self.z_max_layout = QHBoxLayout()\n self.z_max_layout.addWidget(self.set_maxZ_button)\n max_label = QLabel('Z-max')\n max_label.setAlignment(Qt.AlignCenter | Qt.AlignVCenter)\n self.z_max_layout.addWidget(max_label)\n self.z_max_layout.addWidget(self.entry_maxZ)\n grid.addLayout(self.z_max_layout, 1, 2)\n\n w = max(min_label.sizeHint().width(), max_label.sizeHint().width())\n min_label.setFixedWidth(w)\n max_label.setFixedWidth(w)\n\n # Configuration list\n grid.addWidget(self.list_configurations, 2, 0)\n\n # Options and Start button\n options_layout = QVBoxLayout()\n options_layout.addWidget(self.checkbox_withAutofocus)\n if SUPPORT_LASER_AUTOFOCUS:\n options_layout.addWidget(self.checkbox_withReflectionAutofocus)\n options_layout.addWidget(self.checkbox_genFocusMap)\n if ENABLE_OBJECTIVE_PIEZO:\n options_layout.addWidget(self.checkbox_usePiezo)\n options_layout.addWidget(self.checkbox_set_z_range)\n if ENABLE_STITCHER:\n options_layout.addWidget(self.checkbox_stitchOutput)\n\n bottom_right = QHBoxLayout()\n bottom_right.addLayout(options_layout)\n bottom_right.addSpacing(2)\n bottom_right.addWidget(self.btn_startAcquisition)\n\n grid.addLayout(bottom_right, 2, 2)\n spacer_widget = QWidget()\n spacer_widget.setFixedWidth(2)\n grid.addWidget(spacer_widget, 0, 1)\n\n # Set column stretches\n grid.setColumnStretch(0, 1) # Middle spacer\n grid.setColumnStretch(1, 0) # Middle spacer\n grid.setColumnStretch(2, 1) # Middle spacer\n\n main_layout.addLayout(grid)\n # Row 5: Progress Bar\n row_progress_layout = QHBoxLayout()\n row_progress_layout.addWidget(self.progress_label)\n row_progress_layout.addWidget(self.progress_bar)\n row_progress_layout.addWidget(self.eta_label)\n main_layout.addLayout(row_progress_layout)\n self.toggle_z_range_controls(self.checkbox_set_z_range.isChecked())\n\n # Connections\n self.btn_setSavingDir.clicked.connect(self.set_saving_dir)\n self.btn_startAcquisition.clicked.connect(self.toggle_acquisition)\n self.entry_deltaZ.valueChanged.connect(self.set_deltaZ)\n self.entry_NZ.valueChanged.connect(self.multipointController.set_NZ)\n self.entry_dt.valueChanged.connect(self.multipointController.set_deltat)\n self.entry_Nt.valueChanged.connect(self.multipointController.set_Nt)\n self.entry_scan_size.valueChanged.connect(self.update_coverage_from_scan_size)\n self.entry_well_coverage.valueChanged.connect(self.update_scan_size_from_coverage)\n self.combobox_shape.currentTextChanged.connect(self.on_set_shape)\n self.entry_scan_size.valueChanged.connect(self.update_coordinates)\n self.entry_overlap.valueChanged.connect(self.update_coordinates)\n self.checkbox_withAutofocus.toggled.connect(self.multipointController.set_af_flag)\n self.checkbox_withReflectionAutofocus.toggled.connect(self.multipointController.set_reflection_af_flag)\n self.checkbox_genFocusMap.toggled.connect(self.multipointController.set_gen_focus_map_flag)\n self.checkbox_usePiezo.toggled.connect(self.multipointController.set_use_piezo)\n self.checkbox_stitchOutput.toggled.connect(self.display_stitcher_widget)\n self.list_configurations.itemSelectionChanged.connect(self.emit_selected_channels)\n self.navigationViewer.signal_update_live_scan_grid.connect(self.set_live_scan_coordinates)\n self.navigationViewer.signal_update_well_coordinates.connect(self.set_well_coordinates)\n self.multipointController.acquisitionFinished.connect(self.acquisition_is_finished)\n self.multipointController.signal_acquisition_progress.connect(self.update_acquisition_progress)\n self.multipointController.signal_region_progress.connect(self.update_region_progress)\n self.signal_acquisition_started.connect(self.display_progress_bar)\n self.eta_timer.timeout.connect(self.update_eta_display)\n self.combobox_z_stack.currentIndexChanged.connect(self.signal_z_stacking.emit)\n if not self.performance_mode:\n self.napariMosaicWidget.signal_layers_initialized.connect(self.enable_manual_ROI)\n self.entry_NZ.valueChanged.connect(self.signal_acquisition_z_levels.emit)\n\n def enable_manual_ROI(self, enable):\n self.combobox_shape.model().item(2).setEnabled(enable)\n if not enable:\n self.set_default_shape()\n\n def update_region_progress(self, current_fov, num_fovs):\n self.progress_bar.setMaximum(num_fovs)\n self.progress_bar.setValue(current_fov)\n\n if self.acquisition_start_time is not None and current_fov > 0:\n elapsed_time = time.time() - self.acquisition_start_time\n Nt = self.entry_Nt.value()\n dt = self.entry_dt.value()\n\n # Calculate total processed FOVs and total FOVs\n processed_fovs = (self.current_region - 1) * num_fovs + current_fov + self.current_time_point * self.num_regions * num_fovs\n total_fovs = self.num_regions * num_fovs * Nt\n remaining_fovs = total_fovs - processed_fovs\n\n # Calculate ETA\n fov_per_second = processed_fovs / elapsed_time\n self.eta_seconds = remaining_fovs / fov_per_second + (Nt - 1 - self.current_time_point) * dt if fov_per_second > 0 else 0\n self.update_eta_display()\n\n # Start or restart the timer\n self.eta_timer.start(1000) # Update every 1000 ms (1 second)\n\n def update_acquisition_progress(self, current_region, num_regions, current_time_point):\n self.current_region = current_region\n self.current_time_point = current_time_point\n\n if self.current_region == 1 and self.current_time_point == 0: # First region\n self.acquisition_start_time = time.time()\n self.num_regions = num_regions\n\n progress_parts = []\n # Update timepoint progress if there are multiple timepoints and the timepoint has changed\n if self.entry_Nt.value() > 1:\n progress_parts.append(f\"Time {current_time_point + 1}/{self.entry_Nt.value()}\")\n\n # Update region progress if there are multiple regions\n if num_regions > 1:\n progress_parts.append(f\"Region {current_region}/{num_regions}\")\n\n # Set the progress label text, ensuring it's not empty\n progress_text = \" \".join(progress_parts)\n self.progress_label.setText(progress_text if progress_text else \"Progress\")\n\n self.progress_bar.setValue(0)\n\n def update_eta_display(self):\n if self.eta_seconds > 0:\n self.eta_seconds -= 1 # Decrease by 1 second\n hours, remainder = divmod(int(self.eta_seconds), 3600)\n minutes, seconds = divmod(remainder, 60)\n if hours > 0:\n eta_str = f\"{hours:02d}:{minutes:02d}:{seconds:02d}\"\n else:\n eta_str = f\"{minutes:02d}:{seconds:02d}\"\n self.eta_label.setText(f\"{eta_str}\")\n else:\n self.eta_timer.stop()\n self.eta_label.setText(\"00:00\")\n\n def display_progress_bar(self, show):\n self.progress_label.setVisible(show)\n self.progress_bar.setVisible(show)\n self.eta_label.setVisible(show)\n if show:\n self.progress_bar.setValue(0)\n self.progress_label.setText(\"Region 0/0\")\n self.eta_label.setText(\"--:--\")\n self.acquisition_start_time = None\n else:\n self.eta_timer.stop()\n\n def toggle_z_range_controls(self, is_visible):\n # Efficiently set visibility for all widgets in both layouts\n for layout in (self.z_min_layout, self.z_max_layout):\n for i in range(layout.count()):\n widget = layout.itemAt(i).widget()\n if widget:\n widget.setVisible(is_visible)\n\n # Enable/disable NZ entry based on the inverse of is_visible\n self.entry_NZ.setEnabled(not is_visible)\n current_z = self.navigationController.z_pos_mm * 1000\n self.entry_minZ.setValue(current_z)\n self.entry_maxZ.setValue(current_z)\n\n # Safely connect or disconnect signals\n try:\n if is_visible:\n self.entry_minZ.valueChanged.connect(self.update_z_max)\n self.entry_maxZ.valueChanged.connect(self.update_z_min)\n self.entry_minZ.valueChanged.connect(self.update_Nz)\n self.entry_maxZ.valueChanged.connect(self.update_Nz)\n self.entry_deltaZ.valueChanged.connect(self.update_Nz)\n else:\n self.entry_minZ.valueChanged.disconnect(self.update_z_max)\n self.entry_maxZ.valueChanged.disconnect(self.update_z_min)\n self.entry_minZ.valueChanged.disconnect(self.update_Nz)\n self.entry_maxZ.valueChanged.disconnect(self.update_Nz)\n self.entry_deltaZ.valueChanged.disconnect(self.update_Nz)\n except TypeError:\n # Handle case where signals might not be connected/disconnected\n pass\n\n # Update the layout\n self.updateGeometry()\n self.update()\n\n def set_default_scan_size(self):\n self.set_default_shape()\n if 'glass slide' in self.navigationViewer.sample:\n self.entry_scan_size.setEnabled(True)\n self.entry_well_coverage.setEnabled(False)\n else:\n self.entry_well_coverage.setEnabled(True)\n self.entry_well_coverage.setValue(100)\n self.update_scan_size_from_coverage()\n\n def set_default_shape(self):\n if self.scanCoordinates.format in ['384 well plate', '1536 well plate']:\n self.combobox_shape.setCurrentText('Square')\n elif self.scanCoordinates.format != 0:\n self.combobox_shape.setCurrentText('Circle')\n\n def get_effective_well_size(self):\n well_size = self.scanCoordinates.well_size_mm\n if self.combobox_shape.currentText() == 'Circle':\n fov_size_mm = (self.objectiveStore.get_pixel_size() / 1000) * Acquisition.CROP_WIDTH\n return well_size + fov_size_mm * (1 + math.sqrt(2))\n return well_size\n\n def on_set_shape(self):\n shape = self.combobox_shape.currentText()\n if shape == 'Manual':\n self.signal_draw_shape.emit(True)\n else:\n self.signal_draw_shape.emit(False)\n self.update_coverage_from_scan_size()\n self.update_coordinates()\n\n def update_manual_shape(self, shapes_data_mm):\n self.clear_regions()\n if shapes_data_mm and len(shapes_data_mm) > 0:\n self.manual_shapes = shapes_data_mm\n print(f\"Manual ROIs updated with {len(self.manual_shapes)} shapes\")\n else:\n self.manual_shapes = None\n print(\"No valid shapes found, cleared manual ROIs\")\n self.update_coordinates()\n\n def convert_pixel_to_mm(self, pixel_coords):\n # Convert pixel coordinates to millimeter coordinates\n mm_coords = pixel_coords * self.napariMosaicWidget.viewer_pixel_size_mm\n mm_coords += np.array([self.napariMosaicWidget.top_left_coordinate[1], self.napariMosaicWidget.top_left_coordinate[0]])\n return mm_coords\n\n def update_coverage_from_scan_size(self):\n if 'glass slide' not in self.navigationViewer.sample and hasattr(self.navigationViewer, 'well_size_mm'):\n effective_well_size = self.get_effective_well_size()\n scan_size = self.entry_scan_size.value()\n coverage = round((scan_size / effective_well_size) * 100, 2)\n print('COVERAGE', coverage)\n self.entry_well_coverage.setValue(coverage)\n\n def update_scan_size_from_coverage(self):\n if hasattr(self.navigationViewer, 'well_size_mm'):\n effective_well_size = self.get_effective_well_size()\n coverage = self.entry_well_coverage.value()\n scan_size = round((coverage / 100) * effective_well_size, 3)\n print('SIZE', scan_size)\n self.entry_scan_size.setValue(scan_size)\n\n def update_dz(self):\n z_min = self.entry_minZ.value()\n z_max = self.entry_maxZ.value()\n nz = self.entry_NZ.value()\n dz = (z_max - z_min) / (nz - 1) if nz > 1 else 0\n self.entry_deltaZ.setValue(dz)\n\n def update_Nz(self):\n z_min = self.entry_minZ.value()\n z_max = self.entry_maxZ.value()\n dz = self.entry_deltaZ.value()\n nz = math.ceil((z_max - z_min) / dz) + 1\n self.entry_NZ.setValue(nz)\n\n def set_z_min(self):\n z_value = self.navigationController.z_pos_mm * 1000 # Convert to μm\n self.entry_minZ.setValue(z_value)\n\n def set_z_max(self):\n z_value = self.navigationController.z_pos_mm * 1000 # Convert to μm\n self.entry_maxZ.setValue(z_value)\n\n def update_z_min(self, z_pos_um):\n if z_pos_um < self.entry_minZ.value():\n self.entry_minZ.setValue(z_pos_um)\n\n def update_z_max(self, z_pos_um):\n if z_pos_um > self.entry_maxZ.value():\n self.entry_maxZ.setValue(z_pos_um)\n\n def init_z(self, z_pos_mm=None):\n # sets initial z range form the current z position used after startup of the GUI\n if z_pos_mm is None:\n z_pos_mm = self.navigationController.z_pos_mm\n\n # block entry update signals\n self.entry_minZ.blockSignals(True)\n self.entry_maxZ.blockSignals(True)\n\n # set entry range values bith to current z pos\n self.entry_minZ.setValue(z_pos_mm*1000)\n self.entry_maxZ.setValue(z_pos_mm*1000)\n print(\"init z-level wellplate:\", self.entry_minZ.value())\n\n # reallow updates from entry sinals (signal enforces min <= max when we update either entry)\n self.entry_minZ.blockSignals(False)\n self.entry_maxZ.blockSignals(False)\n\n def set_live_scan_coordinates(self, x_mm, y_mm):\n parent = self.multipointController.parent\n is_current_widget = (parent is not None and hasattr(parent, 'recordTabWidget') and\n parent.recordTabWidget.currentWidget() == self)\n\n if self.combobox_shape.currentText() != 'Manual' and self.scanCoordinates.format == 0 and (parent is None or is_current_widget):\n\n if self.region_coordinates:\n self.clear_regions()\n\n self.add_region('current', x_mm, y_mm)\n\n def set_well_coordinates(self, selected):\n self.well_selected = selected and bool(self.scanCoordinates.get_selected_wells())\n if hasattr(self.multipointController.parent, 'recordTabWidget') and self.multipointController.parent.recordTabWidget.currentWidget() == self:\n if self.scanCoordinates.format == 0:\n x = self.navigationController.x_pos_mm\n y = self.navigationController.y_pos_mm\n self.set_live_scan_coordinates(x, y)\n\n elif self.well_selected:\n # Get the set of currently selected well IDs\n selected_well_ids = set(self.scanCoordinates.name)\n\n # Remove regions that are no longer selected\n for well_id in list(self.region_coordinates.keys()):\n if well_id not in selected_well_ids:\n self.remove_region(well_id)\n\n # Add regions for selected wells\n for well_id, (x, y) in zip(self.scanCoordinates.name, self.scanCoordinates.coordinates_mm):\n if well_id not in self.region_coordinates:\n self.add_region(well_id, x, y)\n\n self.signal_update_navigation_viewer.emit()\n print(f\"Updated region coordinates: {len(self.region_coordinates)} wells\")\n\n else:\n print(\"Clear well coordinates\")\n self.clear_regions()\n\n def update_coordinates(self):\n shape = self.combobox_shape.currentText()\n if shape == 'Manual':\n self.region_fov_coordinates_dict.clear()\n self.region_coordinates.clear()\n if self.manual_shapes is not None:\n # Handle manual ROIs\n for i, manual_shape in enumerate(self.manual_shapes):\n scan_coordinates = self.create_manual_region_coordinates(\n self.objectiveStore,\n manual_shape,\n overlap_percent=self.entry_overlap.value()\n )\n if scan_coordinates:\n if len(self.manual_shapes) <= 1:\n region_name = f'manual'\n else:\n region_name = f'manual_{i}'\n self.region_fov_coordinates_dict[region_name] = scan_coordinates\n # Set the region coordinates to the center of the manual ROI\n center = np.mean(manual_shape, axis=0)\n self.region_coordinates[region_name] = [center[0], center[1]]\n else:\n print(\"No Manual ROI found\")\n\n elif 'glass slide' in self.navigationViewer.sample:\n x = self.navigationController.x_pos_mm\n y = self.navigationController.y_pos_mm\n self.set_live_scan_coordinates(x, y)\n else:\n if len(self.region_coordinates) > 0:\n self.clear_regions()\n self.set_well_coordinates(True)\n\n def update_region_z_level(self, well_id, new_z):\n if len(self.region_coordinates[well_id]) == 3:\n # [x, y, z] -> [x, y, new_z]\n self.region_coordinates[well_id][2] = new_z\n else:\n # [x, y] -> [x, y, new_z]\n self.region_coordinates[well_id].append[new_z]\n print(f\"Updated z-level to {new_z} for region {well_id}\")\n\n def add_region(self, well_id, x, y):\n z = self.navigationController.z_pos_mm\n action = \"Updated\" if well_id in self.region_coordinates else \"Added\"\n\n self.region_coordinates[well_id] = [float(x), float(y)] #, float(z)]\n\n scan_coordinates = self.create_region_coordinates(\n self.objectiveStore,\n x, y,\n scan_size_mm=self.entry_scan_size.value(),\n overlap_percent=self.entry_overlap.value(),\n shape=self.combobox_shape.currentText()\n )\n self.region_fov_coordinates_dict[well_id] = scan_coordinates\n\n print(f\"{action} Region: {well_id} - x={x:.3f}, y={y:.3f}\") #, z={z:.3f}\")\n # print(\"Size:\", self.entry_scan_size.value())\n # print(\"Shape:\", self.combobox_shape.currentText())\n # print(\"# fovs in region:\", len(scan_coordinates))\n\n def remove_region(self, well_id):\n if well_id in self.region_coordinates:\n del self.region_coordinates[well_id]\n\n if well_id in self.region_fov_coordinates_dict:\n region_scan_coordinates = self.region_fov_coordinates_dict.pop(well_id)\n for coord in region_scan_coordinates:\n self.navigationViewer.deregister_fov_to_image(coord[0], coord[1])\n\n print(f\"Removed Region: {well_id}\")\n\n def clear_regions(self):\n self.navigationViewer.clear_overlay()\n self.region_coordinates.clear()\n self.region_fov_coordinates_dict.clear()\n print(\"Cleared All Regions\")\n\n def create_region_coordinates(self, objectiveStore, center_x, center_y, scan_size_mm=None, overlap_percent=10, shape='Square'):\n if shape == 'Manual':\n return self.create_manual_region_coordinates(objectiveStore, self.manual_shapes, overlap_percent)\n\n if scan_size_mm is None:\n scan_size_mm = self.scanCoordinates.well_size_mm\n pixel_size_um = objectiveStore.get_pixel_size()\n fov_size_mm = (pixel_size_um / 1000) * Acquisition.CROP_WIDTH\n step_size_mm = fov_size_mm * (1 - overlap_percent / 100)\n\n steps = math.floor(scan_size_mm / step_size_mm)\n if shape == 'Circle':\n tile_diagonal = math.sqrt(2) * fov_size_mm\n if steps % 2 == 1: # for odd steps\n actual_scan_size_mm = (steps - 1) * step_size_mm + tile_diagonal\n else: # for even steps\n actual_scan_size_mm = math.sqrt(((steps - 1) * step_size_mm + fov_size_mm)**2 + (step_size_mm + fov_size_mm)**2)\n\n if actual_scan_size_mm > scan_size_mm:\n actual_scan_size_mm -= step_size_mm\n steps -= 1\n else:\n actual_scan_size_mm = (steps - 1) * step_size_mm + fov_size_mm\n\n steps = max(1, steps) # Ensure at least one step\n # print(f\"steps: {steps}, step_size_mm: {step_size_mm}\")\n # print(f\"scan size mm: {scan_size_mm}\")\n # print(f\"actual scan size mm: {actual_scan_size_mm}\")\n\n scan_coordinates = []\n half_steps = (steps - 1) / 2\n radius_squared = (scan_size_mm / 2) ** 2\n fov_size_mm_half = fov_size_mm / 2\n\n for i in range(steps):\n row = []\n y = center_y + (i - half_steps) * step_size_mm\n for j in range(steps):\n x = center_x + (j - half_steps) * step_size_mm\n if shape == 'Square' or (shape == 'Circle' and self._is_in_circle(x, y, center_x, center_y, radius_squared, fov_size_mm_half)):\n row.append((x, y))\n self.navigationViewer.register_fov_to_image(x, y)\n\n if self.fov_pattern == 'S-Pattern' and i % 2 == 1:\n row.reverse()\n scan_coordinates.extend(row)\n\n if not scan_coordinates and shape == 'Circle':\n scan_coordinates.append((center_x, center_y))\n self.navigationViewer.register_fov_to_image(center_x, center_y)\n\n self.signal_update_navigation_viewer.emit()\n return scan_coordinates\n\n def _is_in_circle(self, x, y, center_x, center_y, radius_squared, fov_size_mm_half):\n corners = [\n (x - fov_size_mm_half, y - fov_size_mm_half),\n (x + fov_size_mm_half, y - fov_size_mm_half),\n (x - fov_size_mm_half, y + fov_size_mm_half),\n (x + fov_size_mm_half, y + fov_size_mm_half)\n ]\n return all((cx - center_x)**2 + (cy - center_y)**2 <= radius_squared for cx, cy in corners)\n\n def create_scan_grid(self, objectiveStore, scan_size_mm=None, overlap_percent=10, shape='Square'):\n if scan_size_mm is None:\n scan_size_mm = self.scanCoordinates.well_size_mm\n\n pixel_size_um = objectiveStore.get_pixel_size()\n fov_size_mm = (pixel_size_um / 1000) * Acquisition.CROP_WIDTH\n step_size_mm = fov_size_mm * (1 - overlap_percent / 100)\n\n steps = math.floor(scan_size_mm / step_size_mm)\n if shape == 'Circle':\n # check if corners of middle row/col all fit\n if steps % 2 == 1: # for odd steps\n tile_diagonal = math.sqrt(2) * fov_size_mm\n actual_scan_size_mm = (steps - 1) * step_size_mm + tile_diagonal\n else: # for even steps\n actual_scan_size_mm = math.sqrt(((steps - 1) * step_size_mm + fov_size_mm)**2 + (step_size_mm + fov_size_mm)**2)\n\n if actual_scan_size_mm > scan_size_mm:\n actual_scan_size_mm -= step_size_mm\n steps -= 1\n else:\n actual_scan_size_mm = (steps - 1) * step_size_mm + fov_size_mm\n\n steps = max(1, steps) # Ensure at least one step\n # print(\"steps:\", steps)\n # print(\"scan size mm:\", scan_size_mm)\n # print(\"actual scan size mm:\", actual_scan_size_mm)\n\n region_skip_positions = []\n\n if shape == 'Circle':\n radius = scan_size_mm / 2\n for i in range(steps):\n for j in range(steps):\n x_rel = (j - (steps - 1) / 2) * step_size_mm\n y_rel = (i - (steps - 1) / 2) * step_size_mm\n corners = [\n (x_rel - fov_size_mm / 2, y_rel - fov_size_mm / 2), # Top-left\n (x_rel + fov_size_mm / 2, y_rel - fov_size_mm / 2), # Top-right\n (x_rel - fov_size_mm / 2, y_rel + fov_size_mm / 2), # Bottom-left\n (x_rel + fov_size_mm / 2, y_rel + fov_size_mm / 2) # Bottom-right\n ]\n if any(math.sqrt(cx**2 + cy**2) > radius for cx, cy in corners):\n region_skip_positions.append((i, j))\n\n # If all positions were skipped, clear the list and set steps to 1\n if len(region_skip_positions) == steps * steps:\n region_skip_positions.clear()\n steps = 1\n\n self.scanCoordinates.grid_skip_positions = region_skip_positions\n return steps, step_size_mm\n\n def create_manual_region_coordinates(self, objectiveStore, shape_coords, overlap_percent):\n if shape_coords is None or len(shape_coords) < 3:\n print(\"Invalid manual ROI data\")\n return []\n\n pixel_size_um = objectiveStore.get_pixel_size()\n fov_size_mm = (pixel_size_um / 1000) * Acquisition.CROP_WIDTH\n step_size_mm = fov_size_mm * (1 - overlap_percent / 100)\n\n # Ensure shape_coords is a numpy array\n shape_coords = np.array(shape_coords)\n if shape_coords.ndim == 1:\n shape_coords = shape_coords.reshape(-1, 2)\n elif shape_coords.ndim > 2:\n print(f\"Unexpected shape of manual_shape: {shape_coords.shape}\")\n return []\n\n # Calculate bounding box\n x_min, y_min = np.min(shape_coords, axis=0)\n x_max, y_max = np.max(shape_coords, axis=0)\n\n # Create a grid of points within the bounding box\n x_range = np.arange(x_min, x_max + step_size_mm, step_size_mm)\n y_range = np.arange(y_min, y_max + step_size_mm, step_size_mm)\n xx, yy = np.meshgrid(x_range, y_range)\n grid_points = np.column_stack((xx.ravel(), yy.ravel()))\n\n # # Use Delaunay triangulation for efficient point-in-polygon test\n # hull = Delaunay(shape_coords)\n # mask = hull.find_simplex(grid_points) >= 0\n\n # Use Ray Casting for point-in-polygon test\n mask = np.array([self.point_inside_polygon(x, y, shape_coords) for x, y in grid_points])\n\n # Filter points inside the polygon\n valid_points = grid_points[mask]\n\n # Sort points\n sorted_indices = np.lexsort((valid_points[:, 0], valid_points[:, 1]))\n sorted_points = valid_points[sorted_indices]\n\n # Apply S-Pattern if needed\n if self.fov_pattern == 'S-Pattern':\n unique_y = np.unique(sorted_points[:, 1])\n for i in range(1, len(unique_y), 2):\n mask = sorted_points[:, 1] == unique_y[i]\n sorted_points[mask] = sorted_points[mask][::-1]\n\n # Register FOVs\n for x, y in sorted_points:\n self.navigationViewer.register_fov_to_image(x, y)\n\n self.signal_update_navigation_viewer.emit()\n return sorted_points.tolist()\n\n def point_inside_polygon(self, x, y, poly):\n n = len(poly)\n inside = False\n p1x, p1y = poly[0]\n for i in range(n + 1):\n p2x, p2y = poly[i % n]\n if y > min(p1y, p2y):\n if y <= max(p1y, p2y):\n if x <= max(p1x, p2x):\n if p1y != p2y:\n xinters = (y - p1y) * (p2x - p1x) / (p2y - p1y) + p1x\n if p1x == p2x or x <= xinters:\n inside = not inside\n p1x, p1y = p2x, p2y\n return inside\n\n def sort_coordinates(self):\n print(f\"Acquisition pattern: {self.acquisition_pattern}\")\n\n if len(self.region_coordinates) <= 1:\n return\n\n def sort_key(item):\n key, coord = item\n if 'manual' in key:\n return (0, coord[1], coord[0]) # Manual coords: sort by y, then x\n else:\n row, col = key[0], int(key[1:])\n return (1, ord(row), col) # Well coords: sort by row, then column\n\n sorted_items = sorted(self.region_coordinates.items(), key=sort_key)\n\n if self.acquisition_pattern == 'S-Pattern':\n # Group by row and reverse alternate rows\n rows = itertools.groupby(sorted_items, key=lambda x: x[1][1] if 'manual' in x[0] else x[0][0])\n sorted_items = []\n for i, (_, group) in enumerate(rows):\n row = list(group)\n if i % 2 == 1:\n row.reverse()\n sorted_items.extend(row)\n\n # Update dictionaries efficiently\n self.region_coordinates = {k: v for k, v in sorted_items}\n self.region_fov_coordinates_dict = {k: self.region_fov_coordinates_dict[k]\n for k, _ in sorted_items\n if k in self.region_fov_coordinates_dict}\n\n def toggle_acquisition(self, pressed):\n if not self.base_path_is_set:\n self.btn_startAcquisition.setChecked(False)\n QMessageBox.warning(self, \"Warning\", \"Please choose base saving directory first\")\n return\n\n if not self.use_coordinate_acquisition and 'glass slide' in self.navigationViewer.sample and not self.well_selected:\n #if self.navigationViewer.sample != 'glass slide' and self.well_selected == False:\n self.btn_startAcquisition.setChecked(False)\n msg = QMessageBox()\n msg.setText(\"Please select a well to scan first\")\n msg.exec_()\n return\n\n if not self.list_configurations.selectedItems():\n self.btn_startAcquisition.setChecked(False)\n QMessageBox.warning(self, \"Warning\", \"Please select at least one imaging channel\")\n return\n\n if pressed:\n self.setEnabled_all(False)\n\n scan_size_mm = self.entry_scan_size.value()\n overlap_percent = self.entry_overlap.value()\n shape = self.combobox_shape.currentText()\n\n self.sort_coordinates()\n if self.use_coordinate_acquisition:\n if len(self.region_coordinates) == 0:\n # Use current location if no regions added\n x = self.navigationController.x_pos_mm\n y = self.navigationController.y_pos_mm\n z = self.navigationController.z_pos_mm\n self.region_coordinates['current'] = [x, y, z]\n scan_coordinates = self.create_region_coordinates(\n self.objectiveStore,\n x, y,\n scan_size_mm=scan_size_mm,\n overlap_percent=overlap_percent,\n shape=shape\n )\n self.region_fov_coordinates_dict['current'] = scan_coordinates\n\n # Calculate total number of positions for signal emission\n total_positions = sum(len(coords) for coords in self.region_fov_coordinates_dict.values())\n Nx = Ny = int(math.sqrt(total_positions))\n dx_mm = dy_mm = scan_size_mm / (Nx - 1) if Nx > 1 else scan_size_mm\n\n else:\n # Use grid-based acquisition\n if self.scanCoordinates.format == 0 or len(self.region_coordinates) == 0:\n x = self.navigationController.x_pos_mm\n y = self.navigationController.y_pos_mm\n z = self.navigationController.z_pos_mm\n self.region_coordinates['current'] = [x, y, z]\n steps, step_size_mm = self.create_scan_grid(\n self.objectiveStore,\n scan_size_mm=scan_size_mm,\n overlap_percent=overlap_percent,\n shape=shape\n )\n Nx = Ny = steps\n dx_mm = dy_mm = step_size_mm\n\n # Set up multipoint controller\n self.multipointController.set_NX(Nx)\n self.multipointController.set_NY(Ny)\n self.multipointController.set_deltaX(dx_mm)\n self.multipointController.set_deltaY(dy_mm)\n\n if self.checkbox_set_z_range.isChecked():\n # Set Z-range (convert from μm to mm)\n minZ = self.entry_minZ.value() / 1000 # Convert from μm to mm\n maxZ = self.entry_maxZ.value() / 1000 # Convert from μm to mm\n self.multipointController.set_z_range(minZ, maxZ)\n print(\"set z-range\", (minZ, maxZ))\n else:\n z = self.navigationController.z_pos_mm\n self.multipointController.set_z_range(z, z)\n\n self.multipointController.set_deltaZ(self.entry_deltaZ.value())\n self.multipointController.set_NZ(self.entry_NZ.value())\n self.multipointController.set_deltat(self.entry_dt.value())\n self.multipointController.set_Nt(self.entry_Nt.value())\n self.multipointController.set_use_piezo(self.checkbox_usePiezo.isChecked())\n self.multipointController.set_af_flag(self.checkbox_withAutofocus.isChecked())\n self.multipointController.set_reflection_af_flag(self.checkbox_withReflectionAutofocus.isChecked())\n self.multipointController.set_selected_configurations([item.text() for item in self.list_configurations.selectedItems()])\n self.multipointController.start_new_experiment(self.lineEdit_experimentID.text())\n\n # Emit signals\n self.signal_acquisition_started.emit(True)\n self.signal_acquisition_shape.emit(Nx, Ny, self.entry_NZ.value(),\n dx_mm, dy_mm, self.entry_deltaZ.value())\n\n # Start acquisition\n if self.use_coordinate_acquisition:\n self.multipointController.run_acquisition(location_list=self.region_coordinates, coordinate_dict=self.region_fov_coordinates_dict)\n else:\n if self.scanCoordinates.format == 0:\n self.multipointController.run_acquisition(location_list=list(self.region_coordinates.values())) # glass slide\n else:\n self.multipointController.run_acquisition() # wellplate\n else:\n self.multipointController.request_abort_aquisition()\n self.setEnabled_all(True)\n\n def acquisition_is_finished(self):\n self.signal_acquisition_started.emit(False)\n self.btn_startAcquisition.setChecked(False)\n self.set_well_coordinates(self.well_selected)\n if self.combobox_shape.currentText() == 'Manual':\n self.signal_draw_shape.emit(True)\n self.setEnabled_all(True)\n\n def setEnabled_all(self, enabled):\n for widget in self.findChildren(QWidget):\n if (widget != self.btn_startAcquisition and\n widget != self.progress_bar and\n widget != self.progress_label and\n widget != self.eta_label):\n widget.setEnabled(enabled)\n\n if self.scanCoordinates.format == 0:\n self.entry_well_coverage.setEnabled(False)\n\n def set_saving_dir(self):\n dialog = QFileDialog()\n save_dir_base = dialog.getExistingDirectory(None, \"Select Folder\")\n self.multipointController.set_base_path(save_dir_base)\n self.lineEdit_savingDir.setText(save_dir_base)\n self.base_path_is_set = True\n\n def set_deltaZ(self, value):\n mm_per_ustep = SCREW_PITCH_Z_MM/(self.multipointController.navigationController.z_microstepping*FULLSTEPS_PER_REV_Z)\n deltaZ = round(value/1000/mm_per_ustep)*mm_per_ustep*1000\n self.entry_deltaZ.setValue(deltaZ)\n self.multipointController.set_deltaZ(deltaZ)\n\n def emit_selected_channels(self):\n selected_channels = [item.text() for item in self.list_configurations.selectedItems()]\n self.signal_acquisition_channels.emit(selected_channels)\n\n def display_stitcher_widget(self, checked):\n self.signal_stitcher_widget.emit(checked)\n\n\nclass StitcherWidget(QFrame):\n\n def __init__(self, configurationManager, contrastManager, *args, **kwargs):\n super(StitcherWidget, self).__init__(*args, **kwargs)\n self.configurationManager = configurationManager\n self.contrastManager = contrastManager\n self.stitcherThread = None\n self.output_path = \"\"\n self.initUI()\n\n def initUI(self):\n self.setFrameStyle(QFrame.Panel | QFrame.Raised) # Set frame style\n self.layout = QVBoxLayout(self)\n self.rowLayout1 = QHBoxLayout()\n self.rowLayout2 = QHBoxLayout()\n\n # Use registration checkbox\n self.useRegistrationCheck = QCheckBox(\"Registration\")\n self.useRegistrationCheck.toggled.connect(self.onRegistrationCheck)\n self.rowLayout1.addWidget(self.useRegistrationCheck)\n self.rowLayout1.addStretch()\n\n # Apply flatfield correction checkbox\n self.applyFlatfieldCheck = QCheckBox(\"Flatfield Correction\")\n self.rowLayout1.addWidget(self.applyFlatfieldCheck)\n self.rowLayout1.addStretch()\n\n # Output format dropdown\n self.outputFormatLabel = QLabel('Output Format', self)\n self.outputFormatCombo = QComboBox(self)\n self.outputFormatCombo.addItem(\"OME-ZARR\")\n self.outputFormatCombo.addItem(\"OME-TIFF\")\n self.rowLayout1.addWidget(self.outputFormatLabel)\n self.rowLayout1.addWidget(self.outputFormatCombo)\n\n # Select registration channel\n self.registrationChannelLabel = QLabel(\"Registration Configuration\", self)\n self.registrationChannelLabel.setVisible(False)\n self.rowLayout2.addWidget(self.registrationChannelLabel)\n self.registrationChannelCombo = QComboBox(self)\n self.registrationChannelLabel.setVisible(False)\n self.registrationChannelCombo.setVisible(False)\n self.registrationChannelCombo.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n self.rowLayout2.addWidget(self.registrationChannelCombo)\n\n # Select registration cz-level\n self.registrationZLabel = QLabel(\" Z-Level\", self)\n self.registrationZLabel.setVisible(False)\n self.rowLayout2.addWidget(self.registrationZLabel)\n self.registrationZCombo = QSpinBox(self)\n self.registrationZCombo.setSingleStep(1)\n self.registrationZCombo.setMinimum(0)\n self.registrationZCombo.setMaximum(0)\n self.registrationZCombo.setValue(0)\n self.registrationZLabel.setVisible(False)\n self.registrationZCombo.setVisible(False)\n self.rowLayout2.addWidget(self.registrationZCombo)\n\n self.layout.addLayout(self.rowLayout1)\n self.layout.addLayout(self.rowLayout2)\n self.setLayout(self.layout)\n\n # Button to view output in Napari\n self.viewOutputButton = QPushButton(\"View Output in Napari\")\n self.viewOutputButton.setEnabled(False) # Initially disabled\n self.viewOutputButton.setVisible(False)\n self.viewOutputButton.clicked.connect(self.viewOutputNapari)\n self.layout.addWidget(self.viewOutputButton)\n\n # Progress bar\n progress_row = QHBoxLayout()\n\n # Status label\n self.statusLabel = QLabel(\"Status: Image Acquisition\")\n progress_row.addWidget(self.statusLabel)\n self.statusLabel.setVisible(False)\n\n self.progressBar = QProgressBar()\n progress_row.addWidget(self.progressBar)\n self.progressBar.setVisible(False) # Initially hidden\n self.layout.addLayout(progress_row)\n\n def setStitcherThread(self, thread):\n self.stitcherThread = thread\n\n def onRegistrationCheck(self, checked):\n self.registrationChannelLabel.setVisible(checked)\n self.registrationChannelCombo.setVisible(checked)\n self.registrationZLabel.setVisible(checked)\n self.registrationZCombo.setVisible(checked)\n\n def updateRegistrationChannels(self, selected_channels):\n self.registrationChannelCombo.clear() # Clear existing items\n self.registrationChannelCombo.addItems(selected_channels)\n\n def updateRegistrationZLevels(self, Nz):\n self.registrationZCombo.setMinimum(0)\n self.registrationZCombo.setMaximum(Nz - 1)\n\n def gettingFlatfields(self):\n self.statusLabel.setText('Status: Calculating Flatfields')\n self.viewOutputButton.setVisible(False)\n self.viewOutputButton.setStyleSheet(\"\")\n self.progressBar.setValue(0)\n self.statusLabel.setVisible(True)\n self.progressBar.setVisible(True)\n\n def startingStitching(self):\n self.statusLabel.setText('Status: Stitching Scans')\n self.viewOutputButton.setVisible(False)\n self.progressBar.setValue(0)\n self.statusLabel.setVisible(True)\n self.progressBar.setVisible(True)\n\n def updateProgressBar(self, value, total):\n self.progressBar.setMaximum(total)\n self.progressBar.setValue(value)\n self.progressBar.setVisible(True)\n\n def startingSaving(self, stitch_complete=False):\n if stitch_complete:\n self.statusLabel.setText('Status: Saving Stitched Acquisition')\n else:\n self.statusLabel.setText('Status: Saving Stitched Region')\n self.statusLabel.setVisible(True)\n self.progressBar.setRange(0, 0) # indeterminate mode.\n self.progressBar.setVisible(True)\n\n def finishedSaving(self, output_path, dtype):\n if self.stitcherThread is not None:\n self.stitcherThread.quit()\n self.stitcherThread.deleteLater()\n self.statusLabel.setVisible(False)\n self.progressBar.setVisible(False)\n self.viewOutputButton.setVisible(True)\n self.viewOutputButton.setStyleSheet(\"background-color: #C2C2FF\")\n self.viewOutputButton.setEnabled(True)\n try:\n self.viewOutputButton.clicked.disconnect()\n except TypeError:\n pass\n self.viewOutputButton.clicked.connect(self.viewOutputNapari)\n\n self.output_path = output_path\n\n def extractWavelength(self, name):\n # Split the string and find the wavelength number immediately after \"Fluorescence\"\n parts = name.split()\n if 'Fluorescence' in parts:\n index = parts.index('Fluorescence') + 1\n if index < len(parts):\n return parts[index].split()[0] # Assuming '488 nm Ex' and taking '488'\n for color in ['R', 'G', 'B']:\n if color in parts or \"full_\" + color in parts:\n return color\n return None\n\n def generateColormap(self, channel_info):\n \"\"\"Convert a HEX value to a normalized RGB tuple.\"\"\"\n c0 = (0, 0, 0)\n c1 = (((channel_info['hex'] >> 16) & 0xFF) / 255, # Normalize the Red component\n ((channel_info['hex'] >> 8) & 0xFF) / 255, # Normalize the Green component\n (channel_info['hex'] & 0xFF) / 255) # Normalize the Blue component\n return Colormap(colors=[c0, c1], controls=[0, 1], name=channel_info['name'])\n\n def updateContrastLimits(self, channel, min_val, max_val):\n self.contrastManager.update_limits(channel, min_val, max_val)\n\n def viewOutputNapari(self):\n try:\n napari_viewer = napari.Viewer()\n if \".ome.zarr\" in self.output_path:\n napari_viewer.open(self.output_path, plugin='napari-ome-zarr')\n else:\n napari_viewer.open(self.output_path)\n\n for layer in napari_viewer.layers:\n layer_name = layer.name.replace(\"_\", \" \").replace(\"full \", \"full_\")\n channel_info = CHANNEL_COLORS_MAP.get(self.extractWavelength(layer_name), {'hex': 0xFFFFFF, 'name': 'gray'})\n\n if channel_info['name'] in AVAILABLE_COLORMAPS:\n layer.colormap = AVAILABLE_COLORMAPS[channel_info['name']]\n else:\n layer.colormap = self.generateColormap(channel_info)\n\n min_val, max_val = self.contrastManager.get_limits(layer_name)\n layer.contrast_limits = (min_val, max_val)\n\n except Exception as e:\n QMessageBox.critical(self, \"Error Opening in Napari\", str(e))\n print(f\"An error occurred while opening output in Napari: {e}\")\n\n def resetUI(self):\n self.output_path = \"\"\n\n # Reset UI components to their default states\n self.applyFlatfieldCheck.setChecked(False)\n self.outputFormatCombo.setCurrentIndex(0) # Assuming the first index is the default\n self.useRegistrationCheck.setChecked(False)\n self.registrationChannelCombo.clear() # Clear existing items\n self.registrationChannelLabel.setVisible(False)\n self.registrationChannelCombo.setVisible(False)\n\n # Reset the visibility and state of buttons and labels\n self.viewOutputButton.setEnabled(False)\n self.viewOutputButton.setVisible(False)\n self.progressBar.setValue(0)\n self.progressBar.setVisible(False)\n self.statusLabel.setText(\"Status: Image Acquisition\")\n self.statusLabel.setVisible(False)\n\n def closeEvent(self, event):\n if self.stitcherThread is not None:\n self.stitcherThread.quit()\n self.stitcherThread.wait()\n self.stitcherThread.deleteLater()\n self.stitcherThread = None\n super().closeEvent(event)\n\n\nclass NapariLiveWidget(QWidget):\n signal_coordinates_clicked = Signal(int, int, int, int)\n signal_newExposureTime = Signal(float)\n signal_newAnalogGain = Signal(float)\n signal_autoLevelSetting = Signal(bool)\n\n def __init__(self, streamHandler, liveController, navigationController, configurationManager, contrastManager, wellSelectionWidget=None, show_trigger_options=True, show_display_options=True, show_autolevel=False, autolevel=False, parent=None):\n super().__init__(parent)\n self.streamHandler = streamHandler\n self.liveController = liveController\n self.navigationController = navigationController\n self.configurationManager = configurationManager\n self.wellSelectionWidget = wellSelectionWidget\n self.live_configuration = self.liveController.currentConfiguration\n self.image_width = 0\n self.image_height = 0\n self.dtype = np.uint8\n self.channels = set()\n self.init_live = False\n self.init_live_rgb = False\n self.init_scale = False\n self.previous_scale = None\n self.previous_center = None\n self.last_was_autofocus = False\n self.fps_trigger = 10\n self.fps_display = 10\n self.contrastManager = contrastManager\n\n self.initNapariViewer()\n self.addNapariGrayclipColormap()\n self.initControlWidgets(show_trigger_options, show_display_options, show_autolevel, autolevel)\n self.update_microscope_mode_by_name(self.live_configuration.name)\n\n def initNapariViewer(self):\n self.viewer = napari.Viewer(show=False)\n self.viewerWidget = self.viewer.window._qt_window\n self.viewer.dims.axis_labels = ['Y-axis', 'X-axis']\n self.layout = QVBoxLayout()\n self.layout.addWidget(self.viewerWidget)\n self.setLayout(self.layout)\n self.customizeViewer()\n\n def customizeViewer(self):\n # Hide the status bar (which includes the activity button)\n if hasattr(self.viewer.window, '_status_bar'):\n self.viewer.window._status_bar.hide()\n\n # Hide the layer buttons\n if hasattr(self.viewer.window._qt_viewer, 'layerButtons'):\n self.viewer.window._qt_viewer.layerButtons.hide()\n\n def updateHistogram(self, layer):\n if self.histogram_widget is not None and layer.data is not None:\n self.pg_image_item.setImage(layer.data, autoLevels=False)\n self.histogram_widget.setLevels(*layer.contrast_limits)\n self.histogram_widget.setHistogramRange(layer.data.min(), layer.data.max())\n\n # Set the histogram widget's region to match the layer's contrast limits\n self.histogram_widget.region.setRegion(layer.contrast_limits)\n\n # Update colormap only if it has changed\n if hasattr(self, 'last_colormap') and self.last_colormap != layer.colormap.name:\n self.histogram_widget.gradient.setColorMap(self.createColorMap(layer.colormap))\n self.last_colormap = layer.colormap.name\n\n def createColorMap(self, colormap):\n colors = colormap.colors\n positions = np.linspac"}}},{"rowIdx":570454,"cells":{"sha256":{"kind":"string","value":"bc301d944570dd1d080dd947cc79d241b86d82c0f46ad11dac7179469ee83efb"},"language":{"kind":"string","value":"Python"},"size":{"kind":"number","value":200050,"string":"200,050"},"lines":{"kind":"number","value":3913,"string":"3,913"},"content":{"kind":"string","value":"\"\"\"Registration tab - main workflow UI for brain-to-atlas registration.\r\n\r\nContains:\r\n- _load_image_volume(): helper to load TIFF/NIfTI volumes for preview\r\n- RegistrationTab(QWidget): the primary registration workflow tab\r\n\"\"\"\r\n\r\nimport os\r\nimport re\r\nimport sys\r\nimport time\r\nimport subprocess\r\n\r\nimport numpy as np\r\n\r\nfrom PySide6.QtWidgets import (\r\n QVBoxLayout,\r\n QHBoxLayout,\r\n QWidget,\r\n QLabel,\r\n QFileDialog,\r\n QCheckBox,\r\n QPushButton,\r\n QComboBox,\r\n QSpinBox,\r\n QDoubleSpinBox,\r\n QMessageBox,\r\n QScrollArea,\r\n QProgressBar,\r\n QGroupBox,\r\n QFrame,\r\n QSplitter,\r\n QDialog,\r\n QSizePolicy,\r\n QButtonGroup,\r\n)\r\nfrom PySide6.QtGui import QIcon\r\nfrom PySide6.QtCore import Qt, QSize, QObject, QThread, QTimer, Signal\r\n\r\nfrom regtools.utils.io.volume_io import load_nifti_obj, save_nifti\r\nimport regtools.gui.gui_constants as gc\r\nfrom regtools.gui.gui_constants import (\r\n create_section_divider,\r\n tag_themed,\r\n)\r\nfrom regtools.gui.icons import themed_icon, register_icon_reapply\r\nfrom regtools.gui.widgets import MultiLineButton, DropButton, FlowLayout\r\nfrom regtools.gui.worker import GuiThreadRelay\r\nfrom regtools.registration.core.laplacian.defaults import DEFAULT_LAPLACIAN_RTOL\r\nfrom regtools.registration.gui.cli_args import build_registration_cli_args, RegistrationOpts\r\nfrom regtools.registration.gui.session_state import (\r\n load_session_state,\r\n save_session_state,\r\n)\r\nfrom regtools.registration.gui.job_bat import build_job_batch_lines\r\nfrom regtools.registration.gui.load_error import build_load_error_message\r\nfrom regtools.registration.run_status import (\r\n write_cancelled_marker,\r\n clear_incomplete_marker,\r\n is_run_incomplete,\r\n)\r\nfrom regtools.registration.gui.terminal_job import _TerminalJobMixin\r\nfrom regtools.registration.gui.workers import (\r\n _load_image_volume,\r\n _MetaWorker,\r\n)\r\nfrom regtools.registration.gui import help_widgets as _help\r\nfrom regtools.registration.gui.preview_panel import (\r\n InputAtlasPreviewPanel,\r\n ForegroundInitPreview,\r\n load_preview_array,\r\n load_template_preview,\r\n load_template_preview_with_affine,\r\n load_template_preview_with_meta,\r\n)\r\n\r\n\r\n_PARCELLATION_LABELS = {\r\n \"standard\": \"Standard CCF\",\r\n \"2022\": \"CCF 2022\",\r\n \"symmetric\": \"Symmetric\",\r\n \"paxinos\": \"Franklin-Paxinos\",\r\n}\r\n\r\n\r\ndef _last_axis_ml_affine():\r\n \"\"\"A 4x4 affine declaring \"the LAST voxel axis is medio-lateral, positive\".\r\n\r\n Used for a custom fixed image whose format carries no affine at all (TIFF,\r\n NumPy). There is no anatomical polarity to recover, so the convention is\r\n the one ``halve_and_pad(axis=1)`` already applies to a 2-D slice: higher\r\n index is anatomical right. The preview's L/R markers make a wrong guess\r\n visible, and the existing horizontal-flip control corrects it.\r\n\r\n Built as a real permutation (world X <- axis 2, Y <- axis 0, Z <- axis 1)\r\n rather than the identity, so it is a WELL-FORMED orientation: the plane\r\n records ``('A', 'S', 'R')`` and stays convertible into another frame. A\r\n degenerate matrix would make ``reoriented_to`` refuse.\r\n \"\"\"\r\n affine = np.zeros((4, 4), dtype=np.float64)\r\n affine[0, 2] = 1.0 # world X (R+) <- voxel axis 2\r\n affine[1, 0] = 1.0 # world Y (A+) <- voxel axis 0\r\n affine[2, 1] = 1.0 # world Z (S+) <- voxel axis 1\r\n affine[3, 3] = 1.0\r\n return affine\r\n\r\n\r\ndef _safe_dir_component(name):\r\n \"\"\"Make a string safe to use as an auto-generated output directory name.\r\n\r\n Replaces characters that break downstream tooling - commas and brackets,\r\n which ITK's ``[transform,invert]`` argument parser mis-reads during ANTs\r\n point transformation (microscope exports often embed a comma-separated\r\n channel list in the file name, e.g. ``..._DAPI, FITC, ORNG_06``), plus\r\n whitespace - with underscores, then collapses runs of underscores. Returns\r\n ``\"registration\"`` if nothing usable remains.\r\n \"\"\"\r\n cleaned = re.sub(r\"[,\\[\\]\\s]+\", \"_\", name)\r\n cleaned = re.sub(r\"_+\", \"_\", cleaned).strip(\"_\")\r\n return cleaned or \"registration\"\r\n\r\n\r\ndef _make_annotation_sdf_target(annotation_path, output_path, *, band_px=8):\r\n \"\"\"Create a clipped signed-distance fixed target from annotation labels.\"\"\"\r\n from scipy.ndimage import distance_transform_edt\r\n from regtools.utils.io.volume_io import load_volume\r\n\r\n vd = load_volume(annotation_path, reorient=None, orient=False)\r\n if getattr(vd, \"error\", None):\r\n raise RuntimeError(vd.error)\r\n labels = np.squeeze(np.asarray(vd.data))\r\n mask = labels > 0\r\n if not np.any(mask):\r\n raise ValueError(\"Annotation contains no nonzero labels; cannot build SDF target.\")\r\n sdf = distance_transform_edt(~mask) - distance_transform_edt(mask)\r\n sdf = np.clip(sdf, -float(band_px), float(band_px)).astype(np.float32)\r\n\r\n affine = np.eye(4)\r\n try:\r\n nii = vd.to_nifti()\r\n if nii is not None:\r\n affine = nii.affine\r\n except Exception:\r\n pass\r\n os.makedirs(os.path.dirname(output_path), exist_ok=True)\r\n save_nifti(sdf, affine, output_path)\r\n return output_path\r\n\r\n\r\nclass H5adFieldDialog(QDialog):\r\n \"\"\"Modal picker mapping an .h5ad's obsm/obs fields to Z/Y/X + units.\r\n\r\n AnnData has no fixed spatial-coordinate convention, so the user states\r\n which field is which axis rather than the loader guessing. ``.mapping``\r\n returns the kwargs dict for read_h5ad_points, or None if under-specified\r\n (fewer than two axes chosen).\r\n \"\"\"\r\n\r\n def __init__(self, parent=None, path=None):\r\n super().__init__(parent)\r\n self.setWindowTitle(\"h5ad Coordinate Fields\")\r\n self._path = path\r\n self._forced = None\r\n from regtools.utils.io.loaders.h5ad_points import list_h5ad_fields\r\n\r\n self._fields = list_h5ad_fields(path) if path else {\"obsm\": {}, \"obs\": []}\r\n\r\n # Source option tokens: \"obsm:spatial[0]\" (per column) and \"obs:center_x\".\r\n self._options = []\r\n for key, ncols in self._fields[\"obsm\"].items():\r\n for c in range(ncols):\r\n self._options.append(f\"obsm:{key}[{c}]\")\r\n for col in self._fields[\"obs\"]:\r\n self._options.append(f\"obs:{col}\")\r\n\r\n layout = QVBoxLayout(self)\r\n layout.setSpacing(10)\r\n layout.addWidget(QLabel(\"Map each axis to an h5ad field. Leave Z as (none) for 2D sections.\"))\r\n\r\n self._combos = {}\r\n for axis in (\"Z\", \"Y\", \"X\"):\r\n row = QHBoxLayout()\r\n row.addWidget(QLabel(f\"{axis}:\"))\r\n combo = QComboBox()\r\n combo.addItem(\"(none)\")\r\n for opt in self._options:\r\n combo.addItem(opt)\r\n self._combos[axis] = combo\r\n row.addWidget(combo, stretch=1)\r\n layout.addLayout(row)\r\n\r\n urow = QHBoxLayout()\r\n urow.addWidget(QLabel(\"Units:\"))\r\n self._units = QComboBox()\r\n self._units.addItems([\"um\", \"px\"])\r\n urow.addWidget(self._units, stretch=1)\r\n layout.addLayout(urow)\r\n\r\n srow = QHBoxLayout()\r\n srow.addWidget(QLabel(\"Spacing:\"))\r\n self._spacing = QDoubleSpinBox()\r\n self._spacing.setDecimals(4)\r\n self._spacing.setRange(0.0001, 100000.0)\r\n self._spacing.setValue(25.0)\r\n self._spacing.setSuffix(\" um/px\")\r\n srow.addWidget(self._spacing, stretch=1)\r\n layout.addLayout(srow)\r\n\r\n confirm = QPushButton(\"Confirm\")\r\n confirm.clicked.connect(self.accept)\r\n crow = QHBoxLayout()\r\n crow.addStretch(1)\r\n crow.addWidget(confirm)\r\n layout.addLayout(crow)\r\n\r\n def source_options(self):\r\n return list(self._options)\r\n\r\n def set_mapping_for_test(self, *, obsm_key=None, cols=None, order, units, spacing_um):\r\n \"\"\"Test hook: force selections without driving the widgets.\"\"\"\r\n forced = {\"order\": order, \"units\": units, \"spacing_um\": spacing_um}\r\n if obsm_key is not None:\r\n forced[\"obsm_key\"] = obsm_key\r\n if cols is not None:\r\n forced[\"cols\"] = cols\r\n self._forced = forced\r\n\r\n @property\r\n def mapping(self):\r\n if self._forced is not None:\r\n return dict(self._forced)\r\n return self._build_mapping_from_widgets()\r\n\r\n def _build_mapping_from_widgets(self):\r\n picked = [] # (axis_letter, token)\r\n for axis, letter in ((\"Z\", \"z\"), (\"Y\", \"y\"), (\"X\", \"x\")):\r\n tok = self._combos[axis].currentText()\r\n if tok and tok != \"(none)\":\r\n picked.append((letter, tok))\r\n if len(picked) < 2:\r\n return None\r\n order = \"\".join(a for a, _ in picked)\r\n toks = [t for _, t in picked]\r\n units = self._units.currentText()\r\n spacing = float(self._spacing.value()) if units == \"um\" else None\r\n # All axes from one obsm matrix -> explicit obsm_cols (so the loader\r\n # honours which column the user assigned to each axis; positional\r\n # order alone would silently transpose a stored-as-(x,y) matrix).\r\n if all(t.startswith(\"obsm:\") for t in toks):\r\n keys = {t.split(\":\", 1)[1].split(\"[\", 1)[0] for t in toks}\r\n if len(keys) != 1:\r\n return None # axes drawn from different obsm keys - unsupported\r\n key = keys.pop()\r\n obsm_cols = [int(t.split(\"[\", 1)[1].rstrip(\"]\")) for t in toks]\r\n return {\"obsm_key\": key, \"obsm_cols\": obsm_cols, \"order\": order, \"units\": units, \"spacing_um\": spacing}\r\n # All axes from obs columns.\r\n if all(t.startswith(\"obs:\") for t in toks):\r\n cols = [t.split(\":\", 1)[1] for t in toks]\r\n return {\"cols\": cols, \"order\": order, \"units\": units, \"spacing_um\": spacing}\r\n return None # mixed obsm/obs sources - unsupported\r\n\r\n\r\nclass CustomAtlasDialog(QDialog):\r\n \"\"\"Modal picker for custom template/annotation atlas inputs.\"\"\"\r\n\r\n def __init__(self, parent=None, *, file_filter=None):\r\n super().__init__(parent)\r\n self.setWindowTitle(\"Custom Atlas\")\r\n self.template_path = None\r\n self.annotation_path = None\r\n self._filter = file_filter or \"All files (*)\"\r\n\r\n layout = QVBoxLayout(self)\r\n layout.setSpacing(10)\r\n\r\n self.template_label = QLabel(\"Template: none\")\r\n self.annotation_label = QLabel(\"Annotation: none\")\r\n template_btn = QPushButton(\"Template\")\r\n annotation_btn = QPushButton(\"Annotation\")\r\n confirm_btn = QPushButton(\"Confirm\")\r\n\r\n template_btn.clicked.connect(self._choose_template)\r\n annotation_btn.clicked.connect(self._choose_annotation)\r\n confirm_btn.clicked.connect(self.accept)\r\n\r\n row1 = QHBoxLayout()\r\n row1.addWidget(template_btn)\r\n row1.addWidget(self.template_label, stretch=1)\r\n layout.addLayout(row1)\r\n\r\n row2 = QHBoxLayout()\r\n row2.addWidget(annotation_btn)\r\n row2.addWidget(self.annotation_label, stretch=1)\r\n layout.addLayout(row2)\r\n\r\n # Optional in-plane pixel-size override. Image-format atlases (TIFF/PNG)\r\n # carry no spacing metadata; unticked, they default to ANTs unit spacing\r\n # (1 mm/px isotropic) - a neutral \"resolution unknown\" default. Tick this\r\n # to pin the true µm/px. NIfTI/NRRD/DICOM templates use their own header\r\n # either way (this is ignored for them).\r\n self.spacing_check = QCheckBox(\"Override pixel size (µm/px)\")\r\n self.spacing_check.setToolTip(\r\n \"For image-format atlases (TIFF/PNG/...) that lack spacing metadata. \"\r\n \"Unticked, they default to ANTs unit spacing (1 mm/px). Tick to pin \"\r\n \"the true µm/px. NIfTI/NRRD/DICOM headers already carry spacing, so \"\r\n \"this is ignored for them.\"\r\n )\r\n self.spacing_spin = QDoubleSpinBox()\r\n self.spacing_spin.setDecimals(4)\r\n self.spacing_spin.setRange(0.0001, 100000.0)\r\n self.spacing_spin.setValue(25.0)\r\n self.spacing_spin.setSuffix(\" µm/px\")\r\n self.spacing_spin.setEnabled(False)\r\n self.spacing_check.toggled.connect(self.spacing_spin.setEnabled)\r\n\r\n row3 = QHBoxLayout()\r\n row3.addWidget(self.spacing_check)\r\n row3.addWidget(self.spacing_spin, stretch=1)\r\n layout.addLayout(row3)\r\n\r\n confirm_row = QHBoxLayout()\r\n confirm_row.addStretch(1)\r\n confirm_row.addWidget(confirm_btn)\r\n layout.addLayout(confirm_row)\r\n\r\n @property\r\n def um_per_px(self):\r\n \"\"\"Chosen in-plane pixel size (µm/px), or None when not overridden.\"\"\"\r\n if self.spacing_check.isChecked():\r\n return float(self.spacing_spin.value())\r\n return None\r\n\r\n def _choose_template(self):\r\n path = QFileDialog.getOpenFileName(self, \"Select template\", filter=self._filter)[0]\r\n if path:\r\n self.template_path = path\r\n self.template_label.setText(f\"Template: {os.path.basename(path)}\")\r\n\r\n def _choose_annotation(self):\r\n path = QFileDialog.getOpenFileName(self, \"Select annotation\", filter=self._filter)[0]\r\n if path:\r\n self.annotation_path = path\r\n self.annotation_label.setText(f\"Annotation: {os.path.basename(path)}\")\r\n\r\n\r\ndef _warm_foreground_init_imports():\r\n \"\"\"Import the Foreground-Init preview's heavy dependencies ahead of time.\r\n\r\n Clicking the \"Foreground Init\" tab calls ForegroundInitPreview.update_preview,\r\n whose first act is a cold import of the registration_2d core stack + skimage's\r\n Triangle threshold (function-level in _clean_foreground_mask) - a multi-second\r\n import that, run on the GUI thread, FROZE the GUI on first click. Warming these\r\n on a daemon thread at tab construction means they are already in sys.modules by\r\n the time the user clicks, so the click pays only the (fast, downsampled) compute.\r\n Pure imports -> safe on a background thread; best-effort (the real call still\r\n works if this fails, just slower once).\r\n \"\"\"\r\n try:\r\n import regtools.registration.registration_2d # noqa: F401 (core stack)\r\n from skimage.filters import threshold_triangle # noqa: F401\r\n from scipy import ndimage # noqa: F401\r\n except Exception:\r\n pass\r\n\r\n\r\nclass _PreviewInputWorker(QObject):\r\n \"\"\"Load a downsampled preview array (+ native shape/spacing/affine/orientation)\r\n off the UI thread.\"\"\"\r\n\r\n result = Signal(object, object, object, object, object) # (arr, native_shape, spacing, affine, orientation)\r\n error = Signal() # emitted on failure\r\n\r\n def __init__(self, path):\r\n super().__init__()\r\n self._path = path\r\n\r\n def run(self):\r\n try:\r\n from regtools.registration.gui.preview_panel import load_preview_array_with_meta\r\n\r\n # max_inplane=512 (vs the 256 default) keeps the input preview at least\r\n # as sharp as the template pane (which loads at 384); at 256 a wide\r\n # section is strided down and upscaled in the pane -> visibly blocky.\r\n arr, shape, spacing, affine, orientation = load_preview_array_with_meta(self._path, max_inplane=512)\r\n self.result.emit(arr, shape, spacing, affine, orientation)\r\n except Exception:\r\n self.error.emit()\r\n\r\n\r\nclass _PreviewResultWorker(QObject):\r\n \"\"\"Load a downsampled registered-result preview array off the UI thread.\r\n\r\n Mirrors :class:`_PreviewInputWorker` but only needs the array (the result is\r\n already in atlas space; its shape/spacing subtitle isn't shown). Any failure\r\n emits ``error`` so the caller degrades silently instead of raising into the GUI.\r\n \"\"\"\r\n\r\n result = Signal(object) # np.ndarray on success\r\n error = Signal() # emitted on failure\r\n\r\n def __init__(self, path, is_2d):\r\n super().__init__()\r\n self._path = path\r\n self._is_2d = is_2d\r\n\r\n def run(self):\r\n try:\r\n # Load like the TEMPLATE (PIR for a 3D result), not raw: the result is\r\n # in the fixed/atlas grid, so it must be oriented the same way the atlas\r\n # pane is (load_nii_pir) or the overlay would be transposed for any\r\n # atlas whose on-disk axes aren't already PIR (e.g. some Waxholm exports).\r\n from regtools.registration.gui.preview_panel import load_template_preview\r\n\r\n self.result.emit(load_template_preview(self._path, self._is_2d))\r\n except Exception:\r\n self.error.emit()\r\n\r\n\r\nclass _PreviewTemplateWorker(QObject):\r\n \"\"\"Load the atlas/template preview array (PIR-oriented) off the UI thread.\r\n\r\n Emits the array AND its own 4x4 affine. The pane needs the affine of the\r\n array it is actually handed - not of the file on disk - to convert the\r\n atlas's fitted midsagittal plane into that array's voxel frame; keeping\r\n them in one emission is what stops the two drifting apart.\r\n \"\"\"\r\n\r\n # (np.ndarray, 4x4 affine or None, pre-stride PIR shape or None)\r\n result = Signal(object, object, object)\r\n error = Signal() # emitted on failure\r\n\r\n def __init__(self, path, is_2d):\r\n super().__init__()\r\n self._path = path\r\n self._is_2d = is_2d\r\n\r\n def run(self):\r\n try:\r\n arr, affine, full_shape = load_template_preview_with_meta(self._path, self._is_2d)\r\n self.result.emit(arr, affine, full_shape)\r\n except Exception:\r\n self.error.emit()\r\n\r\n\r\nclass _PreviewAnnotationWorker(QObject):\r\n \"\"\"Load the annotation label volume + color LUT off the UI thread.\r\n\r\n Strided/oriented IDENTICALLY to the template preview (via\r\n ``load_annotation_preview``) so the labels land on the atlas pane. Any\r\n failure (missing/unreadable annotation, bad CSV) emits ``error`` so the\r\n caller clears the overlay instead of raising into the GUI thread.\r\n \"\"\"\r\n\r\n result = Signal(object, object) # (labels int32 volume, lut (N,3) uint8)\r\n error = Signal()\r\n\r\n def __init__(self, ann_path, atlas_csv, is_2d):\r\n super().__init__()\r\n self._ann = ann_path\r\n self._csv = atlas_csv\r\n self._is2d = is_2d\r\n\r\n def run(self):\r\n try:\r\n from regtools.registration.gui.preview_panel import (\r\n load_annotation_preview,\r\n build_label_lut,\r\n build_annotation_display,\r\n )\r\n\r\n labels = load_annotation_preview(self._ann, self._is2d)\r\n # build_annotation_display bounds the LUT height: a custom annotation\r\n # may carry raw structure / instance IDs in the hundreds of millions,\r\n # and colorize_annotation indexes the LUT densely by label value.\r\n base_lut = build_label_lut(self._csv) if self._csv else None\r\n display_labels, lut = build_annotation_display(labels, base_lut)\r\n self.result.emit(display_labels, lut)\r\n except Exception:\r\n self.error.emit()\r\n\r\n\r\nclass _TemplateMetadataWorker(QObject):\r\n \"\"\"Load the template object (header/data) off the UI thread.\r\n\r\n load_nifti_obj / load_volume block the GUI hard on a slow or network share\r\n (same failure mode as the input metadata load). Detect + load in a worker so\r\n selecting a template never freezes the window.\r\n \"\"\"\r\n\r\n result = Signal(object) # nibabel image on success\r\n error = Signal(str, str) # (message, exception-type-name) on failure\r\n\r\n def __init__(self, path, um_per_px=None):\r\n super().__init__()\r\n self._path = path\r\n # Optional custom-atlas in-plane pixel-size override (µm/px). The unified\r\n # loader reads a TIFF's voxel_spacing in MICROMETRES (Z,Y,X) and ignores\r\n # it for NIfTI/NRRD/DICOM, so an isotropic (µm,µm,µm) tuple corrects a\r\n # spacing-less image-format atlas and is harmless for header formats.\r\n self._um_per_px = um_per_px\r\n\r\n def run(self):\r\n try:\r\n from regtools.utils.io.volume_io import detect_format\r\n\r\n fmt, _err = detect_format(self._path)\r\n if fmt == \"nifti\":\r\n nii = load_nifti_obj(self._path, pir=False) # lazy header read\r\n else:\r\n # Any OTHER format the app supports (NRRD, DICOM, TIFF, VSI,\r\n # NumPy, 2D images) goes through the unified loader, which\r\n # auto-detects + converts to a NIfTI object - the same I/O path\r\n # registration itself uses for a non-NIfTI fixed image. load_volume\r\n # does NOT raise on a bad file (returns VolumeData with .error set,\r\n # and .to_nifti() then returns None), so convert that soft failure\r\n # into an error signal - the caller's error slot then runs (dialog\r\n # + previous template preserved) instead of the success slot\r\n # receiving None.\r\n from regtools.utils.io.volume_io import load_volume, ANTS_UNIT_SPACING_UM\r\n\r\n if self._um_per_px is not None:\r\n _spacing = (self._um_per_px,) * 3 # µm override\r\n elif fmt == \"tiff\":\r\n # Spacing-less image atlas: match the run's ANTs unit default\r\n # (1mm iso) instead of the loader's 1.25µm fallback, so the\r\n # preview subtitle reflects what registration will actually use.\r\n _spacing = ANTS_UNIT_SPACING_UM\r\n else:\r\n _spacing = None # NRRD/DICOM/NumPy: keep their own metadata\r\n # These values are µm; spacing_unit='um' is a no-op for TIFF's\r\n # native µm but gives the correct µm->mm for a NumPy atlas (and is\r\n # ignored when _spacing is None). Matches the run's fixed conversion.\r\n vd = load_volume(self._path, voxel_spacing=_spacing, spacing_unit=\"um\")\r\n if getattr(vd, \"error\", None):\r\n raise RuntimeError(vd.error)\r\n nii = vd.to_nifti() # NOT .nifti_image (doesn't exist)\r\n if nii is None:\r\n raise RuntimeError(f\"Could not read template: {self._path}\")\r\n self.result.emit(nii)\r\n except Exception as e:\r\n # Carry the exception TYPE alongside the message so the dialog can\r\n # tell an internal app bug (AttributeError/KeyError on our objects)\r\n # apart from a genuinely bad file - see load_error.build_load_error_message.\r\n self.error.emit(str(e), type(e).__name__)\r\n\r\n\r\nclass _InputMetadataWorker(QObject):\r\n \"\"\"Read input-image metadata (shape/zooms/dtype) off the UI thread.\r\n\r\n A synchronous header read (load_nifti_obj) blocks the GUI hard when the file\r\n lives on a slow/unresponsive network share - the open+header round-trip can\r\n stall for the SMB timeout. Doing it here keeps the UI responsive.\r\n \"\"\"\r\n\r\n result = Signal(str) # formatted metadata string on success\r\n error = Signal(str) # error message on failure\r\n\r\n def __init__(self, path, fmt):\r\n super().__init__()\r\n self._path = path\r\n self._fmt = fmt\r\n\r\n def run(self):\r\n try:\r\n if self._fmt == \"nifti\":\r\n nii = load_nifti_obj(self._path, pir=False) # lazy header read\r\n shape = nii.header.get_data_shape()\r\n zooms = nii.header.get_zooms()\r\n dtype = nii.header.get_data_dtype()\r\n else:\r\n # NRRD/DICOM: VolumeData exposes shape/spacing/dtype directly.\r\n # It has NO .nifti_image attribute (.to_nifti() would be a needless\r\n # reconstruction), so read the fields off the container.\r\n from regtools.utils.io.volume_io import load_volume\r\n\r\n vd = load_volume(self._path)\r\n shape = vd.data.shape\r\n zooms = vd.voxel_spacing\r\n dtype = vd.dtype\r\n if len(shape) == 3:\r\n s = f\"{shape[0]}×{shape[1]}×{shape[2]}, {zooms[0]:.3f}×{zooms[1]:.3f}×{zooms[2]:.3f}mm, {dtype}\"\r\n else:\r\n s = f\"{shape}, {zooms}, {dtype}\"\r\n self.result.emit(s)\r\n except Exception as e:\r\n self.error.emit(str(e))\r\n\r\n\r\nclass _VsiMetadataWorker(QObject):\r\n \"\"\"Read VSI metadata (shape + voxel spacing) off the UI thread.\r\n\r\n VSI whole-slide images are large and ``load_volume`` decodes them fully;\r\n doing that inline froze the GUI (and skimage can't read VSI at all, so the\r\n old 2D path also popped a spurious \"metadata unreadable\" dialog). Load once\r\n in a worker and hand back a formatted metadata string plus the moving voxel\r\n size (µm/px) to pre-fill the moving-resolution spin.\r\n \"\"\"\r\n\r\n result = Signal(object) # (metadata_str, moving_um_or_None)\r\n error = Signal(str)\r\n\r\n def __init__(self, path):\r\n super().__init__()\r\n self._path = path\r\n\r\n def run(self):\r\n try:\r\n from regtools.utils.io.volume_io import load_volume\r\n\r\n vd = load_volume(self._path, reorient=None, orient=False)\r\n if getattr(vd, \"error\", None):\r\n raise RuntimeError(vd.error)\r\n shape = vd.data.shape\r\n if len(shape) >= 3:\r\n metadata_str = f\"{shape[0]}×{shape[1]}px, {shape[2]}ch, {vd.dtype}\"\r\n elif len(shape) == 2:\r\n metadata_str = f\"{shape[0]}×{shape[1]}px, {vd.dtype}\"\r\n else:\r\n metadata_str = f\"{shape}, {vd.dtype}\"\r\n moving_um = None\r\n vs = vd.voxel_spacing\r\n if vs and vs[-1]:\r\n moving_um = float(vs[-1]) * 1000.0 # mm/px -> µm/px\r\n self.result.emit((metadata_str, moving_um))\r\n except Exception as e:\r\n self.error.emit(str(e))\r\n\r\n\r\nclass RegistrationTab(_TerminalJobMixin, QWidget):\r\n def __init__(self, app, config_template_path=None):\r\n super().__init__()\r\n\r\n # Set up tab settings and layout.\r\n self.app = app\r\n self._config_template_path = config_template_path\r\n # _inner holds the content widget placed inside the scroll area.\r\n # self.layout is the VBoxLayout of _inner; all widgets are added to it.\r\n self._inner = QWidget()\r\n self.layout = QVBoxLayout()\r\n self.layout.setSpacing(4)\r\n self.layout.setContentsMargins(12, 8, 12, 8)\r\n\r\n # Guard flag: when True, _on_atlas_changed will NOT prompt for a custom\r\n # template file (used during programmatic atlas_combo changes, e.g. config\r\n # load and session restore, so the dialog doesn't fire mid-init).\r\n self._suppress_template_dialog = False\r\n\r\n # Declare variables to keep track of file paths and settings\r\n self.input_file = None\r\n self.input_path = None\r\n self.input_points_list = [] # List of input points files\r\n self._h5ad_mappings = {} # {h5ad path: field mapping dict} for --h5ad_map\r\n # Preview-only segmentation mask overlaid on the input pane. NOT part of\r\n # the run - masks are transformed from the Apply-Transforms tab.\r\n self.input_segmentation_path = None\r\n self.data_type = \"nifti\" # \"tiff\", \"nifti\", \"2d\", or \"batch_2d\"\r\n self.template = None\r\n self.template_path = None\r\n self.custom_annotation_path = None # optional custom annotation (Allen-indexed)\r\n # In-plane µm/px override for a custom image-format atlas (None = use the\r\n # file's own metadata). Fed to both the template preview load and the\r\n # registration run (RegistrationOpts.fixed_um_per_px). Reset whenever a\r\n # built-in atlas is selected.\r\n self._custom_atlas_um_per_px = None\r\n self.custom_annotation_sdt_registration = False\r\n self.output_path = None\r\n self.num_sections = 0\r\n self.y_shape = 0\r\n self.x_shape = 0\r\n self.slice_mapping_csv = None # CSV path for batch 2D slice mapping\r\n self._slice_mapping = None # {input basename: template_index} from that CSV\r\n self._slice_mapping_first = None # first row's index (fallback for the pane)\r\n self._previewed_input_path = None # which input the preview pane is showing\r\n # Multi-page TIFF semantics: when True, the pages of the selected TIFF\r\n # are stacked along the channel axis (single 2D section). When False,\r\n # pages are separate Z-sections (volume). Set by the prompt that fires\r\n # on multi-page TIFF detection in _on_meta_done.\r\n self.tif_pages_as_channels = False\r\n # When tif_pages_as_channels is True, a second prompt resolves how\r\n # to reduce the channel axis before registration:\r\n # tif_pages_reduce_mode in {'average', 'channel'}\r\n # tif_pages_channel_index: int (only consulted when mode == 'channel')\r\n self.tif_pages_reduce_mode = \"average\"\r\n self.tif_pages_channel_index = 0\r\n\r\n # 2D image transform state (set via preview dialog)\r\n self._2d_rotate_angle = 0.0 # Rotation angle in degrees (CCW, arbitrary)\r\n self._2d_flip_h = False\r\n self._2d_flip_v = False\r\n self._2d_tissuecyte = False # apply standard TissueCyte transpose+180 base orient\r\n self._template_data_cache = None # Cached template volume for slice preview\r\n self._template_is_2d = False # True when the loaded template is a 2D image\r\n self._axes_order = None # Axis permutation from AxisConfirmDialog (None = default)\r\n self._axes_flips = None # Per-axis flip dict from AxisConfirmDialog (None = no flips)\r\n self._orient_points = True # apply the image orientation to input points too\r\n # 2D optional moving mask (NIfTI file restricting ANTs metric evaluation)\r\n self.moving_mask_path = None # None = no mask\r\n # Live AtlasEditMaskController the run just saved from (set right before\r\n # _save_atlas_edit_mask is called; None otherwise - see _atlas_edit_ctrl).\r\n self._atlas_edit_controller = None\r\n # Widget currently showing a preflight-validation highlight (cleared on\r\n # next edit / after a short timeout). None when nothing is highlighted.\r\n self._highlighted_widget = None\r\n # Preview-input background worker state.\r\n # _preview_input_threads: list of (thread, task) pairs kept alive until\r\n # the thread finishes so Qt does not GC them mid-run.\r\n self._preview_input_threads: list = []\r\n # Monotonic counter incremented on each new load request; the result\r\n # handler compares its captured copy against this to drop stale results.\r\n self._preview_input_seq: int = 0\r\n # Same GC-protection + stale-drop guards for the async template load.\r\n self._preview_template_threads: list = []\r\n self._preview_template_seq: int = 0\r\n # Path of the template currently being loaded off-thread, or None.\r\n # The seq counter only DISCARDS a stale result - it does not stop the\r\n # redundant work. Without this coalescing guard, repeated refreshes\r\n # (the cache is only populated once the result lands) each spawn another\r\n # worker that decompresses the same multi-hundred-MB atlas, spiking\r\n # memory and stalling the UI. See _refresh_preview_template.\r\n self._preview_template_inflight = None\r\n # Same guards for the async annotation-overlay load (label volume + LUT\r\n # loaded off the UI thread; large volumes must never freeze the GUI).\r\n self._preview_annotation_threads: list = []\r\n self._preview_annotation_seq: int = 0\r\n # Annotation path currently being loaded off-thread, or None (see above).\r\n self._preview_annotation_inflight = None\r\n # Same guards for the async post-run result load (registered result -> the\r\n # input pane's Result overlay source; loaded downsampled off the UI thread).\r\n self._preview_result_threads: list = []\r\n self._preview_result_seq: int = 0\r\n self._annotation_data_cache = None # cached (labels, lut) for the overlay\r\n # 4x4 affine OF THE CACHED PREVIEW ARRAY (PIR-reoriented, so not the\r\n # file's own). Paired with _template_data_cache; the pane needs it to\r\n # express the midsagittal plane in that array's voxel frame.\r\n self._template_preview_affine = None\r\n self._template_preview_full_shape = None\r\n # The plane fitted on demand for a CUSTOM fixed image (None until the\r\n # user ticks the hemisphere toggle). Built-in atlases never fit: they\r\n # read the maintainer's parameters from regtools/hemisphere/planes.json.\r\n self._custom_hemisphere_plane = None\r\n # Native (header) template shape + voxel spacing (mm), captured when the\r\n # template metadata loads, forwarded to the panel for the atlas subtitle.\r\n self._template_native_shape = None\r\n self._template_native_spacing = None\r\n # Same guards for the async template *metadata* load (header/data read\r\n # off the UI thread so selecting a template never freezes the window).\r\n self._template_meta_threads: list = []\r\n self._template_meta_seq: int = 0\r\n # ...and for the async input-metadata read (off-thread to avoid a\r\n # network-drive freeze when a header read blocks the UI thread).\r\n self._input_meta_threads: list = []\r\n self._input_meta_seq: int = 0\r\n self._vsi_meta_threads: list = []\r\n self._vsi_meta_seq: int = 0\r\n # Cross-format input-selection generation. Bumped once per new input pick\r\n # (any format) so a stale metadata worker from a *previously-selected*\r\n # input of ANY format is dropped, in addition to its own per-worker seq.\r\n self._input_load_seq: int = 0\r\n # Keeps overwritten-but-still-running background QThreads referenced until\r\n # they finish, so replacing the single attribute mid-run can't GC the C++\r\n # QThread (\"Destroyed while thread is still running\" / std::terminate).\r\n self._bg_threads: list = []\r\n\r\n ###############################################################################\r\n ## FILE IO ##\r\n ###############################################################################\r\n\r\n # Title\r\n self.file_io_title = QLabel(\"Input Files\")\r\n tag_themed(self.file_io_title, \"section_title\")\r\n\r\n # Button to select folder containing data (supports drag & drop)\r\n self.input_folder_button = DropButton(\r\n \"Select Input Data\\nDrop or click to select any image format\",\r\n valid_extensions=[\r\n \".nii\",\r\n \".nii.gz\",\r\n \".tif\",\r\n \".tiff\",\r\n \".nrrd\",\r\n \".nhdr\",\r\n \".dcm\",\r\n \".ima\",\r\n \".png\",\r\n \".jpg\",\r\n \".jpeg\",\r\n \".bmp\",\r\n \".gif\",\r\n \".webp\",\r\n \".vsi\",\r\n \".npy\",\r\n \".npz\",\r\n ],\r\n accept_folders=True,\r\n )\r\n self.input_folder_button.clicked.connect(self._select_input_path)\r\n self.input_folder_button.fileDropped.connect(self._handle_input_drop)\r\n self.input_folder_button.setToolTip(\r\n \"Choose the brain/section to register (the moving image). \"\r\n \"Click to browse, or drag & drop a file or folder. \"\r\n \"Supports NIfTI, NRRD, DICOM, TIFF, VSI, common 2D images, and NumPy; \"\r\n \"the format is auto-detected.\"\r\n )\r\n tag_themed(self.input_folder_button, \"file_warning\")\r\n\r\n # Detected format label (shown above Register button after auto-detection)\r\n self.detected_format_label = QLabel(\"\")\r\n self._style_detected_format_label()\r\n self.detected_format_label.setAlignment(Qt.AlignmentFlag.AlignCenter)\r\n\r\n # Warning banner for mismatched resolutions (batch 2D)\r\n self.resolution_warning_label = QLabel(\"\")\r\n tag_themed(self.resolution_warning_label, \"warning_card\")\r\n self.resolution_warning_label.setWordWrap(True)\r\n self.resolution_warning_label.setVisible(False)\r\n\r\n # Partial-coverage banner: atlases whose field of view is smaller than\r\n # the CCF's (currently the DAPI variants - cerebrum+midbrain only).\r\n # A SEPARATE widget from resolution_warning_label above, which is driven\r\n # by _on_meta_done for batch 2D - one shared label would let the two\r\n # overwrite each other.\r\n self.coverage_warning_label = QLabel(\"\")\r\n tag_themed(self.coverage_warning_label, \"warning_card\")\r\n self.coverage_warning_label.setWordWrap(True)\r\n self.coverage_warning_label.setVisible(False)\r\n\r\n # Slice mapping CSV button (only for batch 2D)\r\n self.slice_mapping_button = DropButton(\r\n \"Select Slice Mapping CSV (Required)\\nCSV with columns: input_file, template_index\",\r\n valid_extensions=[\".csv\"],\r\n )\r\n self.slice_mapping_button.clicked.connect(self._select_slice_mapping)\r\n self.slice_mapping_button.fileDropped.connect(self._handle_slice_mapping_drop)\r\n self.slice_mapping_button.setToolTip(\r\n \"Required for Batch 2D. CSV mapping each input section to its target \"\r\n \"template slice, with columns 'input_file' and 'template_index'. \"\r\n \"Click to browse or drop a .csv file.\"\r\n )\r\n tag_themed(self.slice_mapping_button, \"file_warning\")\r\n self.slice_mapping_button.setVisible(False)\r\n\r\n # Template slice index (only shown for 2D Image mode)\r\n self.ccf_index_label = QLabel(\"Template Slice Index:\")\r\n tag_themed(self.ccf_index_label, \"param_label\")\r\n self.ccf_index_label.setToolTip(\"The coronal slice index in the CCF template to register the 2D image to\")\r\n self.ccf_index_spin = QSpinBox()\r\n self.ccf_index_spin.setRange(0, 527) # Allen CCF 25μm has 528 coronal slices\r\n self.ccf_index_spin.setValue(264) # Default to midpoint\r\n self.ccf_index_spin.setToolTip(\"Coronal slice index in the Allen CCF (0-527 at 25μm resolution)\")\r\n self.ccf_index_spin.setMinimumWidth(80)\r\n self.ccf_index_spin.valueChanged.connect(\r\n lambda v: self.preview_panel.set_ccf_index(v) if getattr(self, \"preview_panel\", None) else None\r\n )\r\n # Hidden by default\r\n self.ccf_index_label.setVisible(False)\r\n self.ccf_index_spin.setVisible(False)\r\n\r\n # Auto-detect the CCF slice index from the section via DeepSlice (2D mode).\r\n # When checked, the manual spin is disabled and the registration run passes\r\n # --auto_deepslice so the pipeline predicts the index (requires the optional\r\n # 'DeepSlice' package; a manual index always wins if provided).\r\n self.deepslice_checkbox = QCheckBox(\"Auto-detect (DeepSlice)\")\r\n self.deepslice_checkbox.setToolTip(\r\n \"Predict the CCF coronal slice index from the section using DeepSlice.\\n\"\r\n \"Requires: pip install DeepSlice. Uncheck to set the index manually.\"\r\n )\r\n self.deepslice_checkbox.toggled.connect(self._on_deepslice_auto_toggled)\r\n self.deepslice_checkbox.setVisible(False)\r\n\r\n # Interactively tilt the CCF cutting plane and export the oblique slice\r\n # as the 2D registration fixed image (+ matching annotation slice).\r\n self.tilt_slice_button = QPushButton(\"Tilt and slice template…\")\r\n self.tilt_slice_button.setToolTip(\r\n \"Interactively tilt the CCF cutting plane and export the oblique slice as the 2D registration fixed image.\"\r\n )\r\n self.tilt_slice_button.clicked.connect(self._open_tilt_slice)\r\n self.tilt_slice_button.setVisible(False)\r\n\r\n # Moving image resolution (only shown for 2D Image mode)\r\n self.moving_um_label = QLabel(\"Moving resolution:\")\r\n tag_themed(self.moving_um_label, \"param_label\")\r\n self.moving_um_label.setToolTip(\"Pixel size of the moving (input) image\")\r\n self.moving_um_spin = QDoubleSpinBox()\r\n self.moving_um_spin.setDecimals(4)\r\n self.moving_um_spin.setRange(0.0001, 10000.0)\r\n self.moving_um_spin.setValue(25.0)\r\n self.moving_um_spin.setSuffix(\" um/px\")\r\n self.moving_um_spin.setToolTip(\r\n \"Moving image pixel size in micrometers per pixel. \"\r\n \"Used to register at true physical scale. If unknown, check the box.\"\r\n )\r\n self.moving_um_unknown = QCheckBox(\"Don't know\")\r\n self.moving_um_unknown.setToolTip(\r\n \"Do not pass physical spacing for the moving image; registration uses equal unit spacing.\"\r\n )\r\n self.moving_um_unknown.toggled.connect(self._on_moving_um_unknown)\r\n # Status text for the unknown-spacing mode.\r\n self.moving_um_estimate_label = QLabel(\"\")\r\n tag_themed(self.moving_um_estimate_label, \"description\")\r\n # Default to \"Don't know\" - most sections have no reliable µm/px, and this\r\n # matches the batch-2D default (batch_2d_model.moving_um_unknown=True). Set\r\n # after the widgets exist and before the fg-init-preview refresh is wired\r\n # (line ~1130) so only _on_moving_um_unknown (disables the spin) fires here.\r\n self.moving_um_unknown.setChecked(True)\r\n # Hidden by default (shown only in 2D mode)\r\n self.moving_um_label.setVisible(False)\r\n self.moving_um_spin.setVisible(False)\r\n self.moving_um_unknown.setVisible(False)\r\n self.moving_um_estimate_label.setVisible(False)\r\n\r\n # Moving mask picker (2D mode only, optional)\r\n self.moving_mask_button = QPushButton(\"Select moving mask (optional)\")\r\n self.moving_mask_button.setToolTip(\r\n \"Optional NIfTI mask that restricts where ANTs evaluates the registration metric \"\r\n \"in the moving (input) image. Useful when the image contains non-brain content \"\r\n \"(skull, embedding medium, etc.) that the optimizer should ignore. \"\r\n \"Leave unset to register the full image.\"\r\n )\r\n self.moving_mask_button.setMinimumHeight(28)\r\n self.moving_mask_button.clicked.connect(self._select_moving_mask)\r\n self.moving_mask_button.setVisible(False)\r\n\r\n self.moving_mask_clear_button = QPushButton(\"Clear mask\")\r\n self.moving_mask_clear_button.setToolTip(\"Remove the moving mask (register without mask)\")\r\n self.moving_mask_clear_button.setMinimumHeight(28)\r\n self.moving_mask_clear_button.clicked.connect(self._clear_moving_mask)\r\n self.moving_mask_clear_button.setVisible(False)\r\n\r\n self.moving_mask_label = QLabel(\"\")\r\n tag_themed(self.moving_mask_label, \"subtle_label\")\r\n self.moving_mask_label.setVisible(False)\r\n\r\n # Atlas edit mask (2D mode only, optional): paints the FIXED template,\r\n # not the moving image - draws over tears/folds/debris in the atlas\r\n # slice before registration. See atlas_edit_mask.AtlasEditMaskController.\r\n self.atlas_edit_arm_button = QPushButton(\"Edit atlas\")\r\n self.atlas_edit_arm_button.setToolTip(\r\n \"Arm atlas-edit drawing on the atlas pane: brush/polygon strokes \"\r\n \"mark template pixels to replace with the fill value below before \"\r\n \"registration (e.g. to blank out a tear or fold).\"\r\n )\r\n self.atlas_edit_arm_button.setCheckable(True)\r\n self.atlas_edit_arm_button.setMinimumHeight(28)\r\n self.atlas_edit_arm_button.toggled.connect(self._on_atlas_edit_armed_toggled)\r\n self.atlas_edit_arm_button.setVisible(False)\r\n\r\n self.atlas_edit_brush_button = QPushButton(\"Brush\")\r\n self.atlas_edit_brush_button.setCheckable(True)\r\n self.atlas_edit_brush_button.setChecked(True)\r\n self.atlas_edit_brush_button.setEnabled(False)\r\n self.atlas_edit_brush_button.toggled.connect(self._on_atlas_edit_tool_changed)\r\n self.atlas_edit_brush_button.setVisible(False)\r\n\r\n self.atlas_edit_polygon_button = QPushButton(\"Polygon\")\r\n self.atlas_edit_polygon_button.setCheckable(True)\r\n self.atlas_edit_polygon_button.setEnabled(False)\r\n self.atlas_edit_polygon_button.toggled.connect(self._on_atlas_edit_tool_changed)\r\n self.atlas_edit_polygon_button.setVisible(False)\r\n\r\n # Brush / Polygon are mutually exclusive tool choices.\r\n self._atlas_edit_tool_group = QButtonGroup(self)\r\n self._atlas_edit_tool_group.setExclusive(True)\r\n self._atlas_edit_tool_group.addButton(self.atlas_edit_brush_button)\r\n self._atlas_edit_tool_group.addButton(self.atlas_edit_polygon_button)\r\n\r\n self.atlas_edit_eraser_button = QPushButton(\"Eraser\")\r\n self.atlas_edit_eraser_button.setCheckable(True)\r\n self.atlas_edit_eraser_button.setEnabled(False)\r\n self.atlas_edit_eraser_button.toggled.connect(self._on_atlas_edit_eraser_toggled)\r\n self.atlas_edit_eraser_button.setVisible(False)\r\n\r\n self.atlas_edit_radius_label = QLabel(\"Radius\")\r\n self.atlas_edit_radius_label.setVisible(False)\r\n self.atlas_edit_radius_spin = QSpinBox()\r\n self.atlas_edit_radius_spin.setRange(1, 200)\r\n self.atlas_edit_radius_spin.setValue(6)\r\n self.atlas_edit_radius_spin.setSuffix(\" px\")\r\n self.atlas_edit_radius_spin.setEnabled(False)\r\n self.atlas_edit_radius_spin.valueChanged.connect(self._on_atlas_edit_radius_changed)\r\n self.atlas_edit_radius_spin.setVisible(False)\r\n\r\n self.atlas_edit_fill_label = QLabel(\"Fill value\")\r\n self.atlas_edit_fill_label.setVisible(False)\r\n self.atlas_edit_fill_spin = QDoubleSpinBox()\r\n self.atlas_edit_fill_spin.setRange(-1e6, 1e6)\r\n self.atlas_edit_fill_spin.setDecimals(2)\r\n self.atlas_edit_fill_spin.setValue(0.0)\r\n self.atlas_edit_fill_spin.setToolTip(\"Intensity written into masked template pixels (default 0 - blank).\")\r\n self.atlas_edit_fill_spin.setEnabled(False)\r\n self.atlas_edit_fill_spin.setVisible(False)\r\n\r\n self.atlas_edit_clear_button = QPushButton(\"Clear\")\r\n self.atlas_edit_clear_button.setToolTip(\"Erase the whole atlas edit mask\")\r\n self.atlas_edit_clear_button.setEnabled(False)\r\n self.atlas_edit_clear_button.clicked.connect(self._on_atlas_edit_clear)\r\n self.atlas_edit_clear_button.setVisible(False)\r\n\r\n # Transform info label for 2D mode. Driven by the inline orientation\r\n # controls on the embedded preview panel (the old \"Adjust Alignment\" /\r\n # \"Preview Slice\" popups were removed in favour of those live controls).\r\n self.transform_info_label = QLabel(\"\")\r\n self.transform_info_label.setWordWrap(True)\r\n tag_themed(self.transform_info_label, \"subtle_label\")\r\n self.transform_info_label.setVisible(False)\r\n\r\n input_folder_desc = \"Select the input data type above, then choose the corresponding file or folder.\"\r\n self.input_folder_desc = QLabel(input_folder_desc)\r\n tag_themed(self.input_folder_desc, \"description\")\r\n self.input_folder_desc.setFixedHeight(20)\r\n\r\n # Axis-ordering info label (TIFF 3D data). Driven by the inline axis\r\n # controls on the embedded preview panel (the old AxisConfirmDialog\r\n # \"Adjust Alignment\" popup was removed in favour of those live controls).\r\n self.axes_info_label = QLabel(\"\")\r\n self.axes_info_label.setWordWrap(True)\r\n tag_themed(self.axes_info_label, \"subtle_label\")\r\n\r\n # Input points button (optional, supports drag & drop and multi-select)\r\n self.input_points_button = DropButton(\r\n \"Select Input Points Files (Optional)\\nNo cell points loaded\", valid_extensions=[\".txt\", \".csv\", \".h5ad\"]\r\n )\r\n self.input_points_button.clicked.connect(self._select_input_points)\r\n self.input_points_button.fileDropped.connect(self._handle_points_drop)\r\n self.input_points_button.setToolTip(\r\n \"Optional. Cell/point coordinate files (.txt or .csv) in the ORIGINAL \"\r\n \"input image's space; they are carried through the same transform and \"\r\n \"mapped into atlas space. With 'Apply orientation to points' on (default) \"\r\n \"any image rotation/flip/axis reorientation is applied to the points too. \"\r\n \"Click to browse or drop one or more files.\"\r\n )\r\n tag_themed(self.input_points_button, \"file\")\r\n\r\n # Segmentation mask for the preview overlay (optional). PREVIEW ONLY: it\r\n # is not passed to the run, because a mask is transformed by the\r\n # Apply-Transforms tab (--input_data --is_segmentation), not by this one.\r\n # What it buys is the check that matters before you spend a registration:\r\n # does the mask actually sit on the tissue, and does the orientation you\r\n # picked keep the two together.\r\n self.input_segmentation_button = DropButton(\r\n \"Select Segmentation for Preview (Optional)\\nNo segmentation loaded\",\r\n valid_extensions=[\".tif\", \".tiff\", \".png\", \".npy\", \".npz\", \".nii\", \".nii.gz\", \".nrrd\"],\r\n )\r\n self.input_segmentation_button.clicked.connect(self._select_input_segmentation)\r\n self.input_segmentation_button.fileDropped.connect(self._handle_segmentation_drop)\r\n self.input_segmentation_button.setToolTip(\r\n \"Optional, PREVIEW ONLY - this file is not part of the run. A \"\r\n \"Cellpose (or other) integer label image on the input's own grid; \"\r\n \"'Show segmentation' in the preview draws it over the input, one \"\r\n \"colour per object, so you can confirm it covers the anatomy and \"\r\n \"follows your orientation settings before registering. Click to \"\r\n \"browse or drop a file.\"\r\n )\r\n tag_themed(self.input_segmentation_button, \"file\")\r\n\r\n # Apply the moving-image orientation (2D rotate/flip/tissuecyte, 3D axis\r\n # permute/flip) to the input points too, so they follow the image instead\r\n # of landing on the wrong anatomy. Default ON: points are assumed to be in\r\n # the ORIGINAL (un-oriented) image frame. Also drives the preview overlay\r\n # (oriented vs raw) so misalignment is visible before running.\r\n self.orient_points_checkbox = QCheckBox(\"Apply orientation to points\")\r\n self.orient_points_checkbox.setChecked(True)\r\n self.orient_points_checkbox.setToolTip(\r\n \"When on, the same orientation applied to the input image/volume \"\r\n \"(rotate, flip, TissueCyte, or 3D axis permutation/flips) is also \"\r\n \"applied to the loaded cell points, so they stay on the same anatomy. \"\r\n \"Turn off only if your points are already in the oriented frame. \"\r\n \"Enable 'Show cell points' in the preview to see the effect.\"\r\n )\r\n self.orient_points_checkbox.toggled.connect(self._on_orient_points_toggled)\r\n\r\n # Atlas selector dropdown (built-in atlases auto-load template; custom shows file picker)\r\n self.atlas_label = QLabel(\"Atlas:\")\r\n tag_themed(self.atlas_label, \"param_label\")\r\n self.atlas_combo = QComboBox()\r\n # One item per atlas FAMILY; resolution/modality variants (25/10 \\u00b5m,\r\n # Nissl) live in the variant sub-dropdown below. Families come from the\r\n # registry so adding an atlas needs no GUI change. installed_only=False so\r\n # a not-yet-downloaded atlas still appears (selecting it shows the hint).\r\n from regtools.registration.core.config import atlas_families, family_label\r\n\r\n for _fam_key in atlas_families(installed_only=False):\r\n self.atlas_combo.addItem(family_label(_fam_key), _fam_key)\r\n self.atlas_combo.addItem(\"Custom Template...\", \"custom\")\r\n self.atlas_combo.setToolTip(_help.build_combo_tooltip(\"atlas\"))\r\n _help.apply_item_tooltips(self.atlas_combo, \"atlas\")\r\n self.atlas_combo.setMinimumWidth(130)\r\n self.selected_atlas = \"allen_ccf\" # variant-level registry key (source of truth)\r\n self.atlas_combo.currentIndexChanged.connect(self._on_atlas_changed)\r\n\r\n # Variant sub-dropdown \\u2014 shown only when the selected family has more than\r\n # one INSTALLED variant (e.g. Allen CCF 25 \\u00b5m / 10 \\u00b5m / Nissl). Its data is\r\n # the variant-level registry key, which becomes selected_atlas.\r\n self.variant_combo = QComboBox()\r\n self.variant_combo.setMinimumWidth(120)\r\n self.variant_combo.setVisible(False)\r\n self.variant_combo.setToolTip(\"Resolution / modality variant of the selected atlas\")\r\n self.variant_combo.currentIndexChanged.connect(self._on_variant_changed)\r\n\r\n # Stand-in for the combo when a family defines exactly one variant. The\r\n # combo is hidden in that case (a one-item dropdown is noise), but the\r\n # variant label is where the resolution and any caveats live - Waxholm's\r\n # \"T2* - 39 µm\", the B6 DAPI's \"8.6 x 33.3 x 8.6 µm (cerebrum+midbrain\r\n # only)\" - so without this the text had nowhere to go and was simply\r\n # never shown. Exactly one of the two is visible at any time.\r\n self.variant_static_label = QLabel(\"\")\r\n tag_themed(self.variant_static_label, \"subtle_label\")\r\n self.variant_static_label.setVisible(False)\r\n\r\n # Facet selectors. Each is shown only when the selected family has more\r\n # than one distinct value for that field, so a family without the axis\r\n # costs nothing. Registry-driven: a future family with both axes needs no\r\n # GUI change.\r\n self.parcellation_combo = QComboBox()\r\n self.parcellation_combo.setMinimumWidth(120)\r\n self.parcellation_combo.setVisible(False)\r\n self.parcellation_combo.setToolTip(\"Which parcellation (annotation) to count regions against\")\r\n self.parcellation_combo.currentIndexChanged.connect(self._on_facet_changed)\r\n\r\n self.age_combo = QComboBox()\r\n self.age_combo.setMinimumWidth(100)\r\n self.age_combo.setVisible(False)\r\n self.age_combo.setToolTip(\"Developmental stage of the selected atlas\")\r\n self.age_combo.currentIndexChanged.connect(self._on_facet_changed)\r\n\r\n # Template status / metadata label. The atlas dropdown is the sole\r\n # selector; choosing \"Custom Template...\" opens a file dialog directly.\r\n self.template_info_label = QLabel(\"No template loaded\")\r\n self.template_info_label.setWordWrap(True)\r\n # Ignored horizontal policy: a word-wrapped QLabel still reports a WIDE\r\n # minimumSizeHint (roughly its longest single line - the multi-word atlas\r\n # name), which would floor the whole controls column. Ignoring the hint\r\n # lets it shrink and wrap so the column can get narrow beside the preview.\r\n self.template_info_label.setSizePolicy(QSizePolicy.Policy.Ignored, QSizePolicy.Policy.Preferred)\r\n self.template_info_label.setToolTip(\r\n \"The fixed atlas/template to register onto. Built-in atlases load \"\r\n \"automatically; choose 'Custom Template...' in the dropdown to browse \"\r\n \"for a NIfTI, NRRD, TIFF, VSI, or NumPy file.\"\r\n )\r\n tag_themed(self.template_info_label, \"file_warning\")\r\n\r\n # Template hemisphere dropdown\r\n self.template_half_label = QLabel(\"Template Region:\")\r\n tag_themed(self.template_half_label, \"param_label\")\r\n self.template_half_combo = QComboBox()\r\n self.template_half_combo.addItem(\"Whole Brain\", \"whole\")\r\n self.template_half_combo.addItem(\"Left Half\", \"left\")\r\n self.template_half_combo.addItem(\"Right Half\", \"right\")\r\n self.template_half_combo.setToolTip(_help.build_combo_tooltip(\"template_half\"))\r\n _help.apply_item_tooltips(self.template_half_combo, \"template_half\")\r\n self.template_half_combo.setMinimumWidth(85)\r\n self.template_half_combo.currentIndexChanged.connect(self._update_template_dimensions)\r\n\r\n # Hemisphere split: automatic for a built-in atlas (its plane is shipped\r\n # in regtools/hemisphere/planes.json), opt-in for a custom fixed image\r\n # (fitting is the only way one can acquire a plane, so the cost must be\r\n # user-initiated).\r\n self.hemisphere_check = QCheckBox(\"Hemisphere split\")\r\n self.hemisphere_check.setChecked(True)\r\n # The tooltip must describe what ticking this ACTUALLY does. The\r\n # custom unlock is real now that the pipeline reads the sidecar\r\n # (_prepare_3d_inputs -> plane_for_image), but the minutes-long\r\n # synchronous freeze is also real and has to be stated up front.\r\n self.hemisphere_check.setToolTip(\r\n \"Draw this template's midsagittal midline: tick, then LEFT-click \"\r\n \"one end of the midline on the atlas preview and RIGHT-click the \"\r\n \"other. Place them towards opposite ends of the section. The line \"\r\n \"is saved beside the image, unlocks Template Region, and is the \"\r\n \"plane the run crops along - so what you draw is what it cuts. \"\r\n \"Nothing is fitted: a custom template has no annotation to score a \"\r\n \"fit against, and you can see the midline better than an optimiser \"\r\n \"can infer it. If the file cannot be written beside the image the \"\r\n \"run cannot read it back, so Template Region stays locked to Whole \"\r\n \"Brain and the status line says so.\"\r\n )\r\n self.hemisphere_check.toggled.connect(self._on_hemisphere_toggled)\r\n self.hemisphere_status_label = QLabel(\"\")\r\n self.hemisphere_status_label.setWordWrap(True)\r\n tag_themed(self.hemisphere_status_label, \"param_label\")\r\n\r\n # Preview buttons\r\n self.preview_input_button = QPushButton(\"Preview Input\")\r\n self.preview_input_button.setToolTip(\"Open the input data in napari for visualization\")\r\n self.preview_input_button.setMinimumHeight(28)\r\n self.preview_input_button.clicked.connect(self._preview_input_napari)\r\n self.preview_input_button.setEnabled(False)\r\n\r\n self.preview_template_button = QPushButton(\"Preview Template\")\r\n self.preview_template_button.setToolTip(\"Open the template in napari for visualization\")\r\n self.preview_template_button.setMinimumHeight(28)\r\n self.preview_template_button.clicked.connect(self._preview_template_napari)\r\n self.preview_template_button.setEnabled(False)\r\n\r\n # Button to select folder to output registered data (supports drag & drop)\r\n self.output_folder_button = DropButton(\r\n \"Select Output Directory (Optional)\\nAuto: output/<timestamp>/\", accept_folders=True\r\n )\r\n self.output_folder_button.clicked.connect(self._select_output_path)\r\n self.output_folder_button.fileDropped.connect(self._handle_output_drop)\r\n self.output_folder_button.setToolTip(\r\n \"Optional folder where registration results are written. \"\r\n \"Leave unset to auto-create a timestamped folder under output/. \"\r\n \"Click to browse or drop a folder.\"\r\n )\r\n tag_themed(self.output_folder_button, \"file\")\r\n\r\n output_folder_desc = \"Optional. If not set, auto-creates a timestamped folder in output/.\"\r\n self.output_folder_desc = QLabel(output_folder_desc)\r\n tag_themed(self.output_folder_desc, \"description\")\r\n self.output_folder_desc.setFixedHeight(20)\r\n\r\n # Registration method selector\r\n self.reg_method_label = QLabel(\"Registration Method:\")\r\n tag_themed(self.reg_method_label, \"param_label\")\r\n\r\n # Transform (deformation model). The similarity metric is a SEPARATE\r\n # dropdown - the two are orthogonal and were previously fused (e.g.\r\n # \"SyNRA+CC\"), which hid the CC-vs-MI choice.\r\n self.reg_method_combo = QComboBox()\r\n self.reg_method_combo.addItem(\"ANTs SyNRA (Recommended)\", \"synra\")\r\n self.reg_method_combo.addItem(\"ANTs SyN\", \"syn\")\r\n self.reg_method_combo.addItem(\"ANTs SyNAggro\", \"synaggro\")\r\n self.reg_method_combo.addItem(\"Elastix B-spline\", \"elastix\")\r\n self.reg_method_combo.setToolTip(_help.build_combo_tooltip(\"methods\"))\r\n _help.apply_item_tooltips(self.reg_method_combo, \"methods\")\r\n self.reg_method_combo.setMinimumWidth(115)\r\n self.reg_method_combo.setCurrentIndex(self.reg_method_combo.findData(\"synra\"))\r\n\r\n # Similarity metric - applies to the ANTs SyN / SyNRA transforms only.\r\n self.metric_label = QLabel(\"Metric:\")\r\n tag_themed(self.metric_label, \"param_label\")\r\n self.metric_combo = QComboBox()\r\n self.metric_combo.addItem(\"MI (recommended)\", \"mi\")\r\n self.metric_combo.addItem(\"CC (same-modality)\", \"cc\")\r\n self.metric_combo.addItem(\"Mean Squares (SSD)\", \"meansquares\")\r\n self.metric_combo.addItem(\"Demons (same-modality)\", \"demons\")\r\n self.metric_combo.addItem(\"MIND-SSD (cross-modal)\", \"mind\")\r\n self.metric_combo.addItem(\"SSC (cross-modal)\", \"ssc\")\r\n self.metric_combo.addItem(\"NGF (cross-modal, edges)\", \"ngf\")\r\n self.metric_combo.setToolTip(_help.build_combo_tooltip(\"metrics\"))\r\n _help.apply_item_tooltips(self.metric_combo, \"metrics\")\r\n self.metric_combo.setMinimumWidth(90)\r\n self.metric_combo.setCurrentIndex(self.metric_combo.findData(\"mi\"))\r\n # Connect AFTER both combos exist + initial index set so the handlers\r\n # don't fire mid-construction (parameters_tab may not exist yet).\r\n self.reg_method_combo.currentIndexChanged.connect(self._on_transform_changed)\r\n self.metric_combo.currentIndexChanged.connect(self._on_metric_changed)\r\n\r\n # Registration effort preset dropdown (Fast / Default / Thorough)\r\n self.reg_effort_label = QLabel(\"Effort:\")\r\n tag_themed(self.reg_effort_label, \"param_label\")\r\n self.reg_effort_combo = QComboBox()\r\n self.reg_effort_combo.addItem(\"Default\", \"default\")\r\n self.reg_effort_combo.addItem(\"Fast\", \"fast\")\r\n self.reg_effort_combo.addItem(\"Thorough\", \"thorough\")\r\n self.reg_effort_combo.setToolTip(_help.build_combo_tooltip(\"effort\"))\r\n _help.apply_item_tooltips(self.reg_effort_combo, \"effort\")\r\n self.reg_effort_combo.setMinimumWidth(85)\r\n self.reg_effort_combo.setCurrentIndex(self.reg_effort_combo.findData(\"default\"))\r\n\r\n # Axis alignment checkbox\r\n self.axis_align_checkbox = QCheckBox()\r\n self.axis_align_checkbox.setChecked(False) # Off by default\r\n self.axis_align_checkbox.setToolTip(_help.checkbox_tooltip(\"refinement\", \"axis_align\"))\r\n self.axis_align_label = QLabel(\"Use Axis Alignment\")\r\n tag_themed(self.axis_align_label, \"param_label\")\r\n self.axis_align_label.setToolTip(self.axis_align_checkbox.toolTip())\r\n\r\n # Laplacian refinement checkbox\r\n self.laplacian_checkbox = QCheckBox()\r\n self.laplacian_checkbox.setChecked(True) # On by default\r\n self.laplacian_checkbox.setToolTip(_help.checkbox_tooltip(\"refinement\", \"laplacian\"))\r\n self.laplacian_label = QLabel(\"Use Laplacian Refinement\")\r\n tag_themed(self.laplacian_label, \"param_label\")\r\n self.laplacian_label.setToolTip(self.laplacian_checkbox.toolTip())\r\n\r\n # Laplacian correspondence source: all contours vs the foreground silhouette\r\n # (exterior boundary) only. Same Laplacian solve - only the matched points differ.\r\n # Labels are kept short so this group fits the left controls column (the full\r\n # explanation lives in the tooltip); see test_registration_gui_layout_aspect.\r\n self.laplacian_corr_label = QLabel(\"Correspondences\")\r\n tag_themed(self.laplacian_corr_label, \"param_label\")\r\n self.laplacian_corr_combo = QComboBox()\r\n self.laplacian_corr_combo.addItem(\"All contours\", \"all\")\r\n self.laplacian_corr_combo.addItem(\"Exterior only\", \"exterior\")\r\n self.laplacian_corr_combo.addItem(\"Exterior (damage-aware)\", \"exterior_damage_aware\")\r\n # Pin a shrinkable minimum (like reg_method_combo) so the longest item does\r\n # not force the left controls column wider - it elides when squeezed.\r\n self.laplacian_corr_combo.setMinimumWidth(115)\r\n _corr_tip = (\r\n \"Where the Laplacian refinement gets its matched points.\\n\"\r\n \"- All correspondences: Canny/Otsu contours - exterior silhouette + interior edges.\\n\"\r\n \"- Exterior boundary only: just the outer boundary of the Triangle-thresholded \"\r\n \"foreground mask (a clean outline, ignoring interior texture).\\n\"\r\n \"- Exterior (damage-aware): outer boundary via seeded Multi-Otsu-4 GrabCut, with \"\r\n \"tear detection dropping correspondences on damaged/torn boundary.\"\r\n )\r\n self.laplacian_corr_combo.setToolTip(_corr_tip)\r\n self.laplacian_corr_label.setToolTip(_corr_tip)\r\n # Only meaningful when Laplacian refinement is enabled.\r\n self.laplacian_checkbox.toggled.connect(self.laplacian_corr_combo.setEnabled)\r\n self.laplacian_checkbox.toggled.connect(self.laplacian_corr_label.setEnabled)\r\n\r\n # Tear-detection sensitivity - only used by the damage-aware source.\r\n self.laplacian_damage_alpha_label = QLabel(\"Damage α\")\r\n tag_themed(self.laplacian_damage_alpha_label, \"param_label\")\r\n self.laplacian_damage_alpha_spin = QDoubleSpinBox()\r\n self.laplacian_damage_alpha_spin.setRange(1.0, 500.0)\r\n self.laplacian_damage_alpha_spin.setSingleStep(5.0)\r\n self.laplacian_damage_alpha_spin.setValue(50.0)\r\n _alpha_tip = (\r\n \"Tear-detection sensitivity (minimum medial-axis length). Lower flags \"\r\n \"shallower notches as damage; 50 is the default. Only used by the \"\r\n \"'Exterior (damage-aware)' correspondence source.\"\r\n )\r\n self.laplacian_damage_alpha_spin.setToolTip(_alpha_tip)\r\n self.laplacian_damage_alpha_label.setToolTip(_alpha_tip)\r\n\r\n def _sync_damage_alpha_enabled(*_):\r\n on = (\r\n self.laplacian_checkbox.isChecked()\r\n and self.laplacian_corr_combo.currentData() == \"exterior_damage_aware\"\r\n )\r\n self.laplacian_damage_alpha_spin.setEnabled(on)\r\n self.laplacian_damage_alpha_label.setEnabled(on)\r\n\r\n self.laplacian_checkbox.toggled.connect(_sync_damage_alpha_enabled)\r\n self.laplacian_corr_combo.currentIndexChanged.connect(_sync_damage_alpha_enabled)\r\n _sync_damage_alpha_enabled()\r\n\r\n reg_method_desc = (\r\n \"ANTs SyNRA is recommended for most cases. Elastix requires external binaries in ./bin/elastix/ folder.\"\r\n )\r\n self.reg_method_desc = QLabel(reg_method_desc)\r\n tag_themed(self.reg_method_desc, \"description\")\r\n self.reg_method_desc.setFixedHeight(20)\r\n\r\n ###############################################################################\r\n ## METADATA AND RUN APP ##\r\n ###############################################################################\r\n\r\n # Register button\r\n self.register_button = QPushButton(\"Register Data\")\r\n self.register_button.setMinimumHeight(40)\r\n self.register_button.setToolTip(\r\n \"Start registration with the current settings. Enabled once both an \"\r\n \"input and a template are selected; progress is shown in a terminal.\"\r\n )\r\n tag_themed(self.register_button, \"primary\")\r\n self.register_button.clicked.connect(self._thread_register)\r\n self.register_button.setEnabled(False)\r\n\r\n # View Results button (shown when existing output is selected)\r\n self.view_results_button = QPushButton(\"View Results\")\r\n self.view_results_button.setMinimumHeight(40)\r\n self.view_results_button.setToolTip(\"Open the saved figures/metrics for the selected existing output folder.\")\r\n tag_themed(self.view_results_button, \"secondary\")\r\n self.view_results_button.clicked.connect(self._view_existing_results)\r\n self.view_results_button.setVisible(False)\r\n\r\n # Open in Napari button (shown when existing output is selected)\r\n self.open_napari_button = QPushButton(\"Open in Napari\")\r\n self.open_napari_button.setMinimumHeight(40)\r\n self.open_napari_button.setToolTip(\r\n \"Open the existing registration result in napari \"\r\n \"(registered volume, atlas annotations, and any transformed points).\"\r\n )\r\n tag_themed(self.open_napari_button, \"secondary\")\r\n self.open_napari_button.clicked.connect(self._open_in_napari)\r\n self.open_napari_button.setVisible(False)\r\n\r\n # Open output folder button - reveals the run's output directory in the\r\n # OS file explorer. Hidden until a run completes (or an existing output\r\n # with results is selected); enabled only when an output path is known.\r\n self.open_output_button = QPushButton(\"Open Output Folder\")\r\n self.open_output_button.setMinimumHeight(40)\r\n self.open_output_button.setToolTip(\"Open the registration output directory in the file explorer.\")\r\n tag_themed(self.open_output_button, \"secondary\")\r\n self.open_output_button.clicked.connect(self._open_output_folder)\r\n self.open_output_button.setVisible(False)\r\n\r\n # Open report button - opens the run's self-contained HTML report in the\r\n # default browser (building it on demand for older runs). Hidden until a\r\n # run completes or an existing output folder is selected.\r\n self.open_report_button = QPushButton(\"Open Report\")\r\n self.open_report_button.setMinimumHeight(40)\r\n self.open_report_button.setToolTip(\r\n \"Open the self-contained HTML registration report (parameters, metrics, and figures) in your browser.\"\r\n )\r\n tag_themed(self.open_report_button, \"secondary\")\r\n self.open_report_button.clicked.connect(self._open_report)\r\n self.open_report_button.setVisible(False)\r\n\r\n # Status label - updated by the terminal-job mixin while a run is active\r\n self.status_label = QLabel(\"\")\r\n tag_themed(self.status_label, \"muted_label\")\r\n self.status_label.setVisible(False)\r\n\r\n # Indeterminate progress bar - shown while a registration run is active\r\n self.progress_bar = QProgressBar()\r\n self.progress_bar.setRange(0, 0) # indeterminate (busy)\r\n self.progress_bar.setTextVisible(False)\r\n self.progress_bar.setMaximumHeight(6)\r\n self.progress_bar.setVisible(False)\r\n\r\n # Cancel button - terminates the running terminal and resets the UI\r\n self.cancel_button = QPushButton(\"Cancel\")\r\n self.cancel_button.setMinimumHeight(40)\r\n self.cancel_button.setToolTip(\"Stop the running registration and reset the controls.\")\r\n tag_themed(self.cancel_button, \"danger\")\r\n self.cancel_button.setVisible(False)\r\n self.cancel_button.setEnabled(False)\r\n self.cancel_button.clicked.connect(self._on_cancel)\r\n\r\n ################### SETUP UI LAYOUT ###################\r\n #\r\n # The form is grouped into titled cards (theme-aware QGroupBox, styled\r\n # in APP_STYLESHEET so it tracks light/dark). Each existing widget is\r\n # RE-ADDED (reparented) into the appropriate card layout - no widget is\r\n # recreated, so every ``self.xxx`` reference, signal/slot, and the\r\n # show/hide wiring in ``_update_format_controls`` / ``_toggle_buttons``\r\n # keeps working unchanged. The run/action row is moved OUT of the\r\n # scroll area into a sticky action bar pinned at the bottom.\r\n\r\n # Card header icon labels, kept so _apply_icons() can recolor them on a\r\n # live theme switch (icon name -> the QLabel that hosts its pixmap).\r\n self._card_icon_labels = {}\r\n\r\n def _make_card(title, icon_name=None):\r\n \"\"\"Create a themed QGroupBox card with a vertical content layout.\r\n\r\n When ``icon_name`` is given, a leading icon + title header row is\r\n added at the top of the inner layout (QGroupBox titles can't host an\r\n icon), and the box itself is created title-less.\r\n \"\"\"\r\n box = QGroupBox(\"\" if icon_name else title)\r\n box_layout = QVBoxLayout()\r\n box_layout.setSpacing(6)\r\n box_layout.setContentsMargins(12, 12, 12, 12)\r\n box.setLayout(box_layout)\r\n if icon_name:\r\n header_row = QHBoxLayout()\r\n header_row.setSpacing(8)\r\n icon_label = QLabel()\r\n icon_label.setFixedSize(20, 20)\r\n icon_label.setPixmap(themed_icon(icon_name, size=18).pixmap(18, 18))\r\n self._card_icon_labels[icon_name] = icon_label\r\n title_label = QLabel(title)\r\n tag_themed(title_label, \"section_title\")\r\n header_row.addWidget(icon_label)\r\n header_row.addWidget(title_label)\r\n header_row.addStretch(1)\r\n box_layout.addLayout(header_row)\r\n return box, box_layout\r\n\r\n # ---- Card 1: Input & Template -------------------------------------\r\n self.input_template_card, input_card_layout = _make_card(self.file_io_title.text(), icon_name=\"folder\")\r\n\r\n def _flow_group(*widgets):\r\n \"\"\"Pack a label + control (+ info button) into one unit so a\r\n FlowLayout wraps the GROUP as a whole, never splitting a label from\r\n its control across lines.\"\"\"\r\n g = QWidget()\r\n gl = QHBoxLayout(g)\r\n gl.setContentsMargins(0, 0, 0, 0)\r\n gl.setSpacing(6)\r\n for w in widgets:\r\n gl.addWidget(w)\r\n return g\r\n\r\n input_row = QHBoxLayout()\r\n input_row.addWidget(self.input_folder_button, stretch=1)\r\n input_card_layout.addLayout(input_row)\r\n\r\n # Preview input button + orientation/axis echo labels. The labels wrap\r\n # (stretch) so a long transform/axis summary shrinks with the column\r\n # instead of forcing the card wide.\r\n preview_input_row = QHBoxLayout()\r\n preview_input_row.addWidget(self.preview_input_button)\r\n preview_input_row.addSpacing(12)\r\n preview_input_row.addWidget(self.transform_info_label, stretch=1)\r\n preview_input_row.addWidget(self.axes_info_label, stretch=1)\r\n input_card_layout.addLayout(preview_input_row)\r\n\r\n # Resolution warning banner (batch 2D)\r\n input_card_layout.addWidget(self.resolution_warning_label)\r\n\r\n # Points files (multi-select)\r\n input_card_layout.addWidget(self.input_points_button)\r\n input_card_layout.addWidget(self.orient_points_checkbox)\r\n # Preview-only segmentation overlay, next to the points it complements.\r\n input_card_layout.addWidget(self.input_segmentation_button)\r\n\r\n # Atlas selector row - atlas label+combo and the (optional) variant combo\r\n # wrap independently when the column is narrow.\r\n atlas_flow = FlowLayout(hspacing=12, vspacing=6)\r\n atlas_flow.addWidget(_flow_group(self.atlas_label, self.atlas_combo))\r\n atlas_flow.addWidget(\r\n _flow_group(self.variant_combo, self.variant_static_label, _help.info_button(\"atlas\", self))\r\n )\r\n atlas_flow.addWidget(_flow_group(self.parcellation_combo, self.age_combo))\r\n input_card_layout.addLayout(atlas_flow)\r\n # Sits under the atlas dropdown: the caveat belongs next to the choice.\r\n input_card_layout.addWidget(self.coverage_warning_label)\r\n\r\n # Template status label + hemisphere dropdown\r\n template_row = QHBoxLayout()\r\n template_row.addWidget(self.template_info_label, stretch=1)\r\n template_row.addWidget(self.template_half_label)\r\n template_row.addWidget(self.template_half_combo)\r\n template_row.addWidget(_help.info_button(\"template_half\", self))\r\n input_card_layout.addLayout(template_row)\r\n\r\n # Hemisphere-split toggle + the fitted midline's status line.\r\n hemisphere_row = QHBoxLayout()\r\n hemisphere_row.addWidget(self.hemisphere_check)\r\n hemisphere_row.addWidget(self.hemisphere_status_label, stretch=1)\r\n input_card_layout.addLayout(hemisphere_row)\r\n\r\n # Preview template button row\r\n preview_template_row = QHBoxLayout()\r\n preview_template_row.addWidget(self.preview_template_button)\r\n preview_template_row.addStretch(1)\r\n input_card_layout.addLayout(preview_template_row)\r\n\r\n self.layout.addWidget(self.input_template_card)\r\n\r\n # ---- Card 2: 2D Section Options (2D-only controls) ----------------\r\n # These controls already show/hide individually via\r\n # _update_format_controls; the card is shown only when at least one of\r\n # them is relevant (2D / batch_2d). Individual widget visibility is\r\n # still fully owned by _update_format_controls.\r\n self.section_2d_card, section_2d_layout = _make_card(\"2D Section Options\", icon_name=\"layers\")\r\n\r\n # Slice mapping CSV (batch 2D)\r\n section_2d_layout.addWidget(self.slice_mapping_button)\r\n\r\n # Template slice index row (2D mode)\r\n ccf_index_row = QHBoxLayout()\r\n ccf_index_row.addWidget(self.ccf_index_label)\r\n ccf_index_row.addWidget(self.ccf_index_spin)\r\n ccf_index_row.addWidget(self.deepslice_checkbox)\r\n ccf_index_row.addWidget(self.tilt_slice_button)\r\n ccf_index_row.addStretch(1)\r\n section_2d_layout.addLayout(ccf_index_row)\r\n\r\n # Moving resolution row (only visible in 2D mode)\r\n moving_um_row = QHBoxLayout()\r\n moving_um_row.addWidget(self.moving_um_label)\r\n moving_um_row.addWidget(self.moving_um_spin)\r\n moving_um_row.addSpacing(8)\r\n moving_um_row.addWidget(self.moving_um_unknown)\r\n moving_um_row.addSpacing(8)\r\n moving_um_row.addWidget(self.moving_um_estimate_label)\r\n moving_um_row.addStretch(1)\r\n section_2d_layout.addLayout(moving_um_row)\r\n\r\n # Moving mask row (only visible in 2D mode)\r\n moving_mask_row = QHBoxLayout()\r\n moving_mask_row.addWidget(self.moving_mask_button)\r\n moving_mask_row.addWidget(self.moving_mask_clear_button)\r\n moving_mask_row.addWidget(self.moving_mask_label)\r\n moving_mask_row.addStretch(1)\r\n section_2d_layout.addLayout(moving_mask_row)\r\n\r\n # Atlas edit mask row (only visible in 2D mode): tools to paint over\r\n # the FIXED template on the preview panel's atlas pane.\r\n atlas_edit_row = FlowLayout(hspacing=8, vspacing=4)\r\n atlas_edit_row.addWidget(self.atlas_edit_arm_button)\r\n atlas_edit_row.addWidget(self.atlas_edit_brush_button)\r\n atlas_edit_row.addWidget(self.atlas_edit_polygon_button)\r\n atlas_edit_row.addWidget(self.atlas_edit_eraser_button)\r\n atlas_edit_row.addWidget(_flow_group(self.atlas_edit_radius_label, self.atlas_edit_radius_spin))\r\n atlas_edit_row.addWidget(_flow_group(self.atlas_edit_fill_label, self.atlas_edit_fill_spin))\r\n atlas_edit_row.addWidget(self.atlas_edit_clear_button)\r\n section_2d_layout.addLayout(atlas_edit_row)\r\n\r\n # Match-input-resolution checkbox (ON by default for 2D).\r\n self.match_fixed_res_checkbox = QCheckBox(\"Match input resolution to template\")\r\n self.match_fixed_res_checkbox.setChecked(True)\r\n self.match_fixed_res_checkbox.setToolTip(\r\n \"Downscale the moving image so its longest side matches the \"\r\n \"template's, scaling its µm pitch (and uploaded cell points) to \"\r\n \"match. The SyN warp is computed on the fixed grid, so a moving \"\r\n \"finer than the template is wasted resolution. Downscale-only.\"\r\n )\r\n # Matching to the template rescales the moving image to its pixel grid,\r\n # so the absolute input pitch is estimated, not entered: lock the pitch\r\n # controls to 'estimate' while this is on.\r\n self.match_fixed_res_checkbox.setToolTip(\r\n \"Downscale the moving image so its post-rotation longest side fits \"\r\n \"the template's. Known pixel spacing and uploaded cell points are \"\r\n \"scaled to match. Downscale-only.\"\r\n )\r\n self.match_fixed_res_checkbox.toggled.connect(self._on_match_fixed_res)\r\n section_2d_layout.addWidget(self.match_fixed_res_checkbox)\r\n\r\n self.layout.addWidget(self.section_2d_card)\r\n\r\n # ---- Card 3: Method & Output --------------------------------------\r\n self.method_output_card, method_output_layout = _make_card(\"Method & Output\", icon_name=\"sliders\")\r\n\r\n # Method / metric / effort: three labelled dropdowns that sit on one line\r\n # when there's room but WRAP onto extra lines when the controls column is\r\n # narrowed beside the preview panel (instead of being clipped).\r\n reg_method_flow = FlowLayout(hspacing=16, vspacing=8)\r\n reg_method_flow.addWidget(\r\n _flow_group(self.reg_method_label, self.reg_method_combo, _help.info_button(\"methods\", self))\r\n )\r\n reg_method_flow.addWidget(_flow_group(self.metric_label, self.metric_combo, _help.info_button(\"metrics\", self)))\r\n reg_method_flow.addWidget(\r\n _flow_group(self.reg_effort_label, self.reg_effort_combo, _help.info_button(\"effort\", self))\r\n )\r\n method_output_layout.addLayout(reg_method_flow)\r\n\r\n # Axis alignment + Laplacian refinement toggles, same wrapping behaviour.\r\n align_lap_flow = FlowLayout(hspacing=20, vspacing=8)\r\n align_lap_flow.addWidget(_flow_group(self.axis_align_checkbox, self.axis_align_label))\r\n align_lap_flow.addWidget(\r\n _flow_group(self.laplacian_checkbox, self.laplacian_label, _help.info_button(\"refinement\", self))\r\n )\r\n align_lap_flow.addWidget(_flow_group(self.laplacian_corr_label, self.laplacian_corr_combo))\r\n align_lap_flow.addWidget(_flow_group(self.laplacian_damage_alpha_label, self.laplacian_damage_alpha_spin))\r\n method_output_layout.addLayout(align_lap_flow)\r\n\r\n # Output directory\r\n method_output_layout.addWidget(self.output_folder_button)\r\n\r\n self.layout.addWidget(self.method_output_card)\r\n\r\n # Size every combo to its CURRENT selection so the text shows in full when\r\n # the column has room. Each can still shrink to its (reduced) minimumWidth\r\n # and elide when the FlowLayout squeezes it into a narrow column, so the\r\n # form still fits. AdjustToMinimumContentsLengthWithIcon pinned them to 6\r\n # chars ALWAYS - eliding the selection even on a wide window with plenty of\r\n # room, which is what users saw as \"dropdown text cut off\".\r\n for _cb in (\r\n self.atlas_combo,\r\n self.variant_combo,\r\n self.template_half_combo,\r\n self.reg_method_combo,\r\n self.metric_combo,\r\n self.reg_effort_combo,\r\n self.laplacian_corr_combo,\r\n ):\r\n _cb.setSizeAdjustPolicy(QComboBox.SizeAdjustPolicy.AdjustToContents)\r\n\r\n # Detected format / data type info (below the cards)\r\n self.layout.addWidget(self.detected_format_label)\r\n\r\n self.layout.addStretch(1)\r\n\r\n # Wrap the form (cards) in a QScrollArea so it stays scrollable when the\r\n # window is short. The action bar lives OUTSIDE this scroll area so it\r\n # is always reachable (the whole point of the sticky bar).\r\n self._inner.setLayout(self.layout)\r\n _scroll = QScrollArea()\r\n _scroll.setObjectName(\"registrationFormScroll\")\r\n _scroll.setWidgetResizable(True)\r\n _scroll.setFrameShape(QScrollArea.NoFrame)\r\n _scroll.setWidget(self._inner)\r\n _scroll.viewport().setAutoFillBackground(False)\r\n self._inner.setAutoFillBackground(False)\r\n # The controls form reflows: FlowLayout rows wrap, combos elide, and the\r\n # atlas-name label ignores its wide hint, so the whole column fits inside\r\n # ~500 px (measured minimumSizeHint ≈ 492). Floor the scroll a touch above\r\n # that so nothing is ever clipped, and cap it generously so it can widen to\r\n # show combos comfortably without hogging the window - the preview panel\r\n # (stretch factor 2) takes any extra width first.\r\n _scroll.setMinimumWidth(515) # ~492 fit + vertical-scrollbar allowance\r\n _scroll.setMaximumWidth(620) # capped so the preview column stays preferred\r\n self._form_scroll = _scroll\r\n\r\n # ---- Sticky action bar (pinned, NON-scrolling) --------------------\r\n # Primary Register on the left; inline progress + status next to it\r\n # (shown during a run); a stretch; then the secondary View / Napari\r\n # buttons and Cancel on the right. Same button objects as before -\r\n # only their parent/position changes, so terminal-job wiring and\r\n # _toggle_buttons keep referencing them by name.\r\n self.action_bar = QWidget()\r\n self.action_bar.setObjectName(\"registrationActionBar\")\r\n # Subtle top border (so the bar reads as pinned) is themed live by the\r\n # global stylesheet rule for QWidget#registrationActionBar, so it tracks\r\n # light/dark instead of baking in the import-time light border color.\r\n action_layout = QHBoxLayout()\r\n action_layout.setContentsMargins(12, 8, 12, 8)\r\n action_layout.setSpacing(10)\r\n action_layout.addWidget(self.register_button)\r\n action_layout.addWidget(self.progress_bar, stretch=1)\r\n action_layout.addWidget(self.status_label)\r\n action_layout.addStretch(1)\r\n action_layout.addWidget(self.view_results_button)\r\n action_layout.addWidget(self.open_napari_button)\r\n action_layout.addWidget(self.open_output_button)\r\n action_layout.addWidget(self.open_report_button)\r\n action_layout.addWidget(self.cancel_button)\r\n self.action_bar.setLayout(action_layout)\r\n\r\n # Embedded side-by-side input + atlas preview (right side, collapsible).\r\n # The panel now owns the inline orientation controls that replaced the\r\n # three preview popups; it emits the transform back so the tab keeps its\r\n # six registration attributes (the unchanged consumption path).\r\n self.preview_panel = InputAtlasPreviewPanel()\r\n self.preview_panel.orient2d_changed.connect(self._on_panel_orient2d_changed)\r\n # A finished two-click midline. The panel produces the line in its own\r\n # displayed (strided, PIR) frame; the tab promotes it onto the\r\n # full-resolution grid and persists it for the run.\r\n self.preview_panel.midline_drawn.connect(self._on_midline_drawn)\r\n self.preview_panel.axes_changed.connect(self._on_panel_axes_changed)\r\n # Hemisphere selection crops the atlas pane to match the run.\r\n self.template_half_combo.currentIndexChanged.connect(self._on_half_mode_changed)\r\n # Second preview tab: visualises the 2D foreground_init (foreground mask + the\r\n # estimated scale/centroid register_2d_inmemory applies). Refreshes on orientation\r\n # change and when its tab is shown.\r\n self.fg_init_preview = ForegroundInitPreview()\r\n self.preview_panel.orient2d_changed.connect(self._refresh_fg_init_preview)\r\n self.preview_panel.axes_changed.connect(self._refresh_fg_init_preview)\r\n # Keep the preview's scale estimate in sync with the moving-pitch controls so its\r\n # precedence matches the run (a known µm/px keeps its scale; \"Don't know\" estimates it).\r\n self.moving_um_unknown.toggled.connect(self._refresh_fg_init_preview)\r\n self.moving_um_spin.valueChanged.connect(self._refresh_fg_init_preview)\r\n # Warm the fg-init preview's heavy imports off the UI thread now, so the\r\n # first click on that tab doesn't freeze the GUI on a cold import.\r\n import threading as _threading\r\n\r\n _threading.Thread(target=_warm_foreground_init_imports, daemon=True).start()\r\n from regtools.registration.gui.detachable_tabs import DetachableTabWidget\r\n\r\n self._preview_tabs = DetachableTabWidget()\r\n self._preview_tabs.setObjectName(\"registrationPreviewTabs\")\r\n self._preview_tabs.addTab(self.preview_panel, \"Preview\")\r\n self._preview_tabs.addTab(self.fg_init_preview, \"Foreground Init\")\r\n from regtools.registration.gui.log_view import AnsiLogView\r\n\r\n self._log_view = AnsiLogView()\r\n self._preview_tabs.addTab(self._log_view, \"Log\")\r\n self._preview_tabs.currentChanged.connect(self._on_preview_tab_changed)\r\n _content_split = QSplitter(Qt.Orientation.Horizontal)\r\n _content_split.setObjectName(\"registrationContentSplitter\")\r\n _content_split.addWidget(_scroll)\r\n _content_split.addWidget(self._preview_tabs)\r\n _content_split.setStretchFactor(0, 0)\r\n _content_split.setStretchFactor(1, 2) # preview gets most extra width\r\n _content_split.setCollapsible(0, False)\r\n _content_split.setCollapsible(1, True)\r\n # Open the form a little above its fit floor so every control is visible at\r\n # startup (no wrap/scroll needed); the preview panel keeps the rest.\r\n _content_split.setSizes([600, 1000])\r\n self._content_splitter = _content_split\r\n\r\n _outer = QVBoxLayout()\r\n _outer.setContentsMargins(0, 0, 0, 0)\r\n _outer.setSpacing(0)\r\n _outer.addWidget(_content_split, stretch=1)\r\n _outer.addWidget(self.action_bar)\r\n self.setLayout(_outer)\r\n\r\n # Auto-fill template: if config specifies a custom path, switch to Custom;\r\n # otherwise trigger the default atlas selection (Allen CCF).\r\n # Use _suppress_template_dialog so the programmatic combo change does NOT\r\n # open the file picker dialog (we load the template directly afterward).\r\n if self._config_template_path and os.path.isfile(self._config_template_path):\r\n self._suppress_template_dialog = True\r\n try:\r\n self.atlas_combo.setCurrentIndex(self.atlas_combo.findData(\"custom\"))\r\n finally:\r\n self._suppress_template_dialog = False\r\n self._process_template_path(self._config_template_path)\r\n else:\r\n # Trigger atlas changed to auto-load the default (Allen CCF) template\r\n self._on_atlas_changed(self.atlas_combo.currentIndex())\r\n\r\n # Apply initial visibility (no format detected yet)\r\n self._update_format_controls()\r\n\r\n # Apply icons to buttons + card headers, and register for live re-theme.\r\n self._apply_icons()\r\n register_icon_reapply(self._apply_icons)\r\n\r\n # Restore the user's last-used choices (atlas/template, method, metric,\r\n # effort, hemisphere, slice index, moving-µm). Done last so it overlays\r\n # the freshly-built widgets and the config-driven auto-load above.\r\n self._restore_session_state()\r\n\r\n def _style_detected_format_label(self):\r\n \"\"\"Apply palette-driven style to the detected-format label.\"\"\"\r\n c = gc.get_active_colors()\r\n self.detected_format_label.setStyleSheet(f\"color: {c['text_muted']}; font-size: 14px;\")\r\n\r\n def _on_theme_changed(self):\r\n \"\"\"Re-apply palette-driven styles when the active theme changes.\"\"\"\r\n self._style_detected_format_label()\r\n\r\n def _apply_icons(self):\r\n \"\"\"Set (or re-set) themed icons on buttons and card headers.\r\n\r\n Action-bar buttons use FIXED colors (white on the teal primary, the\r\n theme danger color for Cancel, the teal accent for secondary buttons)\r\n so they always read correctly without needing live recolor. File-picker\r\n buttons and card-header glyphs use the active theme's text color and are\r\n re-rendered here when the theme switches (via register_icon_reapply).\r\n \"\"\"\r\n from regtools.gui.gui_constants import get_active_colors\r\n\r\n c = get_active_colors()\r\n icon_size = QSize(18, 18)\r\n\r\n # --- Sticky action bar -------------------------------------------\r\n # Register: play glyph in white (always on a teal primary background).\r\n self.register_button.setIcon(themed_icon(\"play\", color=\"white\"))\r\n self.register_button.setIconSize(icon_size)\r\n # Secondary buttons: teal accent reads on both light and dark surfaces.\r\n accent = c.get(\"primary\", \"#0d9488\")\r\n self.view_results_button.setIcon(themed_icon(\"eye\", color=accent))\r\n self.view_results_button.setIconSize(icon_size)\r\n self.open_napari_button.setIcon(themed_icon(\"layers\", color=accent))\r\n self.open_napari_button.setIconSize(icon_size)\r\n self.open_output_button.setIcon(themed_icon(\"folder\", color=accent))\r\n self.open_output_button.setIconSize(icon_size)\r\n self.open_report_button.setIcon(themed_icon(\"document\", color=accent))\r\n self.open_report_button.setIconSize(icon_size)\r\n # Cancel: x glyph in the theme danger color.\r\n self.cancel_button.setIcon(themed_icon(\"x\", color=c.get(\"danger\", \"#dc2626\")))\r\n self.cancel_button.setIconSize(icon_size)\r\n\r\n # --- File-picker buttons (leading folder/upload glyph) -----------\r\n text_color = c.get(\"text_primary\", \"#1f2937\")\r\n for btn, name in (\r\n (self.input_folder_button, \"upload\"),\r\n (self.output_folder_button, \"folder\"),\r\n (self.moving_mask_button, \"folder\"),\r\n ):\r\n btn.setIcon(themed_icon(name, color=text_color))\r\n btn.setIconSize(icon_size)\r\n\r\n # --- Card-header glyphs (recolor the QLabel pixmaps) -------------\r\n for name, label in getattr(self, \"_card_icon_labels\", {}).items():\r\n try:\r\n label.setPixmap(themed_icon(name, color=text_color, size=18).pixmap(18, 18))\r\n except RuntimeError:\r\n pass\r\n\r\n # ------------------------------------------------------------------ #\r\n # Session memory: remember the user's last-used choices across #\r\n # launches. Restored on __init__, saved on a run start. Purely a #\r\n # convenience cache - every step is best-effort and never raises, so #\r\n # a missing or corrupt cache can't block the GUI. #\r\n # ------------------------------------------------------------------ #\r\n def _collect_session_state(self) -> dict:\r\n \"\"\"Session state is intentionally NOT persisted across launches; returns\r\n an empty snapshot so callers (and tests) see \"nothing remembered\".\"\"\"\r\n return {}\r\n\r\n def _save_session_state(self):\r\n \"\"\"Session state is intentionally NOT persisted across launches; no-op.\"\"\"\r\n return\r\n\r\n def _restore_session_state(self):\r\n \"\"\"Session restore is intentionally disabled - the tab never persists or\r\n pre-fills choices across launches (``_collect``/``_save_session_state``\r\n are no-ops). Retained purely as a construction hook that seeds the\r\n panel's inline orientation controls from the tab's CURRENT orientation\r\n attributes; it does not read any persisted session state.\r\n\r\n (The ``load_session_state`` import is kept because tests monkeypatch it\r\n on this module to prove restore ignores legacy persisted state.)\r\n \"\"\"\r\n # Reflect the current orientation state in the inline panel controls.\r\n # Seed WITHOUT re-emitting so the six registration attributes are not\r\n # clobbered by a spurious signal. This ran unconditionally before the\r\n # session-restore feature was removed; keep it so the panel is seeded.\r\n self._seed_orientation_controls()\r\n\r\n def _seed_orientation_controls(self):\r\n \"\"\"Push the current six orientation attributes into the panel's inline\r\n controls without re-emitting (used on load / session restore).\"\"\"\r\n panel = getattr(self, \"preview_panel\", None)\r\n if panel is None:\r\n return\r\n try:\r\n panel.set_orient2d_controls(self._2d_rotate_angle, self._2d_flip_h, self._2d_flip_v, self._2d_tissuecyte)\r\n panel.set_axes_controls(self._axes_order, tuple(self._axes_flips) if self._axes_flips else None)\r\n panel.set_orient_points(self._orient_points)\r\n except Exception:\r\n pass\r\n\r\n def _on_panel_orient2d_changed(self, rotate, flip_h, flip_v, tissuecyte):\r\n \"\"\"Inline 2D orient controls changed -> update the six attributes + label.\r\n\r\n Mirrors what the removed Preview2DDialog wrote on Accept; the\r\n registration consumption path (these attrs -> RegistrationOpts) is\r\n unchanged.\r\n \"\"\"\r\n self._2d_rotate_angle = float(rotate)\r\n self._2d_flip_h = bool(flip_h)\r\n self._2d_flip_v = bool(flip_v)\r\n self._2d_tissuecyte = bool(tissuecyte)\r\n parts = []\r\n if self._2d_tissuecyte:\r\n parts.append(\"TissueCyte\")\r\n if self._2d_rotate_angle != 0.0:\r\n parts.append(f\"Rotate {self._2d_rotate_angle:g}°\")\r\n if self._2d_flip_h:\r\n parts.append(\"Flip H\")\r\n if self._2d_flip_v:\r\n parts.append(\"Flip V\")\r\n self.transform_info_label.setText(\" | \".join(parts) if parts else \"No orientation change\")\r\n\r\n def _on_panel_axes_changed(self, order, flips):\r\n \"\"\"Inline 3D axis controls changed -> update the axis attributes + label.\r\n\r\n ``order`` is already ``None`` on identity and ``flips`` carries\r\n reoriented-axis indices matching cli.py, so both are written straight\r\n through (unchanged consumption path).\r\n \"\"\"\r\n self._axes_order = order\r\n self._axes_flips = list(flips) if flips else None\r\n parts = []\r\n if self._axes_order:\r\n parts.append(f\"Axes: {order[0]}->Cor, {order[1]}->Axi, {order[2]}->Sag\")\r\n if self._axes_flips:\r\n parts.append(f\"Flip: {','.join(str(a) for a in self._axes_flips)}\")\r\n self.axes_info_label.setText(\" | \".join(parts) if parts else \"\")\r\n\r\n def _on_preview_tab_changed(self, idx):\r\n \"\"\"Lazily refresh the foreground-init preview when its tab becomes visible.\"\"\"\r\n tabs = getattr(self, \"_preview_tabs\", None)\r\n if tabs is not None and tabs.widget(idx) is getattr(self, \"fg_init_preview\", None):\r\n self._refresh_fg_init_preview()\r\n\r\n def _refresh_fg_init_preview(self, *args):\r\n \"\"\"Recompute the foreground-init preview from the panel's ORIENTED slices.\r\n\r\n Shows the Triangle foreground mask + the estimated scale/centroid - the same init\r\n ``register_2d_inmemory`` applies. Only recomputes while the FG tab is visible;\r\n best-effort (idle hint when nothing is loaded).\r\n \"\"\"\r\n fg = getattr(self, \"fg_init_preview\", None)\r\n tabs = getattr(self, \"_preview_tabs\", None)\r\n if fg is None or tabs is None or tabs.currentWidget() is not fg:\r\n return\r\n panel = getattr(self, \"preview_panel\", None)\r\n moving = None\r\n if panel is not None and hasattr(panel, \"_oriented_input_slice_2d\"):\r\n try:\r\n moving = panel._oriented_input_slice_2d()\r\n except Exception:\r\n moving = None\r\n if moving is None:\r\n fg.clear()\r\n return\r\n fixed = None\r\n if hasattr(panel, \"_current_atlas_slice_2d\"):\r\n try:\r\n fixed = panel._current_atlas_slice_2d()\r\n except Exception:\r\n fixed = None\r\n # Mirror the run's precedence: a known moving µm/px keeps its scale (centroid only);\r\n # \"Don't know\" (unknown pitch) estimates the scale from the foreground area.\r\n mv_um = None\r\n try:\r\n if not self.moving_um_unknown.isChecked():\r\n mv_um = float(self.moving_um_spin.value())\r\n except Exception:\r\n mv_um = None\r\n try:\r\n fg.update_preview(moving, fixed, moving_um_per_px=mv_um)\r\n except Exception:\r\n fg.clear()\r\n\r\n def _on_half_mode_changed(self, *_):\r\n \"\"\"Hemisphere combo changed -> crop the atlas pane to the same half.\"\"\"\r\n panel = getattr(self, \"preview_panel\", None)\r\n if panel is not None:\r\n panel.set_half_mode(self.template_half_combo.currentData())\r\n\r\n # ---- hemisphere split / midsagittal plane --------------------------------\r\n def _on_hemisphere_toggled(self, checked):\r\n \"\"\"Resolve the midsagittal plane for the current fixed image.\r\n\r\n A built-in atlas READS its plane from the shipped table\r\n (``regtools/hemisphere/planes.json``) - nothing is fitted for it, here\r\n or anywhere else on a user's machine.\r\n\r\n A custom template is DRAWN. Nothing is fitted for it either: there is\r\n no shipped entry, often no second hemisphere for a symmetry objective\r\n to score, and ``fit_plane``'s accept gate cannot even be evaluated\r\n without an annotation to reflect. Ticking this arms two-click picking\r\n on the atlas pane, unless a previously drawn midline is already cached\r\n beside the image, in which case that is reloaded and there is nothing\r\n to draw.\r\n\r\n Unticking hides the overlay but, for a BUILT-IN atlas, keeps the plane\r\n driving the pane's half-crop: the run always crops along the fitted\r\n plane, so dropping it here would put the preview back on the array\r\n midpoint and re-open the preview/run disagreement. A custom image's\r\n plane is genuinely opt-in, so unticking discards it.\r\n \"\"\"\r\n panel = getattr(self, \"preview_panel\", None)\r\n status = getattr(self, \"hemisphere_status_label\", None)\r\n if not checked:\r\n if status is not None:\r\n status.setText(\"\")\r\n if self.selected_atlas == \"custom\":\r\n self._custom_hemisphere_plane = None\r\n if panel is not None:\r\n panel.cancel_midline_drawing()\r\n panel.set_hemisphere_plane(None)\r\n elif panel is not None:\r\n panel.set_show_midline(False)\r\n self._update_half_combo_enabled()\r\n return\r\n if self.selected_atlas == \"custom\":\r\n self._begin_or_reload_custom_midline()\r\n return\r\n plane, message = self._resolve_hemisphere_plane()\r\n if status is not None:\r\n status.setText(message)\r\n if panel is not None:\r\n panel.set_hemisphere_plane(plane)\r\n panel.set_show_midline(True)\r\n self._update_half_combo_enabled()\r\n\r\n def _refresh_hemisphere_plane(self):\r\n \"\"\"Re-push the plane after the template changed. NEVER fits.\r\n\r\n Selecting a custom template must not stall the GUI behind an\r\n optimisation nobody asked for, so a custom image only ever gets the\r\n plane a previous explicit tick already produced.\r\n\r\n Failures are swallowed: this runs inside the template push, and a\r\n hemisphere problem must never blank the atlas preview.\r\n \"\"\"\r\n try:\r\n self._do_refresh_hemisphere_plane()\r\n except Exception:\r\n pass\r\n\r\n def _do_refresh_hemisphere_plane(self):\r\n panel = getattr(self, \"preview_panel\", None)\r\n status = getattr(self, \"hemisphere_status_label\", None)\r\n check = getattr(self, \"hemisphere_check\", None)\r\n if check is None or not check.isChecked():\r\n plane, message = None, \"\"\r\n elif self.selected_atlas and self.selected_atlas != \"custom\":\r\n plane, message = self._resolve_hemisphere_plane()\r\n else:\r\n plane = getattr(self, \"_custom_hemisphere_plane\", None)\r\n message = \"\" if plane is None else self.CUSTOM_HEMISPHERE_UNSUPPORTED\r\n if status is not None:\r\n status.setText(message)\r\n if panel is not None:\r\n panel.set_hemisphere_plane(plane)\r\n panel.set_show_midline(check is not None and check.isChecked())\r\n self._update_half_combo_enabled()\r\n\r\n def _resolve_hemisphere_plane(self):\r\n \"\"\"``(plane, status_text)`` for the current template selection.\r\n\r\n Cheap on both branches, and nothing here fits. A built-in atlas's\r\n plane is READ from the shipped table; a custom template's is whatever\r\n the user last drew (:meth:`_on_midline_drawn`), held on the tab.\r\n\r\n This docstring previously warned that the custom branch fitted inline\r\n and blocked for minutes. That is no longer true - the fit was removed\r\n when drawing replaced it - and the warning is recorded as removed\r\n rather than silently dropped, because it named a method\r\n (``_start_custom_hemisphere_fit``) that no longer exists and would\r\n send a reader looking for machinery that is gone.\r\n \"\"\"\r\n from regtools.hemisphere.precompute import plane_for_atlas\r\n\r\n if self.selected_atlas and self.selected_atlas != \"custom\":\r\n try:\r\n from regtools.atlas.registry import get_atlas\r\n\r\n plane = plane_for_atlas(get_atlas(self.selected_atlas))\r\n except Exception:\r\n plane = None\r\n if plane is None:\r\n # An end user of a shipped build cannot fit a plane, so naming\r\n # a maintainer script here would be unactionable noise. Name\r\n # the atlas and state what this build does not carry.\r\n return None, (\r\n f\"Midline: unavailable - no hemisphere \"\r\n f\"parameters are shipped for this build of the \"\r\n f\"{self.selected_atlas} annotation\"\r\n )\r\n return plane, self._midline_status_text(plane)\r\n plane = getattr(self, \"_custom_hemisphere_plane\", None)\r\n if plane is None:\r\n return None, \"\" # nothing drawn for this image yet\r\n # The run reads the SIDECAR, so the status must reflect what the run\r\n # will actually find - not what this tab happens to hold. A fit that\r\n # could not be persisted still drives the overlay, and the status\r\n # says so rather than promising a cut the run cannot make.\r\n if self.custom_plane_the_run_can_use() is None:\r\n return plane, self.CUSTOM_HEMISPHERE_UNSUPPORTED\r\n # A custom plane is UNGATED - fit_plane gets annotation=None, so the\r\n # accept gate that refuses a plane worse than the width // 2 midline\r\n # cannot be computed. That was harmless while this only drew an\r\n # overlay; it now drives the crop, so ask for the one review step that\r\n # IS possible. A built-in atlas's plane was gated by the maintainer\r\n # and gets no such nudge.\r\n return plane, self._midline_status_text(plane) + \" - check the overlay before running\"\r\n\r\n @staticmethod\r\n def _midline_status_text(plane):\r\n # A hand-drawn plane was not fitted by anything, so the word must not\r\n # appear: it would credit an optimiser with a line the user placed,\r\n # and send anyone debugging a bad midline looking for a fit that never\r\n # ran.\r\n if getattr(plane, \"fit_method\", \"\") == \"two_points\":\r\n return f\"Midline: {plane.offset:.1f} (drawn by hand)\"\r\n text = f\"Midline: {plane.offset:.1f} ({plane.fit_method} fit\"\r\n labels = plane.metrics.get(\"qualifying_labels\")\r\n if labels:\r\n text += f\", {int(labels)} bilateral labels\"\r\n agreement = plane.metrics.get(\"exact_label_agreement\")\r\n if agreement is not None:\r\n text += f\", agreement {float(agreement):.2f}\"\r\n return text + \")\"\r\n\r\n #: Shown when a custom fit produced a plane the RUN cannot read back -\r\n #: today that means the sidecar could not be written (a read-only\r\n #: location). The overlay still draws so the fit can be eyeballed, but the\r\n #: crop would fall back to the array midpoint, so the status must not\r\n #: imply otherwise and the Template Region dropdown stays locked.\r\n CUSTOM_HEMISPHERE_UNSUPPORTED = (\r\n \"Midline: preview only - the drawn line could not be saved beside the \"\r\n \"image, so the run cannot read it back and Template Region stays \"\r\n \"locked. Check the folder is writable.\"\r\n )\r\n\r\n def _begin_or_reload_custom_midline(self):\r\n \"\"\"Reload a drawn midline for this image, or arm drawing a new one.\r\n\r\n A cache HIT short-circuits: a midline the user already drew for this\r\n exact file (size + mtime fingerprinted) is theirs, and asking them to\r\n draw it again on every session would be worse than useless. Otherwise\r\n the pane is armed for two clicks.\r\n \"\"\"\r\n from regtools.hemisphere.cache import fingerprint, load_plane, sidecar_path\r\n\r\n status = getattr(self, \"hemisphere_status_label\", None)\r\n path = getattr(self, \"template_path\", None)\r\n if not path or not os.path.isfile(path):\r\n self.hemisphere_check.setChecked(False)\r\n if status is not None:\r\n status.setText(\"Midline: load a template first\")\r\n return\r\n try:\r\n cached = load_plane(sidecar_path(path), fingerprint(path))\r\n except Exception:\r\n cached = None\r\n if cached is not None:\r\n self._apply_custom_hemisphere_plane(cached, self._midline_status_text(cached) + \" (drawn earlier)\")\r\n return\r\n self.begin_midline_drawing()\r\n\r\n def _update_hemisphere_controls(self):\r\n \"\"\"Show the draw toggle only for a custom template; refresh the overlay.\r\n\r\n A built-in atlas's plane is READ from the shipped table - free, and\r\n already reviewed by the maintainer before it was committed - so there\r\n is nothing for the user to opt into and the checkbox is only noise.\r\n Hiding it must NOT hide the midline, though: the overlay is the only\r\n visual confirmation of where the run will cut, so for a built-in atlas\r\n it is simply always on.\r\n \"\"\"\r\n check = getattr(self, \"hemisphere_check\", None)\r\n panel = getattr(self, \"preview_panel\", None)\r\n status = getattr(self, \"hemisphere_status_label\", None)\r\n is_custom = getattr(self, \"selected_atlas\", None) == \"custom\"\r\n if check is not None:\r\n check.setVisible(bool(is_custom))\r\n if not is_custom:\r\n plane, message = self._resolve_hemisphere_plane()\r\n if status is not None:\r\n status.setText(message)\r\n if panel is not None:\r\n panel.cancel_midline_drawing()\r\n panel.set_hemisphere_plane(plane)\r\n panel.set_show_midline(True) # always on for a built-in\r\n self._update_half_combo_enabled()\r\n\r\n # ---- drawing the midline by hand ----------------------------------------\r\n def begin_midline_drawing(self):\r\n \"\"\"Arm two-click midline picking on the atlas pane. ``True`` if armed.\r\n\r\n For a custom template there is nothing to fit against - no shipped\r\n entry, often no second hemisphere for a symmetry objective to score,\r\n and ``fit_plane``'s accept gate cannot even be evaluated without an\r\n annotation to reflect. A person looking at their own section knows\r\n where the midline is, so drawing it is not a degraded fallback here; it\r\n is the only ground truth available, and unlike a fit it needs no\r\n automatic gate because the human IS the gate.\r\n\r\n ``ml_axis`` and ``ml_sign`` are DERIVED from the affine of the array\r\n the pane is actually showing, never assumed. \"Medio-lateral is the last\r\n axis\" is true for the PIR preview and false in general, and assuming it\r\n is exactly how an anterior/posterior split ends up labelled left and\r\n right with nothing in the shapes to reveal it.\r\n\r\n Refuses - rather than drawing a line it cannot honour - when the\r\n medio-lateral axis is not the pane's COLUMN axis (the pane draws\r\n ``data.T``, so only then does a picked line run down the midline), when\r\n there is no affine to derive from, or when the pre-stride shape needed\r\n to place the click on the run's grid is unknown.\r\n \"\"\"\r\n panel = getattr(self, \"preview_panel\", None)\r\n status = getattr(self, \"hemisphere_status_label\", None)\r\n\r\n def _refuse(reason):\r\n if status is not None:\r\n status.setText(f\"Midline: {reason}\")\r\n return False\r\n\r\n if panel is None:\r\n return False\r\n affine = getattr(self, \"_template_preview_affine\", None)\r\n assumed_frame = affine is None\r\n if assumed_frame:\r\n # TIFF / NumPy carry no affine at all. Rather than refuse to let\r\n # those users draw, fall back to the same last-axis-is-medio-\r\n # lateral convention the removed fit used - and say so, because a\r\n # wrong guess puts L and R on the wrong sides and only the drawn\r\n # markers reveal it.\r\n affine = _last_axis_ml_affine()\r\n if getattr(self, \"_template_preview_full_shape\", None) is None:\r\n return _refuse(\r\n \"the preview's full-resolution grid is unknown, so \"\r\n \"a click on it cannot be placed on the volume the \"\r\n \"run uses - draw disabled\"\r\n )\r\n from regtools.hemisphere.plane import ml_axis_and_sign\r\n\r\n try:\r\n ml_axis, ml_sign = ml_axis_and_sign(np.asarray(affine, dtype=float))\r\n except Exception:\r\n return _refuse(\"this preview's affine could not be read\")\r\n if int(ml_axis) != 2:\r\n # The pane draws array axis -1 horizontally, so only a volume whose\r\n # medio-lateral axis IS that one can carry a midline drawn down the\r\n # columns. Same condition _position_midline refuses to draw under.\r\n return _refuse(\r\n \"this template's medio-lateral axis is not the one the preview \"\r\n \"draws across, so a line picked here would sit at ninety \"\r\n \"degrees to the real midline - draw disabled\"\r\n )\r\n # 1 = the COLUMN axis WITHIN the displayed 2-D slice.\r\n panel.begin_midline_drawing(ml_axis=1, ml_sign=float(ml_sign))\r\n if status is not None:\r\n note = (\r\n \" This image carries no orientation, so left/right is \"\r\n \"assumed from the last axis - check the L/R markers.\"\r\n if assumed_frame\r\n else \"\"\r\n )\r\n status.setText(\r\n \"Midline: left-click one end, right-click the \"\r\n \"other. Place them towards opposite ends of the \"\r\n \"section.\" + note\r\n )\r\n return True\r\n\r\n def _on_midline_drawn(self, plane2d):\r\n \"\"\"A finished two-click line from the panel. GUI thread.\r\n\r\n Promotes it onto the full-resolution grid and persists it, because the\r\n run resolves a custom image's plane from the SIDECAR - a drawing that\r\n is never written is one the registration cannot act on.\r\n \"\"\"\r\n from regtools.hemisphere.cache import fingerprint, save_plane, sidecar_path\r\n from regtools.hemisphere.geometry import promote_drawn_plane\r\n\r\n status = getattr(self, \"hemisphere_status_label\", None)\r\n path = getattr(self, \"template_path\", None)\r\n display = getattr(self, \"_template_data_cache\", None)\r\n full_shape = getattr(self, \"_template_preview_full_shape\", None)\r\n affine = getattr(self, \"_template_preview_affine\", None)\r\n if display is None or full_shape is None:\r\n return\r\n try:\r\n import nibabel as nib\r\n\r\n # A 2-D template has no affine and needs none: the line stays 2-D\r\n # and a 2-D plane records no frame.\r\n axcodes = () if affine is None else tuple(str(c) for c in nib.aff2axcodes(np.asarray(affine, dtype=float)))\r\n plane = promote_drawn_plane(\r\n plane2d,\r\n display_shape=tuple(int(n) for n in np.asarray(display).shape),\r\n full_shape=tuple(int(n) for n in full_shape),\r\n axcodes=axcodes,\r\n )\r\n except Exception as error: # noqa: BLE001 - report, skip\r\n if status is not None:\r\n status.setText(f\"Midline: could not use that line - {error}\")\r\n return\r\n\r\n if path:\r\n try:\r\n save_plane(sidecar_path(path), plane, fingerprint(path))\r\n except OSError:\r\n pass # read-only location: keep it for this session\r\n self._custom_hemisphere_plane = plane\r\n self._apply_custom_hemisphere_plane(plane, f\"Midline: {plane.offset:.1f} (drawn by hand)\")\r\n\r\n def custom_plane_the_run_can_use(self):\r\n \"\"\"The custom template's plane AS THE RUN WILL SEE IT, or ``None``.\r\n\r\n The pipeline resolves a custom image's plane from the SIDECAR beside\r\n the file (``_prepare_3d_inputs`` -> ``plane_for_image``), never from\r\n anything this tab holds in memory. So the question that governs the\r\n dropdown is not \"did we fit one?\" but \"can the run read one back?\" -\r\n a fit that could not be persisted (a read-only location; ``_fit_custom_\r\n hemisphere_plane`` swallows that ``OSError`` on purpose so the session\r\n keeps its overlay) would otherwise unlock a cut the run cannot make.\r\n\r\n Reading it back through the same function the run calls means the\r\n preview and the run agree by construction rather than by inspection.\r\n\r\n A 2-D plane is refused: it has no third axis, and the half-brain crop\r\n this dropdown drives is the 3-D one.\r\n \"\"\"\r\n if getattr(self, \"_custom_hemisphere_plane\", None) is None:\r\n return None\r\n try:\r\n from regtools.hemisphere.precompute import plane_for_image\r\n\r\n plane = plane_for_image(getattr(self, \"template_path\", None))\r\n except Exception:\r\n return None\r\n if plane is None or getattr(plane, \"is_2d\", False):\r\n return None\r\n return plane\r\n\r\n def _hemisphere_selection_allowed(self):\r\n \"\"\"Whether the Template Region dropdown may offer a half at all.\r\n\r\n Built-in: a registry atlas that is not ``whole_brain_only``, unchanged.\r\n\r\n Custom: allowed once the image has a plane the RUN can read back - see\r\n :meth:`custom_plane_the_run_can_use`. Previously this was False\r\n unconditionally, because a custom template's plane never reached the\r\n pipeline and the run cropped at the array midpoint; unlocking would\r\n have shown a cut that did not happen. The pipeline now resolves that\r\n sidecar itself, so the preview and the run cut at the same midline and\r\n the gate can finally open. It stays shut when there is no plane, which\r\n is still the default: the fit is opt-in.\r\n \"\"\"\r\n key = getattr(self, \"selected_atlas\", None)\r\n if key == \"custom\":\r\n return self.custom_plane_the_run_can_use() is not None\r\n try:\r\n from regtools.registration.core.config import ATLAS_REGISTRY, get_atlas\r\n\r\n return bool(key in ATLAS_REGISTRY and not get_atlas(key).whole_brain_only)\r\n except Exception:\r\n return False\r\n\r\n def _update_half_combo_enabled(self):\r\n \"\"\"Apply :meth:`_hemisphere_selection_allowed` to the dropdown.\"\"\"\r\n combo = getattr(self, \"template_half_combo\", None)\r\n if combo is None:\r\n return\r\n allowed = self._hemisphere_selection_allowed()\r\n if not allowed:\r\n combo.setCurrentIndex(0) # \"Whole Brain\"\r\n combo.setEnabled(bool(allowed))\r\n\r\n def _apply_custom_hemisphere_plane(self, plane, message):\r\n \"\"\"GUI thread. Push a custom plane into the tab, panel and dropdown.\"\"\"\r\n self._custom_hemisphere_plane = plane\r\n # A custom plane is UNGATED (fit_plane gets annotation=None, so the\r\n # accept gate cannot be computed), and it now drives the crop - so ask\r\n # for the one review step that is possible. Unless the run cannot read\r\n # it back at all, in which case say that instead.\r\n if self.custom_plane_the_run_can_use() is None:\r\n message = self.CUSTOM_HEMISPHERE_UNSUPPORTED\r\n else:\r\n message = message + \" - check the overlay before running\"\r\n status = getattr(self, \"hemisphere_status_label\", None)\r\n if status is not None:\r\n status.setText(message)\r\n panel = getattr(self, \"preview_panel\", None)\r\n if panel is not None:\r\n panel.set_hemisphere_plane(plane)\r\n panel.set_show_midline(True)\r\n self._update_half_combo_enabled()\r\n\r\n def _select_input_path(self):\r\n \"\"\"\r\n Select the path to load the input data from. Supports all image formats.\r\n A unified file dialog lets users pick files or directories.\r\n \"\"\"\r\n msg = QMessageBox()\r\n msg.setWindowTitle(\"Select Input Data\")\r\n msg.setText(\"What would you like to select?\")\r\n msg.setInformativeText(\r\n \"Choose 'File(s)' for NIfTI, NRRD, TIFF, or 2D images.\\nChoose 'Directory' for a folder of TIFF sections or DICOM series.\"\r\n )\r\n file_btn = msg.addButton(\"File(s)\", QMessageBox.AcceptRole)\r\n dir_btn = msg.addButton(\"Directory\", QMessageBox.AcceptRole)\r\n msg.addButton(QMessageBox.Cancel)\r\n msg.exec()\r\n clicked = msg.clickedButton()\r\n if clicked == file_btn:\r\n file_paths, _ = QFileDialog.getOpenFileNames(\r\n None,\r\n \"Select input file(s)\",\r\n filter=\"All supported (*.nii *.nii.gz *.nrrd *.nhdr *.tif *.tiff *.dcm *.ima *.png *.jpg *.jpeg *.bmp *.gif *.webp *.vsi *.npy *.npz);;NIfTI (*.nii *.nii.gz);;NRRD (*.nrrd *.nhdr);;TIFF (*.tif *.tiff);;DICOM (*.dcm *.ima);;Images (*.png *.jpg *.jpeg *.bmp *.gif *.webp);;VSI (*.vsi);;NumPy (*.npy *.npz);;All files (*)\",\r\n )\r\n if not file_paths:\r\n return\r\n if len(file_paths) == 1:\r\n file_path = file_paths[0]\r\n else:\r\n # Multiple files selected - pass as list\r\n self._process_input_path(file_paths)\r\n return\r\n elif clicked == dir_btn:\r\n file_path = QFileDialog.getExistingDirectory(None, \"Select directory with image data\")\r\n else:\r\n return\r\n if file_path == \"\":\r\n return\r\n self._process_input_path(file_path)\r\n\r\n def _update_format_controls(self):\r\n \"\"\"\r\n Update visibility of format-specific controls based on auto-detected data_type.\r\n Called after format detection in _process_input_path.\r\n \"\"\"\r\n # Show/hide CCF index controls and 2D preview controls\r\n is_2d = self.data_type == \"2d\"\r\n is_batch_2d = self.data_type == \"batch_2d\"\r\n # CCF slice controls only apply when in 2D mode AND the template is a 3D atlas\r\n show_ccf = is_2d and not getattr(self, \"_template_is_2d\", False)\r\n self.ccf_index_label.setVisible(show_ccf)\r\n self.ccf_index_spin.setVisible(show_ccf)\r\n self.deepslice_checkbox.setVisible(show_ccf)\r\n self.tilt_slice_button.setVisible(show_ccf)\r\n self.transform_info_label.setVisible(is_2d)\r\n self.moving_um_label.setVisible(is_2d)\r\n self.moving_um_spin.setVisible(is_2d)\r\n self.moving_um_unknown.setVisible(is_2d)\r\n self.moving_um_estimate_label.setVisible(is_2d)\r\n if is_2d and self.moving_um_unknown.isChecked():\r\n self._on_moving_um_unknown(True) # refresh estimate for the new mode\r\n if is_2d:\r\n # Apply the match-resolution lock now that the 2D pitch controls are shown.\r\n self._on_match_fixed_res(self.match_fixed_res_checkbox.isChecked())\r\n self.moving_mask_button.setVisible(is_2d)\r\n self.moving_mask_clear_button.setVisible(is_2d and self.moving_mask_path is not None)\r\n self.moving_mask_label.setVisible(is_2d)\r\n for w in (\r\n self.atlas_edit_arm_button,\r\n self.atlas_edit_brush_button,\r\n self.atlas_edit_polygon_button,\r\n self.atlas_edit_eraser_button,\r\n self.atlas_edit_radius_label,\r\n self.atlas_edit_radius_spin,\r\n self.atlas_edit_fill_label,\r\n self.atlas_edit_fill_spin,\r\n self.atlas_edit_clear_button,\r\n ):\r\n w.setVisible(is_2d)\r\n if not is_2d and self.atlas_edit_arm_button.isChecked():\r\n # Leaving 2D mode disarms drawing so a stray click elsewhere can't\r\n # paint a now-hidden atlas edit mask.\r\n self.atlas_edit_arm_button.setChecked(False)\r\n\r\n # Batch 2D: slice mapping CSV required, warning visible\r\n self.slice_mapping_button.setVisible(is_batch_2d)\r\n # resolution_warning_label visibility is controlled by _on_meta_done\r\n\r\n # The \"2D Section Options\" card holds only 2D/batch-2D controls; hide\r\n # the whole card (no empty box) when neither mode is active. Individual\r\n # child-widget visibility above still governs what shows inside it.\r\n if hasattr(self, \"section_2d_card\"):\r\n self.section_2d_card.setVisible(is_2d or is_batch_2d)\r\n\r\n # Axis-order info is TIFF-only (other formats already encode orientation).\r\n # The inline axis controls live on the preview panel, gated by data_type.\r\n is_tiff = self.data_type == \"tiff\"\r\n self.axes_info_label.setVisible(is_tiff)\r\n\r\n # Disable axis alignment for 2D and batch 2D (not applicable)\r\n self.axis_align_checkbox.setEnabled(not is_2d and not is_batch_2d)\r\n self.axis_align_label.setEnabled(not is_2d and not is_batch_2d)\r\n # Re-apply stylesheet to force Qt to repaint the :disabled pseudo-state\r\n tag_themed(self.axis_align_label, \"param_label\")\r\n if is_2d or is_batch_2d:\r\n self.axis_align_checkbox.setChecked(False)\r\n\r\n # The Foreground-Init preview is a 2D-section concept (the Triangle mask +\r\n # scale/centroid init register_2d_inmemory applies); grey out its tab\r\n # outside a 2D / batch-2D run so it isn't offered where it does nothing.\r\n self._update_fg_init_tab_enabled(is_2d or is_batch_2d)\r\n\r\n def _update_fg_init_tab_enabled(self, enabled=None):\r\n \"\"\"Enable the 'Foreground Init' preview tab only in a 2D-section context.\r\n\r\n foreground_init is applied by ``register_2d_inmemory`` (2D / batch-2D); it\r\n has no effect on a 3D NIfTI/TIFF run, so its preview would be misleading\r\n there. Grey the tab out otherwise, and bounce back to the main Preview tab\r\n if the now-disabled tab was the one showing.\"\"\"\r\n tabs = getattr(self, \"_preview_tabs\", None)\r\n fg = getattr(self, \"fg_init_preview\", None)\r\n if tabs is None or fg is None:\r\n return\r\n if enabled is None:\r\n enabled = self.data_type in (\"2d\", \"batch_2d\")\r\n idx = tabs.indexOf(fg)\r\n if idx < 0:\r\n return\r\n was_showing = tabs.currentIndex() == idx\r\n tabs.setTabEnabled(idx, bool(enabled))\r\n tabs.setTabToolTip(\r\n idx,\r\n \"\" if enabled else \"Foreground init applies to 2D section registration; it isn't used for a 3D volume run.\",\r\n )\r\n # Disabling the *current* tab makes Qt auto-advance to the next ENABLED\r\n # tab (e.g. the Log tab), so decide on the pre-disable state.\r\n if not enabled and was_showing:\r\n tabs.setCurrentIndex(0) # fall back to the main Preview tab\r\n\r\n def _select_input_points(self):\r\n \"\"\"\r\n Select one or more input points files. Supports multi-file selection.\r\n \"\"\"\r\n file_paths, _ = QFileDialog.getOpenFileNames(\r\n None,\r\n \"Select input points file(s)\",\r\n filter=\"Points files (*.txt *.csv *.h5ad);;Text files (*.txt);;CSV files (*.csv);;AnnData (*.h5ad);;All files (*)\",\r\n )\r\n if not file_paths:\r\n return\r\n self._process_points_paths(file_paths)\r\n\r\n def _select_input_segmentation(self):\r\n \"\"\"Pick the preview-only segmentation mask (single file).\"\"\"\r\n file_path, _ = QFileDialog.getOpenFileName(\r\n None,\r\n \"Select segmentation mask for the preview overlay\",\r\n filter=(\"Segmentation masks (*.tif *.tiff *.png *.npy *.npz *.nii *.nii.gz *.nrrd);;All files (*)\"),\r\n )\r\n if not file_path:\r\n return\r\n self._process_segmentation_path(file_path)\r\n\r\n def _handle_segmentation_drop(self, file_path):\r\n \"\"\"Handle a file drop on the segmentation button (replaces, not appends -\r\n one mask overlays one preview).\"\"\"\r\n self._process_segmentation_path(file_path)\r\n\r\n def _process_segmentation_path(self, file_path):\r\n \"\"\"Record the mask path, relabel the button, and refresh the overlay.\"\"\"\r\n self.input_segmentation_path = file_path or None\r\n btn = getattr(self, \"input_segmentation_button\", None)\r\n if btn is not None:\r\n if self.input_segmentation_path:\r\n btn.setText(\r\n f\"Select Segmentation for Preview (Optional)\\n{os.path.basename(self.input_segmentation_path)}\"\r\n )\r\n tag_themed(btn, \"file_success\")\r\n else:\r\n btn.setText(\"Select Segmentation for Preview (Optional)\\nNo segmentation loaded\")\r\n tag_themed(btn, \"file\")\r\n self._update_preview_segmentation()\r\n\r\n def _update_preview_segmentation(self):\r\n \"\"\"Load the mask's 2D section and hand it to the preview overlay.\r\n\r\n Loaded RAW (``orient=False``): the overlay is regridded onto the input\r\n pane's ALREADY-oriented base slice, so orienting it here as well would\r\n apply the transform twice. This mirrors the invariant the point-reorient\r\n viewer settled on - pair on-disk companion data with the frame it was\r\n saved in, and let ONE code path do the orienting.\r\n\r\n The whole array is handed over, 2D or 3D: the panel slices a stack with\r\n its own index, so scrubbing does not come back through here.\r\n \"\"\"\r\n panel = getattr(self, \"preview_panel\", None)\r\n if panel is None:\r\n return\r\n path = getattr(self, \"input_segmentation_path\", None)\r\n if not path:\r\n panel.set_input_segmentation(None)\r\n return\r\n try:\r\n from regtools.utils.io.volume_io import load_volume\r\n\r\n # .npz first, by NAME. Cellpose writes flows and masks into one\r\n # archive, and the generic loader takes the first key - which for\r\n # savez(flows=..., masks=...) is the flow field, not the labels. The\r\n # .npy dict path already prefers 'masks'; match it here rather than\r\n # changing the shared loader other callers depend on.\r\n if str(path).lower().endswith(\".npz\"):\r\n with np.load(path, allow_pickle=False) as z:\r\n keys = list(z.files)\r\n key = next((k for k in (\"masks\", \"mask\", \"labels\", \"seg\") if k in keys), keys[0] if keys else None)\r\n if key is None:\r\n raise ValueError(f\"{os.path.basename(path)} has no arrays\")\r\n panel.set_input_segmentation(np.asarray(z[key]))\r\n if key != \"masks\" and \"masks\" not in keys:\r\n print(f\"[preview] segmentation .npz: using array {key!r} (available: {keys})\")\r\n return\r\n vd = load_volume(path, orient=False)\r\n # Surface the loader's own diagnostic. Without this a real reason\r\n # (\"unsupported codec\", \"file truncated\") is replaced downstream by a\r\n # generic shape complaint, while the button still reads green.\r\n err = getattr(vd, \"error\", None)\r\n if err:\r\n raise ValueError(str(err))\r\n vol = np.asarray(vd.data)\r\n # An RGB/RGBA .png or .tif arrives as (H, W, C). Handing that over as\r\n # a \"3D stack\" makes the panel treat the COLOUR axis as sections, so\r\n # it draws channel 0 - which for a colour mask is not a label image\r\n # at all and covers nothing. A label image has no channels, so this\r\n # is a wrong-file mistake worth naming rather than rendering blank.\r\n n_ch = int(getattr(vd, \"num_channels\", 1) or 1)\r\n if n_ch > 1 or (vol.ndim == 3 and vol.shape[-1] in (3, 4) and vol.shape[0] not in (3, 4)):\r\n raise ValueError(\r\n f\"{os.path.basename(path)} looks like a COLOUR image \"\r\n f\"(shape {vol.shape}); the overlay needs an integer LABEL \"\r\n f\"image, one id per object, not an RGB rendering of one\"\r\n )\r\n if vol.ndim not in (2, 3):\r\n raise ValueError(f\"expected a 2D section or 3D stack, got shape {vol.shape}\")\r\n panel.set_input_segmentation(vol)\r\n except Exception as e:\r\n print(f\"Could not load segmentation for preview overlay: {e}\")\r\n panel.set_input_segmentation(None)\r\n # Say so on the control itself: a message on stdout beside a green\r\n # button reads as success.\r\n btn = getattr(self, \"input_segmentation_button\", None)\r\n if btn is not None:\r\n btn.setText(f\"Select Segmentation for Preview (Optional)\\nCould not read {os.path.basename(str(path))}\")\r\n tag_themed(btn, \"file\")\r\n\r\n def _on_atlas_changed(self, index):\r\n \"\"\"Handle atlas dropdown selection change.\r\n\r\n Built-in atlases (Allen CCF, Waxholm) auto-load. 'Custom Template...'\r\n opens a file dialog directly, unless the change is programmatic (init /\r\n session restore / config template) - see _suppress_template_dialog.\r\n \"\"\"\r\n prev_atlas = getattr(self, \"selected_atlas\", \"allen_ccf\")\r\n fam_key = self.atlas_combo.currentData()\r\n\r\n if fam_key != \"custom\":\r\n # Built-in atlas family: fill the variant sub-dropdown, then load the\r\n # selected variant (25 µm / 10 µm / Nissl...). _populate_variant_combo\r\n # returns the resolved variant-level registry key.\r\n resolved_key = self._populate_variant_combo(fam_key)\r\n self._load_builtin_atlas(resolved_key)\r\n else:\r\n self.selected_atlas = \"custom\"\r\n self._update_hemisphere_controls()\r\n self.variant_combo.setVisible(False)\r\n self.variant_static_label.setVisible(False)\r\n self.parcellation_combo.setVisible(False)\r\n self.age_combo.setVisible(False)\r\n # Custom: clear any loaded template, then browse immediately (only on\r\n # a genuine user pick - not during programmatic combo changes).\r\n # Hemisphere selection needs a midline, which a custom template only\r\n # has once the user ticks \"Hemisphere split\" and it is fitted - so\r\n # the dropdown starts forced to Whole and disabled, and\r\n # _update_half_combo_enabled unlocks it if and when that happens.\r\n self._custom_hemisphere_plane = None\r\n self._update_half_combo_enabled()\r\n self.template_info_label.setText(\"No template loaded\")\r\n tag_themed(self.template_info_label, \"file_warning\")\r\n self.template = None\r\n self.template_path = None\r\n self.custom_annotation_path = None\r\n self.custom_annotation_sdt_registration = False\r\n self._custom_atlas_um_per_px = None # custom-only spacing override\r\n self._template_data_cache = None\r\n self._template_preview_affine = None\r\n self._template_preview_full_shape = None\r\n self._annotation_data_cache = None # new atlas -> reload its annotation\r\n self._template_native_shape = None\r\n self._template_native_spacing = None\r\n # Invalidate any in-flight template metadata / preview workers from the\r\n # previously-selected built-in atlas. Otherwise a stale worker still passes\r\n # its seq guard and resurrects self.template/self.template_path (built-in) +\r\n # re-enables Register while the combo reads \"Custom\". _refresh_preview_template()\r\n # early-returns here (no template) so it never bumps _preview_template_seq itself.\r\n self._template_meta_seq += 1\r\n self._preview_template_seq += 1\r\n self.preview_template_button.setEnabled(False)\r\n self.register_button.setEnabled(False)\r\n self._refresh_preview_template()\r\n if not self._suppress_template_dialog:\r\n if not self._select_template_path():\r\n self._restore_atlas_selection(prev_atlas)\r\n\r\n def _populate_variant_combo(self, family_key, select_key=None):\r\n \"\"\"Fill the facet combos and the variant sub-dropdown for *family_key*,\r\n and return the resolved variant-level registry key to load.\r\n\r\n Three controls, populated outer-to-inner: the facet combos (parcellation,\r\n age) narrow which variants exist, then the variant combo lists what is\r\n left (template x resolution). A facet combo is shown only when the family\r\n has more than one value for it, so most families still show just the\r\n variant dropdown.\r\n\r\n Shows EVERY defined variant so a not-yet-downloaded one is discoverable,\r\n but greys out the un-installed ones - labelled \"... (not installed)\" with\r\n a tooltip pointing at the atlas manager - so they can't be selected.\r\n Signals are blocked so populating never re-triggers a load.\r\n \"\"\"\r\n from regtools.atlas.registry import variants_for\r\n\r\n selected = self._facet_selection(family_key, select_key)\r\n\r\n all_variants = variants_for(family_key, installed_only=False, **selected)\r\n installed = variants_for(family_key, installed_only=True, **selected)\r\n installed_names = {v.name for v in installed}\r\n\r\n # select_key wins whenever it names a variant that survives the facet\r\n # filter, installed or not - an explicit ask (session restore, or a\r\n # facet change carrying the untouched template/resolution forward via\r\n # _equivalent_variant_key) must not be silently swapped for some other\r\n # installed variant just because IT happens to be installed too. Only\r\n # with no specific ask, or one that no longer exists in this family/\r\n # facet combination, do we fall back to \"first installed\" / \"first\r\n # defined\" - the download-hint-surfacing default.\r\n all_names = {v.name for v in all_variants}\r\n if select_key in all_names:\r\n sel = select_key\r\n elif installed:\r\n sel = installed[0].name\r\n elif all_variants:\r\n sel = all_variants[0].name\r\n else:\r\n sel = None\r\n\r\n self.variant_combo.blockSignals(True)\r\n self.variant_combo.clear()\r\n for v in all_variants:\r\n is_installed = v.name in installed_names\r\n label = (v.variant_label or v.name) + (\"\" if is_installed else \" (not installed)\")\r\n self.variant_combo.addItem(label, v.name)\r\n if not is_installed and v.name != sel:\r\n item = self.variant_combo.model().item(self.variant_combo.count() - 1)\r\n if item is not None:\r\n item.setEnabled(False)\r\n item.setToolTip(\"Not downloaded - install it from the Config menu's Atlas manager.\")\r\n idx = self.variant_combo.findData(sel) if sel else -1\r\n self.variant_combo.setCurrentIndex(idx if idx >= 0 else 0)\r\n self.variant_combo.blockSignals(False)\r\n\r\n multi = len(all_variants) > 1\r\n self.variant_combo.setVisible(multi)\r\n static = \"\" if multi or not all_variants else (all_variants[0].variant_label or \"\")\r\n self.variant_static_label.setText(static)\r\n self.variant_static_label.setVisible(bool(static))\r\n\r\n return self.variant_combo.currentData() or family_key\r\n\r\n def _facet_selection(self, family_key, select_key=None):\r\n \"\"\"Populate the facet combos for *family_key*; return the chosen filters.\r\n\r\n Returns a dict suitable for ``variants_for(**kwargs)`` - a facet the\r\n family does not vary along is simply absent, which means \"don't filter\".\r\n \"\"\"\r\n from regtools.atlas.registry import facet_values, ATLAS_REGISTRY\r\n\r\n preferred = ATLAS_REGISTRY.get(select_key) if select_key else None\r\n chosen = {}\r\n for field, combo, labeller in (\r\n (\"parcellation\", self.parcellation_combo, _PARCELLATION_LABELS.get),\r\n (\"age\", self.age_combo, lambda v: v),\r\n ):\r\n # Read the combo's OWN current selection before clearing it - a\r\n # user just picking this very facet (_on_facet_changed re-entering\r\n # here with no select_key) must stick, not get discarded back to\r\n # item 0 by the clear()+repopulate below.\r\n prior = combo.currentData()\r\n values = facet_values(family_key, field, installed_only=False)\r\n combo.blockSignals(True)\r\n combo.clear()\r\n for value in values:\r\n combo.addItem(labeller(value) or str(value), value)\r\n want = getattr(preferred, field, None) if preferred is not None else None\r\n if want is None:\r\n want = prior\r\n if want is None:\r\n # No preferred/prior value: prefer a facet value that has\r\n # something installed, so e.g. a DevCCF install with only the\r\n # P56 package present still defaults there instead of to\r\n # E11.5 (values[0]) just because it sorts first. Only when\r\n # NOTHING in the family is installed does values[0] win back -\r\n # that preserves the existing \"show the download hint\" default.\r\n installed_values = facet_values(family_key, field, installed_only=True)\r\n want = installed_values[0] if installed_values else (values[0] if values else None)\r\n idx = combo.findData(want)\r\n combo.setCurrentIndex(idx if idx >= 0 else 0)\r\n combo.blockSignals(False)\r\n combo.setVisible(len(values) > 1)\r\n if len(values) > 1:\r\n chosen[field] = combo.currentData()\r\n return chosen\r\n\r\n def _on_facet_changed(self, _index):\r\n \"\"\"A facet changed: re-filter the variants and load the resolved key.\r\n\r\n Carries the axis the user did NOT touch. Without this the variant\r\n resolution falls through to \"first installed under the new filter\", so\r\n changing parcellation while on Nissl silently lands on the average\r\n template - and that key is what gets written into the run's metadata.\r\n \"\"\"\r\n fam_key = self.atlas_combo.currentData()\r\n if not fam_key or fam_key == \"custom\":\r\n return\r\n keep = self._equivalent_variant_key(fam_key)\r\n self._load_builtin_atlas(self._populate_variant_combo(fam_key, select_key=keep))\r\n\r\n def _equivalent_variant_key(self, family_key):\r\n \"\"\"The variant matching the current template/resolution under the facet\r\n values now selected, or None when this family has no counterpart.\r\n\r\n Called after the facet combos already hold the NEW values, so reading\r\n them here gives the destination, not the origin.\r\n \"\"\"\r\n from regtools.atlas.registry import ATLAS_REGISTRY, variants_for\r\n\r\n current = ATLAS_REGISTRY.get(getattr(self, \"selected_atlas\", None))\r\n if current is None:\r\n return None\r\n filters = {}\r\n # count() > 1 is the same condition _facet_selection uses to decide\r\n # visibility - safer than isVisible(), which is unreliable headless.\r\n if self.parcellation_combo.count() > 1:\r\n filters[\"parcellation\"] = self.parcellation_combo.currentData()\r\n if self.age_combo.count() > 1:\r\n filters[\"age\"] = self.age_combo.currentData()\r\n candidates = variants_for(family_key, installed_only=False, **filters)\r\n\r\n # (modality, resolution_um) is NOT a unique fingerprint for a variant -\r\n # do NOT go back to matching on it alone:\r\n # - both MERFISH templates ('allen_ccf_merfish' and\r\n # 'allen_ccf_merfish_symmetric') declare modality='merfish_density'\r\n # at the same 25 um, so a parcellation change from the symmetric one\r\n # silently lands on the NON-symmetric variant instead.\r\n # - all 6-8 DevCCF MRI contrasts ('devccf_mri_t2', 'devccf_mri_fa', ...)\r\n # declare modality='mri', frequently at the same per-stage\r\n # resolution, so an age change from one contrast silently lands on\r\n # a different one (or, when the stage's MRI resolution differs too,\r\n # finds nothing at all).\r\n # The variant label's text before \" - \" (\"Nissl\", \"MRI FA\", \"MERFISH\r\n # cell density (Zhuang), symmetric\") names the template/contrast itself\r\n # and is unique within a family/facet combination, so match on that stem\r\n # first; resolution_um only breaks a tie between two resolutions of the\r\n # same stem (e.g. \"Average template\" at 25 vs 10 um). Fall back to the\r\n # old modality/resolution match only when the stem isn't found at all.\r\n def stem(v):\r\n return (v.variant_label or \"\").split(\" - \", 1)[0]\r\n\r\n current_stem = stem(current)\r\n if current_stem:\r\n stem_matches = [v for v in candidates if stem(v) == current_stem]\r\n for v in stem_matches:\r\n if v.resolution_um == current.resolution_um:\r\n return v.name\r\n if stem_matches:\r\n return stem_matches[0].name\r\n\r\n for v in candidates:\r\n if v.modality == current.modality and v.resolution_um == current.resolution_um:\r\n return v.name\r\n return None\r\n\r\n def _on_variant_changed(self, index):\r\n \"\"\"Variant sub-dropdown changed - load that variant (same family).\"\"\"\r\n key = self.variant_combo.currentData()\r\n if key:\r\n self._load_builtin_atlas(key)\r\n\r\n def _update_coverage_warning(self, atlas=None):\r\n \"\"\"Show/hide the partial-FOV banner for the selected atlas.\r\n\r\n Registry-driven: any AtlasInfo carrying a ``coverage_note`` shows it, so\r\n a future partial atlas needs no GUI change. ``None`` (custom atlas)\r\n clears it - we know nothing about a user-supplied template's coverage.\r\n \"\"\"\r\n note = getattr(atlas, \"coverage_note\", None) if atlas is not None else None\r\n self.coverage_warning_label.setText(note or \"\")\r\n self.coverage_warning_label.setVisible(bool(note))\r\n\r\n def _load_builtin_atlas(self, atlas_key):\r\n \"\"\"Load a built-in atlas variant by its registry key (registry-driven).\r\n\r\n Consolidates what used to be a per-atlas if/elif: resolve the AtlasInfo,\r\n set hemisphere availability from ``whole_brain_only``, and load the\r\n template (or show the atlas's own download hint when it is absent).\r\n ``selected_atlas`` is set to the variant-level key for all downstream use.\r\n \"\"\"\r\n from regtools.registration.core.config import get_atlas\r\n from regtools.atlas.registry import _variant_installed\r\n\r\n atlas = get_atlas(atlas_key)\r\n self.selected_atlas = atlas_key\r\n self._last_non_custom_atlas = atlas_key\r\n self.custom_annotation_path = None\r\n self.custom_annotation_sdt_registration = False\r\n self._custom_atlas_um_per_px = None # built-in headers carry spacing\r\n self._custom_hemisphere_plane = None # belongs to the custom image only\r\n\r\n # whole_brain_only atlases stay forced to Whole; the rest are unlocked.\r\n # Also hides the draw toggle (a built-in plane is read, not drawn) and\r\n # turns its midline overlay on.\r\n self._update_hemisphere_controls()\r\n self._update_coverage_warning(atlas)\r\n\r\n # _variant_installed (template AND annotation), not a template-only\r\n # isfile check: 12 of the 18 Allen variants are annotation-only (they\r\n # share the average/Nissl/MERFISH template with another variant), so a\r\n # template-only check reports them as loaded while their annotation is\r\n # missing - the pipeline then warns-and-continues with degraded region\r\n # counts instead of the user seeing the download hint.\r\n if _variant_installed(atlas):\r\n self._process_template_path(atlas.template_path)\r\n else:\r\n fam = atlas.family_label or atlas.name\r\n variant = atlas.variant_label or \"\"\r\n hint = atlas.download_hint or \"Run the atlas downloader\"\r\n self.template_info_label.setText(f\"{fam}\\n{variant} not found\\n{hint}\".replace(\"\\n\\n\", \"\\n\"))\r\n tag_themed(self.template_info_label, \"file_warning\")\r\n\r\n def _select_template_path(self):\r\n \"\"\"Open the custom atlas dialog and apply the confirmed selection.\"\"\"\r\n dlg = CustomAtlasDialog(self, file_filter=self._custom_atlas_file_filter())\r\n if dlg.exec() != QDialog.DialogCode.Accepted:\r\n return False\r\n return self._apply_custom_atlas_choice(\r\n template_path=dlg.template_path,\r\n annotation_path=dlg.annotation_path,\r\n um_per_px=dlg.um_per_px,\r\n )\r\n\r\n def _custom_atlas_file_filter(self):\r\n return (\r\n \"All supported (*.nii *.nii.gz *.nrrd *.nhdr *.tif *.tiff \"\r\n \"*.dcm *.ima *.png *.jpg *.jpeg *.bmp *.gif *.webp *.vsi \"\r\n \"*.npy *.npz);;NIfTI (*.nii *.nii.gz);;NRRD (*.nrrd *.nhdr);;\"\r\n \"TIFF (*.tif *.tiff);;DICOM (*.dcm *.ima);;\"\r\n \"Images (*.png *.jpg *.jpeg *.bmp *.gif *.webp);;VSI (*.vsi);;\"\r\n \"NumPy (*.npy *.npz);;All files (*)\"\r\n )\r\n\r\n def _restore_atlas_selection(self, atlas_key):\r\n \"\"\"Re-select a (possibly variant-level) atlas key in the family + variant\r\n dropdowns without re-prompting for a custom template.\r\n\r\n The atlas_combo holds FAMILY keys, so a variant key (e.g. allen_ccf_10um)\r\n is mapped to its family; the variant is then pinned in the sub-dropdown.\r\n Signals are blocked so exactly one template load happens.\r\n \"\"\"\r\n atlas_key = atlas_key or getattr(self, \"_last_non_custom_atlas\", \"allen_ccf\")\r\n self._suppress_template_dialog = True\r\n try:\r\n if atlas_key == \"custom\":\r\n self.atlas_combo.setCurrentIndex(self.atlas_combo.findData(\"custom\"))\r\n return\r\n from regtools.registration.core.config import get_atlas\r\n\r\n fam_key = get_atlas(atlas_key).family_key\r\n fam_idx = self.atlas_combo.findData(fam_key)\r\n if fam_idx < 0:\r\n fam_idx, fam_key, atlas_key = (self.atlas_combo.findData(\"allen_ccf\"), \"allen_ccf\", \"allen_ccf\")\r\n # Set the family WITHOUT auto-loading its first variant; then load the\r\n # specific variant once via _populate_variant_combo + _load_builtin_atlas.\r\n self.atlas_combo.blockSignals(True)\r\n self.atlas_combo.setCurrentIndex(fam_idx)\r\n self.atlas_combo.blockSignals(False)\r\n resolved = self._populate_variant_combo(fam_key, select_key=atlas_key)\r\n self._load_builtin_atlas(resolved)\r\n finally:\r\n self._suppress_template_dialog = False\r\n\r\n def _apply_custom_atlas_choice(self, *, template_path, annotation_path, um_per_px=None):\r\n self._update_coverage_warning(None)\r\n template_path = template_path or None\r\n annotation_path = annotation_path or None\r\n if not template_path and not annotation_path:\r\n QMessageBox.warning(\r\n self,\r\n \"Custom Atlas\",\r\n \"Choose a template, an annotation, or both before confirming.\",\r\n )\r\n return False\r\n self.custom_annotation_path = annotation_path\r\n self.custom_annotation_sdt_registration = False\r\n # Remember the optional µm/px override so both the preview load and the\r\n # registration run (RegistrationOpts.fixed_um_per_px) see the true\r\n # spacing instead of the loader's ~1.25µm image-format fallback.\r\n self._custom_atlas_um_per_px = um_per_px\r\n if not template_path and annotation_path:\r\n QMessageBox.warning(\r\n self,\r\n \"Annotation-only Custom Atlas\",\r\n \"No intensity template was selected. RegTools will register to a \"\r\n \"signed-distance target generated from the annotation foreground.\",\r\n )\r\n sdf_dir = os.path.join(os.path.dirname(annotation_path), \".regtools_custom_atlas\")\r\n stem = os.path.splitext(os.path.basename(annotation_path))[0]\r\n if stem.endswith(\".nii\"):\r\n stem = stem[:-4]\r\n template_path = os.path.join(sdf_dir, f\"{stem}_sdf_template.nii.gz\")\r\n # Building the SDF target reads the annotation and runs two full-volume\r\n # distance transforms - it can raise (unreadable/empty annotation, or a\r\n # read-only directory where sdf_dir can't be created). Surface that as\r\n # an error dialog and return False, so _on_atlas_changed reverts the\r\n # combo, rather than raising unhandled inside the atlas-combo signal.\r\n from PySide6.QtWidgets import QApplication\r\n\r\n QApplication.setOverrideCursor(Qt.CursorShape.WaitCursor)\r\n try:\r\n _make_annotation_sdf_target(annotation_path, template_path)\r\n except Exception as exc:\r\n QApplication.restoreOverrideCursor()\r\n self.custom_annotation_path = None\r\n QMessageBox.critical(\r\n self,\r\n \"Annotation-only Custom Atlas\",\r\n f\"Could not build a signed-distance target from the annotation:\\n\\n{exc}\",\r\n )\r\n return False\r\n finally:\r\n if QApplication.overrideCursor() is not None:\r\n QApplication.restoreOverrideCursor()\r\n self.custom_annotation_sdt_registration = True\r\n self._process_template_path(template_path, um_per_px=self._custom_atlas_um_per_px)\r\n return True\r\n\r\n def _select_output_path(self, check_empty=True):\r\n \"\"\"\r\n Select the path to save the outputs to and saves the selected folder path internally.\r\n \"\"\"\r\n folder_path = QFileDialog.getExistingDirectory(None, \"Select folder to save the output to\")\r\n if folder_path == \"\": # If user cancels out of the dialog, exit.\r\n return\r\n self._process_output_path(folder_path)\r\n\r\n def _handle_input_drop(self, file_path):\r\n \"\"\"Handle file/folder drop on input button. Auto-detects format.\"\"\"\r\n self._process_input_path(file_path)\r\n\r\n def _handle_points_drop(self, file_path):\r\n \"\"\"Handle file drop on points button - adds to existing list.\"\"\"\r\n # Add dropped file to existing list\r\n if file_path not in self.input_points_list:\r\n new_list = self.input_points_list + [file_path]\r\n self._process_points_paths(new_list)\r\n\r\n def _handle_output_drop(self, folder_path):\r\n \"\"\"Handle folder drop on output button.\"\"\"\r\n if os.path.isdir(folder_path):\r\n self._process_output_path(folder_path)\r\n\r\n def _select_slice_mapping(self):\r\n \"\"\"Open file dialog to select a slice mapping CSV.\"\"\"\r\n file_path, _ = QFileDialog.getOpenFileName(\r\n None, \"Select Slice Mapping CSV\", filter=\"CSV files (*.csv);;All files (*)\"\r\n )\r\n if not file_path:\r\n return\r\n self._process_slice_mapping(file_path)\r\n\r\n def _handle_slice_mapping_drop(self, file_path):\r\n \"\"\"Handle file drop on slice mapping button.\"\"\"\r\n self._process_slice_mapping(file_path)\r\n\r\n def _process_slice_mapping(self, file_path):\r\n \"\"\"Validate and store a slice mapping CSV.\r\n\r\n Expected format: CSV with columns 'input_file' and 'template_index'.\r\n Each row maps an input image filename to its target template slice index.\r\n \"\"\"\r\n import csv\r\n\r\n try:\r\n with open(file_path, newline=\"\") as f:\r\n reader = csv.DictReader(f)\r\n headers = reader.fieldnames or []\r\n if \"input_file\" not in headers or \"template_index\" not in headers:\r\n QMessageBox.warning(\r\n self,\r\n \"Invalid Slice Mapping\",\r\n f\"CSV must have 'input_file' and 'template_index' columns.\\n\"\r\n f\"Found columns: {', '.join(headers)}\",\r\n )\r\n return\r\n rows = list(reader)\r\n if not rows:\r\n QMessageBox.warning(self, \"Invalid Slice Mapping\", \"CSV is empty.\")\r\n return\r\n # Validate template_index values are integers\r\n for i, row in enumerate(rows):\r\n try:\r\n int(row[\"template_index\"])\r\n except (ValueError, KeyError):\r\n QMessageBox.warning(\r\n self,\r\n \"Invalid Slice Mapping\",\r\n f\"Row {i + 2}: 'template_index' must be an integer, got '{row.get('template_index', '')}'\",\r\n )\r\n return\r\n except Exception as e:\r\n QMessageBox.warning(self, \"Error Reading CSV\", str(e))\r\n return\r\n\r\n self.slice_mapping_csv = file_path\r\n # Keep the mapping in memory so the atlas pane can show the template slice\r\n # each section is actually registered against (batch 2D has no CCF spinbox).\r\n self._slice_mapping = {\r\n os.path.basename(str(r.get(\"input_file\", \"\"))).lower(): int(r[\"template_index\"])\r\n for r in rows\r\n if r.get(\"input_file\")\r\n }\r\n self._slice_mapping_first = int(rows[0][\"template_index\"])\r\n self.slice_mapping_button.setText(\r\n f\"Slice Mapping: {os.path.basename(file_path)}\\n{len(rows)} slice mappings loaded\"\r\n )\r\n tag_themed(self.slice_mapping_button, \"file_success\")\r\n # The atlas pane may have been pushed before a mapping existed (so it is\r\n # showing a free-scrubbing volume); re-push it so it starts tracking the\r\n # mapped index, then point it at the previewed section's slice.\r\n self._refresh_preview_template()\r\n self._sync_atlas_index_to_preview()\r\n # Enable register button if template is also set\r\n if self.input_path and self.template_path:\r\n self.register_button.setEnabled(True)\r\n\r\n def _slice_mapping_index_for(self, input_path):\r\n \"\"\"Template slice index mapped to ``input_path``, or the first row's.\r\n\r\n Batch 2D takes its per-section indices from the slice-mapping CSV rather\r\n than the (hidden) CCF spinbox, so the atlas pane has to read them from\r\n there to show the slice a section is registered against.\r\n \"\"\"\r\n mapping = getattr(self, \"_slice_mapping\", None)\r\n if not mapping:\r\n return None\r\n if input_path:\r\n hit = mapping.get(os.path.basename(str(input_path)).lower())\r\n if hit is not None:\r\n return int(hit)\r\n return getattr(self, \"_slice_mapping_first\", None)\r\n\r\n def _preview_ccf_index(self):\r\n \"\"\"The template slice the atlas pane should show, or None to free-scrub.\r\n\r\n A single 2D section follows the CCF spinbox; a batch follows its\r\n slice-mapping CSV entry for the previewed section. Everything else (3D\r\n jobs, a 2D template) has no single meaningful index.\r\n \"\"\"\r\n if getattr(self, \"_template_is_2d\", False):\r\n return None\r\n if self.data_type == \"2d\":\r\n return int(self.ccf_index_spin.value())\r\n if self.data_type == \"batch_2d\":\r\n return self._slice_mapping_index_for(getattr(self, \"_previewed_input_path\", None))\r\n return None\r\n\r\n def _sync_atlas_index_to_preview(self):\r\n \"\"\"Point the atlas pane at the previewed section's template slice.\"\"\"\r\n panel = getattr(self, \"preview_panel\", None)\r\n idx = self._preview_ccf_index()\r\n if panel is not None and idx is not None:\r\n panel.set_ccf_index(int(idx))\r\n\r\n def _select_moving_mask(self):\r\n \"\"\"Open file dialog to select an optional 2D moving-image mask.\"\"\"\r\n file_path, _ = QFileDialog.getOpenFileName(\r\n None, \"Select moving mask (optional)\", filter=\"NIfTI (*.nii *.nii.gz);;All files (*)\"\r\n )\r\n if not file_path:\r\n return\r\n self.moving_mask_path = file_path\r\n self.moving_mask_button.setText(f\"Moving mask: {os.path.basename(file_path)}\")\r\n self.moving_mask_label.setText(os.path.basename(file_path))\r\n self.moving_mask_clear_button.setVisible(True)\r\n\r\n def _clear_moving_mask(self):\r\n \"\"\"Remove the currently selected moving mask.\"\"\"\r\n self.moving_mask_path = None\r\n self.moving_mask_button.setText(\"Select moving mask (optional)\")\r\n self.moving_mask_label.setText(\"\")\r\n self.moving_mask_clear_button.setVisible(False)\r\n\r\n # ---- Atlas edit mask (2D only): paints the FIXED template ---------------\r\n def _atlas_edit_ctrl(self):\r\n \"\"\"The live AtlasEditMaskController owned by the preview panel's atlas\r\n pane, or None before the panel/atlas exist. Fetched fresh on every use\r\n (rather than cached) because the panel recreates the controller\r\n whenever the atlas display grid changes shape (new template load).\"\"\"\r\n panel = getattr(self, \"preview_panel\", None)\r\n return getattr(panel, \"atlas_edit_controller\", None) if panel is not None else None\r\n\r\n def _on_atlas_edit_armed_toggled(self, on):\r\n ctrl = self._atlas_edit_ctrl()\r\n if ctrl is not None:\r\n ctrl.set_enabled(bool(on))\r\n for w in (\r\n self.atlas_edit_brush_button,\r\n self.atlas_edit_polygon_button,\r\n self.atlas_edit_eraser_button,\r\n self.atlas_edit_radius_spin,\r\n self.atlas_edit_clear_button,\r\n self.atlas_edit_fill_spin,\r\n ):\r\n w.setEnabled(bool(on))\r\n\r\n def _on_atlas_edit_tool_changed(self):\r\n ctrl = self._atlas_edit_ctrl()\r\n if ctrl is not None:\r\n ctrl.set_tool(\"polygon\" if self.atlas_edit_polygon_button.isChecked() else \"brush\")\r\n\r\n def _on_atlas_edit_eraser_toggled(self, on):\r\n ctrl = self._atlas_edit_ctrl()\r\n if ctrl is not None:\r\n ctrl.set_eraser(bool(on))\r\n\r\n def _on_atlas_edit_radius_changed(self, value):\r\n ctrl = self._atlas_edit_ctrl()\r\n if ctrl is not None:\r\n ctrl.set_brush_radius(int(value))\r\n\r\n def _on_atlas_edit_clear(self):\r\n ctrl = self._atlas_edit_ctrl()\r\n if ctrl is not None:\r\n ctrl.clear()\r\n\r\n def _atlas_edit_run_template_shape(self, full_shape, half_mode):\r\n \"\"\"``(H, W)`` grid ``_load_and_orient_2d`` will produce for the\r\n template slice at run time, given *full_shape* (the PRE-half-crop PIR\r\n shape captured by the preview loader, ``_template_preview_full_shape``).\r\n\r\n ``halve_and_pad`` is a no-op for ``half_mode == 'whole'`` but a real\r\n crop+pad for 'left'/'right' (see its docstring in\r\n ``transform_utils.py``) - the mask must be saved on that POST-crop\r\n grid, or the run's shape-guard in ``_load_and_orient_2d`` silently\r\n rejects it (the crop makes the grids disagree).\r\n\r\n Mirrors the plane resolution ``_load_and_orient_2d`` itself performs:\r\n for a 3D-atlas fixed image (``ccf_index`` in play), the maintainer-\r\n fitted plane (``pipeline._plane_for_2d_run``) converted into the\r\n template's PIR frame via ``_plane_in_array_frame``; for a plain 2D\r\n fixed image, ``plane=None`` - matching ``_load_and_orient_2d``, which\r\n never applies ``hemisphere_plane`` to a 2D fixed image (its\r\n ``_fixed_is_2d`` branch never sets ``_template_plane``). Any failure\r\n to resolve the real plane falls back to ``plane=None`` (the array-\r\n midpoint crop), the same degraded behaviour ``_plane_for_2d_run``\r\n itself falls back to on a mismatch.\r\n \"\"\"\r\n from regtools.registration.gui.atlas_edit_io import atlas_edit_run_template_shape\r\n\r\n return atlas_edit_run_template_shape(\r\n self.template_path,\r\n None if getattr(self, \"_template_is_2d\", False) else int(self.ccf_index_spin.value()),\r\n getattr(self, \"_template_is_2d\", False),\r\n self.selected_atlas if self._is_builtin_atlas() else None,\r\n full_shape,\r\n half_mode,\r\n )\r\n\r\n def _save_atlas_edit_mask(self, out_dir, template_slice_shape):\r\n \"\"\"Write the current atlas edit mask (on the run's template-slice grid)\r\n as a uint8 NIfTI. Returns the path, or None if nothing is masked.\"\"\"\r\n from regtools.registration.gui.atlas_edit_io import save_atlas_edit_mask\r\n\r\n return save_atlas_edit_mask(getattr(self, \"_atlas_edit_controller\", None), out_dir, template_slice_shape)\r\n\r\n def _process_input_path(self, file_path):\r\n \"\"\"Process input path with automatic format detection (shared by click and drop handlers).\"\"\"\r\n from regtools.utils.io.volume_io import detect_format\r\n\r\n # New input selection of ANY format - supersede any in-flight metadata\r\n # worker (NIfTI/VSI/image) so a slow stale load can't clobber the label\r\n # or moving-µm spin for the input the user just replaced.\r\n self._input_load_seq += 1\r\n\r\n # For lists of files, detect format from the first file\r\n detect_target = file_path[0] if isinstance(file_path, list) else file_path\r\n\r\n fmt, error = detect_format(detect_target)\r\n if error:\r\n # Directories may be TIFF section folders or DICOM series - try as tiff\r\n if os.path.isdir(detect_target):\r\n fmt = \"tiff\"\r\n else:\r\n QMessageBox.warning(self, \"Format Detection\", f\"Could not detect format:\\n{error}\")\r\n return\r\n\r\n # Map detected format to internal data_type\r\n if fmt in (\"nifti\", \"nrrd\"):\r\n self.data_type = \"nifti\"\r\n self._process_nifti_input(file_path, fmt)\r\n elif fmt == \"dicom\":\r\n self.data_type = \"nifti\"\r\n self._process_nifti_input(file_path, fmt)\r\n elif fmt == \"tiff\":\r\n # Could be multi-page TIFF/image volume or single 2D - let _load_image_metadata decide\r\n self.data_type = \"tiff\"\r\n self._update_format_controls()\r\n # Show actual extension in label\r\n if isinstance(detect_target, str) and os.path.isfile(detect_target):\r\n ext = os.path.splitext(detect_target)[1].upper().lstrip(\".\")\r\n self.detected_format_label.setText(f\"Detected: {ext or 'Image'}\")\r\n else:\r\n self.detected_format_label.setText(\"Detected: Image directory\")\r\n self.input_folder_button.setText(\"Select Input Data\\nLoading...\")\r\n self.input_folder_button.setEnabled(False)\r\n self._load_image_metadata(file_path)\r\n return\r\n elif fmt == \"vsi\":\r\n # VSI whole-slide images are large; decoding them inline (for metadata\r\n # AND the moving-resolution pre-fill) froze the GUI. Accept the path\r\n # immediately and load metadata + spacing off the UI thread.\r\n self.data_type = \"2d\"\r\n self.input_path = file_path\r\n display_name = os.path.basename(file_path)\r\n self.input_folder_button.setText(f\"Input: {display_name}\\nReading metadata...\")\r\n self.detected_format_label.setText(\"Detected: VSI\")\r\n tag_themed(self.input_folder_button, \"file_success\")\r\n self.preview_input_button.setEnabled(True)\r\n if self.input_path and self.template_path:\r\n self.register_button.setEnabled(True)\r\n print(f\"Selected input path: {file_path}\")\r\n self._start_vsi_metadata(file_path, display_name)\r\n self._refresh_preview_input(self.input_path, self.data_type)\r\n else:\r\n QMessageBox.warning(self, \"Unsupported Format\", f\"Format '{fmt}' is not supported for registration input.\")\r\n return\r\n\r\n self._update_format_controls()\r\n\r\n def _process_nifti_input(self, file_path, fmt):\r\n \"\"\"Accept a NIfTI/NRRD/DICOM input; read metadata off the UI thread.\r\n\r\n The header read (and NRRD/DICOM full read) is done in a worker so a\r\n slow/unresponsive network share can't freeze the GUI - the path is\r\n accepted immediately and the preview + metadata load asynchronously.\r\n \"\"\"\r\n self.input_path = file_path\r\n display_name = os.path.basename(file_path) if isinstance(file_path, str) else os.path.basename(file_path[0])\r\n self.input_folder_button.setText(f\"Input: {display_name}\\nReading metadata...\")\r\n self.detected_format_label.setText(f\"Detected: {fmt.upper()}\")\r\n tag_themed(self.input_folder_button, \"file_success\")\r\n self.preview_input_button.setEnabled(True)\r\n if self.input_path and self.template_path:\r\n self.register_button.setEnabled(True)\r\n\r\n print(f\"Selected input path: {file_path}\")\r\n self._start_input_metadata(file_path, fmt, display_name)\r\n self._refresh_preview_input(self.input_path, self.data_type)\r\n\r\n def _start_input_metadata(self, file_path, fmt, display_name):\r\n \"\"\"Read shape/zooms/dtype for the metadata label off the UI thread.\"\"\"\r\n self._input_meta_seq += 1\r\n _seq = self._input_meta_seq\r\n _ils = self._input_load_seq # cross-format supersede guard\r\n\r\n def _on_result(meta_str):\r\n if _seq != self._input_meta_seq or _ils != self._input_load_seq:\r\n return\r\n self.input_folder_button.setText(f\"Input: {display_name}\\n{meta_str}\")\r\n self.detected_format_label.setText(f\"Detected: {fmt.upper()}\")\r\n tag_themed(self.input_folder_button, \"file_success\")\r\n\r\n def _on_error(msg):\r\n if _seq != self._input_meta_seq or _ils != self._input_load_seq:\r\n return\r\n self.input_folder_button.setText(f\"Input: {display_name}\\nMetadata unreadable - file may still register\")\r\n self.detected_format_label.setText(f\"Detected: {fmt.upper()} (metadata error)\")\r\n tag_themed(self.input_folder_button, \"file_warning\")\r\n print(f\"Warning: Could not read metadata: {msg}\")\r\n\r\n self._start_worker(_InputMetadataWorker(file_path, fmt), self._input_meta_threads, _on_result, _on_error)\r\n\r\n def _start_vsi_metadata(self, file_path, display_name):\r\n \"\"\"Read VSI metadata (shape + moving µm/px) off the UI thread.\r\n\r\n Mirrors _start_input_metadata's worker/teardown so a large VSI can't\r\n freeze the GUI while it decodes.\r\n \"\"\"\r\n self._vsi_meta_seq += 1\r\n _seq = self._vsi_meta_seq\r\n _ils = self._input_load_seq # cross-format supersede guard\r\n\r\n def _on_result(payload):\r\n if _seq != self._vsi_meta_seq or _ils != self._input_load_seq:\r\n return\r\n metadata_str, moving_um = payload\r\n self.input_folder_button.setText(f\"Input: {display_name}\\n{metadata_str}\")\r\n self.detected_format_label.setText(\"Detected: VSI\")\r\n tag_themed(self.input_folder_button, \"file_success\")\r\n if moving_um:\r\n lo, hi = self.moving_um_spin.minimum(), self.moving_um_spin.maximum()\r\n self.moving_um_spin.setValue(max(lo, min(hi, moving_um)))\r\n\r\n def _on_error(msg):\r\n if _seq != self._vsi_meta_seq or _ils != self._input_load_seq:\r\n return\r\n self.input_folder_button.setText(f\"Input: {display_name}\\nMetadata unreadable - file may still register\")\r\n self.detected_format_label.setText(\"Detected: VSI (metadata error)\")\r\n tag_themed(self.input_folder_button, \"file_warning\")\r\n print(f\"Warning: Could not read VSI metadata: {msg}\")\r\n\r\n self._start_worker(_VsiMetadataWorker(file_path), self._vsi_meta_threads, _on_result, _on_error)\r\n\r\n def _process_2d_input(self, file_path):\r\n \"\"\"Process a 2D image input file (legacy path; retained for reference).\r\n\r\n No longer reached from the VSI branch (which now loads metadata off the\r\n UI thread via _start_vsi_metadata); kept for any external/manual callers.\r\n \"\"\"\r\n _meta_error = None\r\n try:\r\n from skimage import io as skio\r\n\r\n img = skio.imread(file_path)\r\n if img.ndim == 3:\r\n metadata_str = f\"{img.shape[0]}×{img.shape[1]}px, {img.shape[2]}ch, {img.dtype}\"\r\n else:\r\n metadata_str = f\"{img.shape[0]}×{img.shape[1]}px, {img.dtype}\"\r\n self.input_folder_button.setText(f\"Input: {os.path.basename(file_path)}\\n{metadata_str}\")\r\n except Exception as e:\r\n _meta_error = e\r\n self.input_folder_button.setText(\r\n f\"Input: {os.path.basename(file_path)}\\nMetadata unreadable - file may still register\"\r\n )\r\n\r\n self.detected_format_label.setText(\"Detected: 2D Image\")\r\n self.input_path = file_path\r\n if _meta_error is not None:\r\n tag_themed(self.input_folder_button, \"file_warning\")\r\n QMessageBox.warning(\r\n self,\r\n \"Metadata Read Warning\",\r\n f\"Could not read metadata from the selected file:\\n{_meta_error}\\n\\n\"\r\n \"The file path has been accepted and registration may still proceed, \"\r\n \"but verify the file is a valid image.\",\r\n )\r\n else:\r\n tag_themed(self.input_folder_button, \"file_success\")\r\n self.preview_input_button.setEnabled(True)\r\n\r\n if self.input_path and self.template_path:\r\n self.register_button.setEnabled(True)\r\n\r\n print(f\"Selected input path: {file_path}\")\r\n self._refresh_preview_input(self.input_path, self.data_type)\r\n\r\n def _load_image_metadata(self, file_path):\r\n \"\"\"Gather image metadata in a background thread (TIFF, PNG, JPG, BMP, etc.).\"\"\"\r\n _ils = self._input_load_seq # cross-format supersede guard\r\n\r\n def _on_meta_done(result):\r\n if _ils != self._input_load_seq:\r\n return # a newer input (any format) was selected - drop this result\r\n self.input_folder_button.setEnabled(True)\r\n mismatch = result.get(\"resolution_mismatch\")\r\n\r\n # Multi-page TIFF: ask whether pages are sections or color channels\r\n if result.get(\"is_multipage_tif\"):\r\n from regtools.registration.gui.dialogs import (\r\n MultiPageTiffPrompt,\r\n ChannelReducePrompt,\r\n )\r\n\r\n n_pages = result[\"n_pages\"]\r\n y_shape = result[\"y_shape\"]\r\n x_shape = result[\"x_shape\"]\r\n\r\n which = MultiPageTiffPrompt(n_pages, y_shape, x_shape, parent=self).choose()\r\n if which == \"channels\":\r\n # Treat as a single 2D section; ask how to reduce channels\r\n # before registration (average vs. pick a specific channel).\r\n reduce_choice = ChannelReducePrompt(n_pages, parent=self).choose()\r\n if reduce_choice is None:\r\n self.input_folder_button.setText(\"Select Input Data\\nNo data loaded\")\r\n tag_themed(self.input_folder_button, \"file_warning\")\r\n return\r\n\r\n self.tif_pages_as_channels = True\r\n if reduce_choice[\"mode\"] == \"channel\":\r\n self.tif_pages_reduce_mode = \"channel\"\r\n self.tif_pages_channel_index = int(reduce_choice[\"channel_index\"])\r\n _reduce_desc = f\"channel {self.tif_pages_channel_index}\"\r\n else:\r\n self.tif_pages_reduce_mode = \"average\"\r\n self.tif_pages_channel_index = 0\r\n _reduce_desc = \"avg of channels\"\r\n\r\n result[\"switch_2d\"] = True\r\n result[\"metadata_str\"] = f\"{y_shape}×{x_shape}px, {n_pages} channels -> {_reduce_desc}\"\r\n elif which == \"sections\":\r\n # Current behaviour: volume of n_pages sections\r\n self.tif_pages_as_channels = False\r\n result[\"num_sections\"] = n_pages\r\n result[\"switch_2d\"] = False\r\n result[\"metadata_str\"] = f\"{n_pages} page{'s' if n_pages != 1 else ''}, {y_shape}×{x_shape}px\"\r\n else:\r\n # Cancelled - reset the input button and bail out\r\n self.input_folder_button.setText(\"Select Input Data\\nNo data loaded\")\r\n tag_themed(self.input_folder_button, \"file_warning\")\r\n return\r\n\r\n if result.get(\"switch_2d\"):\r\n # Single-page TIFF: auto-switch to 2D mode\r\n self.data_type = \"2d\"\r\n self.resolution_warning_label.setVisible(False)\r\n self._update_format_controls()\r\n self.detected_format_label.setText(\"Detected: 2D Image\")\r\n self.input_folder_button.setText(f\"Input: {result['display_name']}\\n{result['metadata_str']}\")\r\n elif mismatch:\r\n # Resolution mismatch detected -> batch 2D mode\r\n self.data_type = \"batch_2d\"\r\n shapes_summary = \", \".join(\r\n f\"{h}×{w} ({len(files)} file{'s' if len(files) != 1 else ''})\"\r\n for (h, w), files in sorted(mismatch.items(), key=lambda x: -len(x[1]))\r\n )\r\n self.resolution_warning_label.setText(\r\n f\"Warning: Mismatched resolutions detected: {shapes_summary}.\\n\"\r\n f\"These appear to be manually sectioned slices. \"\r\n f\"A slice mapping CSV is required to map each input image to a template slice index.\"\r\n )\r\n self.resolution_warning_label.setVisible(True)\r\n self.slice_mapping_csv = None\r\n self.slice_mapping_button.setText(\r\n \"Select Slice Mapping CSV (Required)\\nCSV with columns: input_file, template_index\"\r\n )\r\n tag_themed(self.slice_mapping_button, \"file_warning\")\r\n self._update_format_controls()\r\n self.detected_format_label.setText(f\"Detected: {result['num_sections']} images - 2D Batch Registration\")\r\n self.input_folder_button.setText(f\"Input: {result['display_name']}\\n{result['metadata_str']}\")\r\n else:\r\n self.resolution_warning_label.setVisible(False)\r\n self.num_sections = result[\"num_sections\"]\r\n self.y_shape = result[\"y_shape\"]\r\n self.x_shape = result[\"x_shape\"]\r\n self.input_folder_button.setText(f\"Input: {result['display_name']}\\n{result['metadata_str']}\")\r\n self.input_path = result[\"file_path\"]\r\n tag_themed(self.input_folder_button, \"file_success\")\r\n self.preview_input_button.setEnabled(True)\r\n if self.input_path and self.template_path:\r\n if self.data_type == \"batch_2d\" and not self.slice_mapping_csv:\r\n self.register_button.setEnabled(False)\r\n else:\r\n self.register_button.setEnabled(True)\r\n print(f\"Selected input path: {result['file_path']}\")\r\n # Kick the input preview panel (off-thread).\r\n self._refresh_preview_input(self.input_path, self.data_type)\r\n\r\n def _on_meta_error(msg):\r\n if _ils != self._input_load_seq:\r\n return # superseded by a newer input selection\r\n self.input_folder_button.setEnabled(True)\r\n self.input_folder_button.setText(\"Select Input Data\\nNo data loaded\")\r\n tag_themed(self.input_folder_button, \"file_warning\")\r\n QMessageBox.critical(self, \"Error\", msg)\r\n\r\n self._meta_worker = _MetaWorker(file_path)\r\n self._meta_worker.finished.connect(_on_meta_done)\r\n self._meta_worker.error.connect(_on_meta_error)\r\n # Keep the thread referenced even if _meta_worker is overwritten mid-run (A4).\r\n self._bg_threads.append(self._meta_worker)\r\n self._meta_worker.finished.connect(\r\n lambda *_a, w=self._meta_worker: (self._bg_threads.remove(w) if w in self._bg_threads else None)\r\n )\r\n self._meta_worker.error.connect(\r\n lambda *_a, w=self._meta_worker: (self._bg_threads.remove(w) if w in self._bg_threads else None)\r\n )\r\n self._meta_worker.start()\r\n\r\n def _start_worker(self, task, threads_list, on_result, on_error=None, on_finished=None):\r\n \"\"\"Run a moveToThread QObject worker with the standard lifecycle\r\n (Epic #128 / #129).\r\n\r\n Encapsulates the create-thread / moveToThread / relay / result|error ->\r\n quit / finished -> deleteLater + drop-from-keep-alive block that every\r\n background worker in this tab repeated. Guarantees:\r\n\r\n * ``on_result`` / ``on_error`` run on the GUI thread via\r\n :class:`GuiThreadRelay` - a bare closure on a moveToThread'd sender is\r\n a DIRECT connection and would run the slot (and its widget touches) in\r\n the worker thread, deadlocking and stranding the thread.\r\n * The worker's ``(thread, task)`` pair is appended to ``threads_list``\r\n before ``start()`` (so it can't be GC'd mid-run and is drained by\r\n :meth:`_shutdown_workers`) and removed on ``finished``.\r\n * ``result``/``error`` also quit the thread; ``finished`` deletes the\r\n relay, task, and thread. NEVER call ``thread.wait()`` in a worker slot.\r\n\r\n ``on_error`` may be ``None``. ``on_finished`` (optional) runs on the GUI\r\n thread when the thread finishes, for per-site bookkeeping such as\r\n clearing a coalescing 'inflight' flag. Signal arity is whatever the\r\n worker declares - the relay forwards all args, so ``result(a, b, c)``\r\n reaches ``on_result(a, b, c)`` unchanged.\r\n \"\"\"\r\n thread = QThread(self)\r\n task.moveToThread(thread)\r\n pair = [thread, task]\r\n\r\n def _drop():\r\n try:\r\n threads_list.remove(pair)\r\n except ValueError:\r\n pass\r\n\r\n relay = GuiThreadRelay(self, on_result=on_result, on_error=on_error)\r\n task.result.connect(relay.result)\r\n task.error.connect(relay.error)\r\n task.result.connect(thread.quit)\r\n task.error.connect(thread.quit)\r\n thread.finished.connect(_drop)\r\n if on_finished is not None:\r\n thread.finished.connect(on_finished)\r\n thread.finished.connect(relay.deleteLater)\r\n thread.finished.connect(task.deleteLater)\r\n thread.finished.connect(thread.deleteLater)\r\n thread.started.connect(task.run)\r\n threads_list.append(pair)\r\n thread.start()\r\n return pair\r\n\r\n def is_shutting_down(self):\r\n \"\"\"Whether teardown has begun, so late callbacks must do nothing.\r\n\r\n A worker's result is delivered on the GUI thread, which means it can\r\n land AFTER teardown started - and ``_on_template_meta_result`` does not\r\n merely update a label, it calls ``_refresh_preview_template``, which\r\n creates a QThread parented to this tab. Building children on a widget\r\n that is being destroyed aborts the process on Windows, with no Python\r\n traceback because the failure is in C++.\r\n\r\n The ``seq`` guards those handlers already carry cannot cover this: they\r\n reject a result from a SUPERSEDED request, and the result that crashes\r\n teardown is the current one.\r\n \"\"\"\r\n return bool(getattr(self, \"_shutting_down\", False))\r\n\r\n def _shutdown_workers(self, timeout_ms=10000):\r\n \"\"\"Stop every background worker thread before this tab is torn down.\r\n\r\n A QThread still running when its owner is destroyed aborts or hangs the\r\n process on Windows (\"QThread: Destroyed while thread is still running\").\r\n This tab spawns atlas metadata/preview loads at construction and on every\r\n selection: moveToThread()'d QThreads parented to the tab (the seven\r\n ``_*_threads`` lists) and QThread subclasses held only by the tab\r\n (``_bg_threads``). Either is destroyed with the tab, so quit and\r\n bounded-wait each running one first. isRunning() skips already-finished\r\n workers, so teardown is cheap in the common case where the load is done.\r\n \"\"\"\r\n from PySide6.QtCore import QThread\r\n\r\n # Announce teardown BEFORE draining, and the order is the whole point:\r\n # draining is what lets an in-flight result RUN, and that result starts\r\n # another worker (_on_template_meta_result -> _refresh_preview_template)\r\n # on a tab that is going away. The flag makes those handlers inert.\r\n self._shutting_down = True\r\n pair_lists = (\r\n \"_template_meta_threads\",\r\n \"_input_meta_threads\",\r\n \"_vsi_meta_threads\",\r\n \"_preview_template_threads\",\r\n \"_preview_input_threads\",\r\n \"_preview_annotation_threads\",\r\n \"_preview_result_threads\",\r\n )\r\n workers = []\r\n for name in pair_lists:\r\n for pair in list(getattr(self, name, None) or []):\r\n if pair:\r\n workers.append(pair[0])\r\n workers.extend(list(getattr(self, \"_bg_threads\", None) or []))\r\n # ``run_async`` (regtools.gui.worker) keeps its (thread, task) pairs in\r\n # ``_async_jobs`` rather than in any of the lists above, so a job\r\n # started through it was invisible here. The hemisphere fit is the\r\n # first user of it on this tab AND the longest-running thing the tab\r\n # ever starts - minutes, against seconds for a metadata load - which\r\n # makes it far and away the most likely to still be running when the\r\n # user closes the window. Leaving it out aborts the process on Windows\r\n # exactly as this method's docstring describes.\r\n for pair in list(getattr(self, \"_async_jobs\", None) or []):\r\n if pair:\r\n workers.append(pair[0])\r\n for t in workers:\r\n try:\r\n if isinstance(t, QThread) and t.isRunning():\r\n t.quit() # no-op for run()-override threads\r\n t.wait(timeout_ms) # blocks until run()/task returns\r\n except RuntimeError:\r\n pass # C++ object already gone\r\n\r\n def closeEvent(self, event):\r\n \"\"\"Drain background workers so none is running when Qt destroys this\r\n tab's child QThreads (a native crash/hang on Windows otherwise).\"\"\"\r\n self._shutdown_workers()\r\n super().closeEvent(event)\r\n\r\n def _collect_h5ad_mappings(self, file_paths):\r\n \"\"\"Ensure every .h5ad in *file_paths* has a coordinate field mapping.\r\n\r\n Opens :class:`H5adFieldDialog` for each .h5ad that lacks one; drops any\r\n the user cancels or under-specifies, since an unmapped .h5ad would raise\r\n at run time. Returns the (possibly shorter) list to actually use.\r\n \"\"\"\r\n if not hasattr(self, \"_h5ad_mappings\"):\r\n self._h5ad_mappings = {}\r\n kept = []\r\n for p in file_paths:\r\n if os.path.splitext(p)[1].lower() != \".h5ad\":\r\n kept.append(p)\r\n continue\r\n if p in self._h5ad_mappings:\r\n kept.append(p)\r\n continue\r\n try:\r\n dlg = H5adFieldDialog(self, p)\r\n except Exception as exc:\r\n QMessageBox.warning(self, \"h5ad\", f\"Could not read fields from {os.path.basename(p)}:\\n{exc}\")\r\n continue\r\n if dlg.exec() and dlg.mapping is not None:\r\n self._h5ad_mappings[p] = dlg.mapping\r\n kept.append(p)\r\n else:\r\n QMessageBox.information(self, \"h5ad\", f\"Skipped {os.path.basename(p)}: no coordinate mapping set.\")\r\n # Forget mappings for files no longer selected.\r\n self._h5ad_mappings = {k: v for k, v in self._h5ad_mappings.items() if k in kept}\r\n return kept\r\n\r\n def _process_points_paths(self, file_paths):\r\n \"\"\"Process list of points file paths (shared by click and drop handlers).\"\"\"\r\n file_paths = self._collect_h5ad_mappings(file_paths)\r\n self.input_points_list = file_paths\r\n\r\n if len(file_paths) == 0:\r\n self.input_points_button.setText(\"Select Input Points Files (Optional)\\nNo cell points loaded\")\r\n tag_themed(self.input_points_button, \"file\")\r\n elif len(file_paths) == 1:\r\n self.input_points_button.setText(f\"Select Input Points Files (Optional)\\n{os.path.basename(file_paths[0])}\")\r\n tag_themed(self.input_points_button, \"file_success\")\r\n else:\r\n # Multiple files selected\r\n names = [os.path.basename(f) for f in file_paths]\r\n display_text = \", \".join(names[:2])\r\n if len(names) > 2:\r\n display_text += f\" (+{len(names) - 2} more)\"\r\n self.input_points_button.setText(\r\n f\"Select Input Points Files (Optional)\\n{len(file_paths)} files: {display_text}\"\r\n )\r\n tag_themed(self.input_points_button, \"file_success\")\r\n\r\n print(f\"Selected {len(file_paths)} input points file(s): {file_paths}\")\r\n self._update_preview_points()\r\n\r\n def _on_orient_points_toggled(self, checked):\r\n \"\"\"'Apply orientation to points' - store it (fed into RegistrationOpts on\r\n run) and mirror it to the panel so the preview overlay redraws oriented\r\n (aligned) vs raw (misaligned).\"\"\"\r\n self._orient_points = bool(checked)\r\n panel = getattr(self, \"preview_panel\", None)\r\n if panel is not None:\r\n panel.set_orient_points(self._orient_points)\r\n\r\n @staticmethod\r\n def _points_file_for_input(points_list, input_path):\r\n \"\"\"The points file that pairs with the input the pane is showing.\r\n\r\n With several sections and several point files, plotting ``points[0]`` on\r\n whichever section is previewed silently overlays the wrong cells. Point\r\n files are conventionally named after their section\r\n (``detected_points_<section>.csv``, ``<section>_points.csv``), so match on\r\n the stems and fall back to the first file when nothing matches (which is\r\n the previous behaviour, and correct for the common single-file case).\r\n \"\"\"\r\n if not points_list:\r\n return None\r\n if len(points_list) == 1 or not input_path:\r\n return points_list[0]\r\n stem = os.path.splitext(os.path.basename(str(input_path)))[0].lower()\r\n if stem:\r\n for cand in points_list:\r\n c = os.path.splitext(os.path.basename(str(cand)))[0].lower()\r\n if c == stem or stem in c or c in stem:\r\n return cand\r\n return points_list[0]\r\n\r\n def _update_preview_points(self):\r\n \"\"\"Load the previewed section's (row, col) coords and hand them to the\r\n preview panel's overlay. Subsampled so a huge cell file can't stall the\r\n UI. The overlay itself is a section feature (gated inside the panel).\"\"\"\r\n panel = getattr(self, \"preview_panel\", None)\r\n if panel is None:\r\n return\r\n if not self.input_points_list:\r\n panel.set_input_points(None)\r\n return\r\n try:\r\n from regtools.utils.io.points_io import load_points_file\r\n\r\n _pts_path = self._points_file_for_input(\r\n self.input_points_list, getattr(self, \"_previewed_input_path\", None)\r\n )\r\n _mapping = getattr(self, \"_h5ad_mappings\", {}).get(_pts_path)\r\n raw = np.asarray(load_points_file(_pts_path, mapping=_mapping))\r\n pts = raw[:, 1:3] if raw.ndim == 2 and raw.shape[1] >= 3 else raw[:, :2]\r\n _CAP = 20000 # cap drawn points for responsiveness\r\n if len(pts) > _CAP:\r\n pts = pts[:: int(np.ceil(len(pts) / _CAP))]\r\n panel.set_input_points(pts)\r\n except Exception as e:\r\n print(f\"Could not load points for preview overlay: {e}\")\r\n panel.set_input_points(None)\r\n\r\n def _process_template_path(self, file_path, um_per_px=None):\r\n \"\"\"Process template path (shared by click and drop handlers). Supports NIfTI, NRRD, TIFF.\r\n\r\n ``um_per_px`` is the optional custom-atlas in-plane pixel-size override\r\n (µm/px); forwarded to the off-thread loader so a spacing-less image-format\r\n atlas shows its true spacing in the preview subtitle (matching what the\r\n registration run uses via --fixed_um_per_px). None for built-in atlases\r\n and header-bearing formats.\r\n\r\n The header/data read happens off the UI thread (``_TemplateMetadataWorker``)\r\n so selecting a template on a slow/network share or a large NRRD/TIFF never\r\n freezes the window. All post-load UI work lives in the handlers\r\n ``_on_template_meta_result`` / ``_on_template_meta_error``; a failed/aborted\r\n load leaves the previous ``self.template_path`` unchanged.\r\n\r\n The worker runs through ``_start_worker``, which relays the result/error\r\n onto the GUI thread (a bare closure on a moveToThread'd sender would run in\r\n the worker thread, where the post-load block - which creates a QThread via\r\n ``_refresh_preview_template`` and touches widgets - would emit \"Cannot\r\n create children for a parent in a different thread\" and race widget\r\n teardown). Per-call context (seq/path/prior-label) is captured by the\r\n result/error closures below and passed to the handlers explicitly.\r\n \"\"\"\r\n # Show a loading state immediately and invalidate the cache. Do NOT set\r\n # self.template_path yet - it's only set on a successful load. Remember the\r\n # prior label text so a failed load can restore it (the previous template,\r\n # if any, stays valid).\r\n _prev_label = self.template_info_label.text()\r\n # A superseding selection may fire while an earlier \"Reading template...\" is\r\n # still showing; don't remember that transient text as the \"prior\" state.\r\n _loading_text = \"Reading template...\"\r\n if _prev_label == _loading_text:\r\n _prev_label = \"No template loaded\"\r\n self.template_info_label.setText(_loading_text)\r\n self._template_data_cache = None # Invalidate cache for new template\r\n self._template_preview_affine = None\r\n self._template_preview_full_shape = None\r\n # A fitted plane belongs to ONE image. This is the single funnel every\r\n # template change goes through (built-in and custom alike), so clearing\r\n # it here is what stops a plane fitted for the previous custom image\r\n # being pushed at the next one - its source_shape would not even match.\r\n self._custom_hemisphere_plane = None\r\n self._annotation_data_cache = None # new template -> reload its annotation\r\n # Block a run against the previous/stale template during the async load\r\n # window; the result/error handlers re-enable it based on final state.\r\n self.register_button.setEnabled(False)\r\n\r\n self._template_meta_seq += 1\r\n _seq = self._template_meta_seq\r\n\r\n def _on_result(nii):\r\n self._on_template_meta_result(nii, _seq, file_path)\r\n\r\n def _on_error(msg, exc_type=\"\"):\r\n self._on_template_meta_error(msg, exc_type, _seq, file_path, _prev_label)\r\n\r\n self._start_worker(\r\n _TemplateMetadataWorker(file_path, um_per_px), self._template_meta_threads, _on_result, _on_error\r\n )\r\n\r\n def _on_template_meta_result(self, nii, seq, file_path):\r\n \"\"\"Main-thread handler for a successful off-thread template load.\r\n\r\n Runs on the GUI thread (relayed by ``_start_worker``). Per-call context\r\n (``seq``, ``file_path``) is captured by the calling closure rather than\r\n read off the task; a stale result is dropped by the seq guard.\r\n \"\"\"\r\n # Teardown wins over the seq guard: seq rejects a SUPERSEDED request,\r\n # but the result that crashes teardown is the current one. Everything\r\n # below touches widgets, and the tail starts another QThread.\r\n if self.is_shutting_down():\r\n return\r\n if seq != self._template_meta_seq:\r\n return\r\n\r\n self.template = nii\r\n self.template_path = file_path # set ONLY on a successful load (commit 6b626dc)\r\n\r\n if self.input_path and self.template_path:\r\n self.register_button.setEnabled(True)\r\n\r\n print(f\"Selected template path: {file_path}\")\r\n self._template_data_cache = None # Invalidate cache for new template\r\n self._template_preview_affine = None\r\n self._template_preview_full_shape = None\r\n self._annotation_data_cache = None # new template -> reload its annotation\r\n\r\n # Detect whether the template is a 2D image or a 3D atlas volume\r\n shape = self.template.header.get_data_shape()\r\n self._template_is_2d = len(shape) <= 2\r\n\r\n # Captu"}}},{"rowIdx":570455,"cells":{"sha256":{"kind":"string","value":"cef8d9536e2ce7cfee012f39d0c71dd0d9c3d17eff802300323cd634879425d7"},"language":{"kind":"string","value":"Python"},"size":{"kind":"number","value":206539,"string":"206,539"},"lines":{"kind":"number","value":8600,"string":"8,600"},"content":{"kind":"string","value":"# This file is automatically generated by tools/idna-data\n# vim: set fileencoding=utf-8 :\n\nfrom typing import List, Tuple, Union\n\n\n\"\"\"IDNA Mapping Table from UTS46.\"\"\"\n\n\n__version__ = '15.0.0'\ndef _seg_0() -> List[Union[Tuple[int, str], Tuple[int, str, str]]]:\n return [\n (0x0, '3'),\n (0x1, '3'),\n (0x2, '3'),\n (0x3, '3'),\n (0x4, '3'),\n (0x5, '3'),\n (0x6, '3'),\n (0x7, '3'),\n (0x8, '3'),\n (0x9, '3'),\n (0xA, '3'),\n (0xB, '3'),\n (0xC, '3'),\n (0xD, '3'),\n (0xE, '3'),\n (0xF, '3'),\n (0x10, '3'),\n (0x11, '3'),\n (0x12, '3'),\n (0x13, '3'),\n (0x14, '3'),\n (0x15, '3'),\n (0x16, '3'),\n (0x17, '3'),\n (0x18, '3'),\n (0x19, '3'),\n 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(0x2F9F0, 'M', '閷'),\n (0x2F9F1, 'M', '𨵷'),\n (0x2F9F2, 'M', '䧦'),\n (0x2F9F3, 'M', '雃'),\n (0x2F9F4, 'M', '嶲'),\n (0x2F9F5, 'M', '霣'),\n (0x2F9F6, 'M', '𩅅'),\n (0x2F9F7, 'M', '𩈚'),\n (0x2F9F8, 'M', '䩮'),\n (0x2F9F9, 'M', '䩶'),\n (0x2F9FA, 'M', '韠'),\n (0x2F9FB, 'M', '𩐊'),\n (0x2F9FC, 'M', '䪲'),\n (0x2F9FD, 'M', '𩒖'),\n (0x2F9FE, 'M', '頋'),\n (0x2FA00, 'M', '頩'),\n (0x2FA01, 'M', '𩖶'),\n (0x2FA02, 'M', '飢'),\n (0x2FA03, 'M', '䬳'),\n (0x2FA04, 'M', '餩'),\n (0x2FA05, 'M', '馧'),\n (0x2FA06, 'M', '駂'),\n (0x2FA07, 'M', '駾'),\n (0x2FA08, 'M', '䯎'),\n (0x2FA09, 'M', '𩬰'),\n (0x2FA0A, 'M', '鬒'),\n (0x2FA0B, 'M', '鱀'),\n (0x2FA0C, 'M', '鳽'),\n (0x2FA0D, 'M', '䳎'),\n (0x2FA0E, 'M', '䳭'),\n (0x2FA0F, 'M', '鵧'),\n (0x2FA10, 'M', '𪃎'),\n (0x2FA11, 'M', '䳸'),\n (0x2FA12, 'M', '𪄅'),\n (0x2FA13, 'M', '𪈎'),\n (0x2FA14, 'M', '𪊑'),\n (0x2FA15, 'M', '麻'),\n (0x2FA16, 'M', '䵖'),\n (0x2FA17, 'M', '黹'),\n (0x2FA18, 'M', '黾'),\n (0x2FA19, 'M', '鼅'),\n (0x2FA1A, 'M', '鼏'),\n (0x2FA1B, 'M', '鼖'),\n (0x2FA1C, 'M', '鼻'),\n (0x2FA1D, 'M', '𪘀'),\n (0x2FA1E, 'X'),\n (0x30000, 'V'),\n (0x3134B, 'X'),\n (0x31350, 'V'),\n (0x323B0, 'X'),\n (0xE0100, 'I'),\n (0xE01F0, 'X'),\n ]\n\nuts46data = tuple(\n _seg_0()\n + _seg_1()\n + _seg_2()\n + _seg_3()\n + _seg_4()\n + _seg_5()\n + _seg_6()\n + _seg_7()\n + _seg_8()\n + _seg_9()\n + _seg_10()\n + _seg_11()\n + _seg_12()\n + _seg_13()\n + _seg_14()\n + _seg_15()\n + _seg_16()\n + _seg_17()\n + _seg_18()\n + _seg_19()\n + _seg_20()\n + _seg_21()\n + _seg_22()\n + _seg_23()\n + _seg_24()\n + _seg_25()\n + _seg_26()\n + _seg_27()\n + _seg_28()\n + _seg_29()\n + _seg_30()\n + _seg_31()\n + _seg_32()\n + _seg_33()\n + _seg_34()\n + _seg_35()\n + _seg_36()\n + _seg_37()\n + _seg_38()\n + _seg_39()\n + _seg_40()\n + _seg_41()\n + _seg_42()\n + _seg_43()\n + _seg_44()\n + _seg_45()\n + _seg_46()\n + _seg_47()\n + _seg_48()\n + _seg_49()\n + _seg_50()\n + _seg_51()\n + _seg_52()\n + _seg_53()\n + _seg_54()\n + _seg_55()\n + _seg_56()\n + _seg_57()\n + _seg_58()\n + _seg_59()\n + _seg_60()\n + _seg_61()\n + _seg_62()\n + _seg_63()\n + _seg_64()\n + _seg_65()\n + _seg_66()\n + _seg_67()\n + _seg_68()\n + _seg_69()\n + _seg_70()\n + _seg_71()\n + _seg_72()\n + _seg_73()\n + _seg_74()\n + _seg_75()\n + _seg_76()\n + _seg_77()\n + _seg_78()\n + _seg_79()\n + _seg_80()\n + _seg_81()\n) # type: Tuple[Union[Tuple[int, str], Tuple[int, str, str]], ...]\n"}}},{"rowIdx":570456,"cells":{"sha256":{"kind":"string","value":"cd65d2b3e068f7d79ba9f75bb31659de4b4c89b2b13a1bcbda6a5258d87f4716"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":177,"string":"177"},"lines":{"kind":"number","value":12,"string":"12"},"content":{"kind":"string","value":"---\ntitle: \"Archive\"\npage-layout: full\nlisting:\n contents: posts/\n type: default\n fields: [date, title]\n sort: 'date desc'\n feed: \n items: 5\n categories: [python]\n---"}}},{"rowIdx":570457,"cells":{"sha256":{"kind":"string","value":"67c8ff40c72ff254bb07fea31b297f1f6e23213337a2bbac9d5efac20ea8fdd1"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":299,"string":"299"},"lines":{"kind":"number","value":18,"string":"18"},"content":{"kind":"string","value":"---\ntitle: \"Home\"\nlisting:\n contents: posts\n sort: \"date desc\"\n type: default\n categories: true\n sort-ui: false\n filter-ui: false\n fields: [date, title, description, categories, image]\n # feed: true\n # items: 3\n # categories: [python]\npage-layout: full\ntitle-block-banner: false\n---\n\n\n"}}},{"rowIdx":570458,"cells":{"sha256":{"kind":"string","value":"64c346e5354b78e9fa79c8bf4c08923a086cecf557fe492c66e87a937327fa75"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":321,"string":"321"},"lines":{"kind":"number","value":22,"string":"22"},"content":{"kind":"string","value":"---\nfilters:\n - interlinks.lua\ninterlinks:\n autolink: true\n aliases:\n quartodoc: null\n #sources:\n # test:\n # url: https://example.com\n---\n\n* `some_func`\n* `some_func()`\n* `some_func(a=1)`\n* `some_func()`{.qd-no-link}\n* `some_func + some_func`\n* `a.b.c`\n* `~a.b.c`\n* `a.b.c()`\n* `quartodoc.Auto()`\n* `Auto()`\n"}}},{"rowIdx":570459,"cells":{"sha256":{"kind":"string","value":"8b09534724ab8870c3bc24fc80d0ec449a352a2fc5d141a422cfe2ae90c34083"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":322,"string":"322"},"lines":{"kind":"number","value":11,"string":"11"},"content":{"kind":"string","value":"\n---\ntitle: Tutorials\ntoc: true\ntoc-depth: 3\nfig-align: center\n---\n\nThere are some written tutorials for different aspects of the process.\n\nThey are _slightly_ outdated, as maintaining large amounts of documentation takes a lot of effort. Contributions on more documentation from community members are always appreciated!\n"}}},{"rowIdx":570460,"cells":{"sha256":{"kind":"string","value":"662add43f09dd2e6f1c411aa2e7d0a47621402b06fd2eac8bd51fb1493acd468"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":337,"string":"337"},"lines":{"kind":"number","value":21,"string":"21"},"content":{"kind":"string","value":"---\ntitle: Examples\nlisting:\n id: listing-examples\n contents: examples.yml\n type: grid\n sort: date desc\n sort-ui: false\n grid-columns: 2\n filter-ui: true\n # template: template.ejs\n\n# css: styles.css\n\n\n---\n\nSome examples of projects that have used Molecular Nodes in their production.\n\n::: {#listing-examples}\n:::\n"}}},{"rowIdx":570461,"cells":{"sha256":{"kind":"string","value":"7349d2df10f19cc86a977f045149b652ebafc7099babf4abd36f76f6f250fc89"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":491,"string":"491"},"lines":{"kind":"number","value":15,"string":"15"},"content":{"kind":"string","value":"---\ntitle: Citing Molecular Nodes\nlisting:\n id: listing-citations\n contents: citations.yml\n type: grid\n grid-columns: 3\n---\n\nA paper for Molecular Nodes has not yet been published, but you can cite the Zenodo entry for the GitHub repo here: [](https://zenodo.org/badge/latestdoi/485261976)\n\nA selection of academic works that use Molecular Nodes in producing some of their figures or supplimentary movies.\n\n::: {#listing-citations}\n:::\n"}}},{"rowIdx":570462,"cells":{"sha256":{"kind":"string","value":"52ca1e90aa19c18e099b66098f465617bf5431a18945d76391db46a9a8aa154e"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":919,"string":"919"},"lines":{"kind":"number","value":22,"string":"22"},"content":{"kind":"string","value":"---\ntitle: Nodes\ndescription: The node groups that ship with Molecular Nodes\ntoc: false\n---\n\nMolecular Nodes is built on top of Blender's Geometry Nodes. Everything you see\nin the viewport — styles, colours, selections, animations — is created by\nchaining together the node groups documented in this section.\n\nInside the Geometry Nodes editor, add them from the *Add* menu\n({{< kbd Shift-A >}}), where they appear grouped in the same categories as the\nsidebar on the left. Each category page documents every node in it: what the\nnode does, a short video of it in use, and its input and output sockets.\n\n## Scripting\n\nEvery node group is also available as a typed Python class, so anything built\nin the GUI can be replicated from a script. Each node's entry links to the\nclass in the [API reference](/api/reference/index.qmd), and the\n[Node Trees](/api/nodebpy.qmd) guide covers how to build node trees from\nPython.\n"}}},{"rowIdx":570463,"cells":{"sha256":{"kind":"string","value":"3a8ec439019272f65c604392104c9974f3ec3b115dbc427a6c6f831394589ac0"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":1025,"string":"1,025"},"lines":{"kind":"number","value":52,"string":"52"},"content":{"kind":"string","value":"---\ntitle: \"Session Info, packages and citat\"\nauthor: \"Eve Zeyl Fiskebeck\"\ndate: \"`r format(Sys.time(), '%d %B, %Y')`\"\nparams:\n \n source_code_dir: 'C:/Users/vi2067/OneDrive - Veterinaerinstituttet/REINSTALLED/projects_vault/Projects/2023/1_AvianInfluenza/12302_Avian_influenza_2023/code/R'\nexecute: \n echo: false\n cache: false\nformat: html\nknitr:\n opts_chunk: \n collapse: true\n comment: \"#>\" \n R.options:\n knitr.graphics.auto_pdf: true\neditor_options: \n chunk_output_type: console\n---\n\n\n# sourcing scripts and functions - used in the paper\n\n\n```{r setup}\n\nlibrary(here)\nsource(here::here(params$source_code, \"AI_main.R\"))\n\n# Provisional until we get a package\nfun_files_path <- list.files(here::here(params$source_code, \"fun\"),\n pattern = \"*.R\",\n full.names = T)\n# sourcing all functions\nsapply(fun_files_path,source,.GlobalEnv)\n\n```\n\nOther libraries used\n```{r}\nlibrary(readxl)\nlibrary(plyr)\n```\n\nLibraries used in the Rscript for wrangling the data \n```{r}\nlibrary(optparse)\nlibrary(seqinr)\n```\n\n\n"}}},{"rowIdx":570464,"cells":{"sha256":{"kind":"string","value":"f5564c01952649b4ec046c3ca5045e865cce910ca541a204aa81f242bc657a71"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":1083,"string":"1,083"},"lines":{"kind":"number","value":56,"string":"56"},"content":{"kind":"string","value":"---\ntitle: \"SEP Phylog tree packages\"\nauthor: \"Eve Zeyl Fiskebeck\"\ndate: \"`r format(Sys.time(), '%d %B, %Y')`\"\nparams:\n source_code: 'code/R'\nexecute: \n echo: false\n cache: false\nformat: html\nknitr:\n opts_chunk: \n collapse: true\n comment: \"#>\" \n R.options:\n knitr.graphics.auto_pdf: true\neditor_options: \n chunk_output_type: console\n---\n\n\n```{r setup}\n#| include: false\n\npackages_phylog <- \n c(\"base\", \"here\", \"tidyverse\", \"rlist\", \"xlsx\", \"glue\", \"ggplot2\", \"ggnewscale\", \n \"scales\", \"qualpalr\", \"RColorBrewer\", \"ggplotify\", \"ggforce\", \"phytools\", \n \"rlang\", \"ape\", \"ggtree\", \"ggtreeExtra\")\n\n```\n\nOther packages prep files - for scripts \n... and I am probably forgetting some \n\n```{r}\n#| include: false\npackages_other <- c(\"readxl\", \"plyr\", \n \"seqinr\", \"optparse\")\n```\n\nBlog: interesting : \nhttps://ropensci.org/blog/2021/11/16/how-to-cite-r-and-r-packages/\n\nCitatations for packages used ... \n\nAll other\n```{r}\nsink(\"citations_incl_phylog.txt\")\nfor (pkg in c(packages_phylog, packages_other)) {\n print(citation(pkg))\n}\nsink()\n```\n\n\n\n"}}},{"rowIdx":570465,"cells":{"sha256":{"kind":"string","value":"a6dd2f634b2f0fd1edc49e24be6b246b17674db122ac9f482d6edf8acc09b73f"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":1112,"string":"1,112"},"lines":{"kind":"number","value":53,"string":"53"},"content":{"kind":"string","value":"---\ntitle: \"Tree Data check: included in Tree\"\nauthor: \"Eve Zeyl Fiskebeck\"\ndate: \"`r format(Sys.time(), '%d %B, %Y')`\"\nparams:\n alltree_metadata_rds: \"data/prepared_data/all_tree_metadata_CAB.RDS\"\n tree: \"results/H5N1_HA/phylog/20240104_HA_H5N1_MSA_filtered_nodub.fasta.contree\"\n\nexecute: \n echo: false\n cache: true\nformat: html\nknitr:\n opts_chunk: \n collapse: true\n comment: \"#>\" \n R.options:\n knitr.graphics.auto_pdf: true\neditor_options: \n chunk_output_type: console\n---\n\n\n```{r setup}\nlibrary(here)\nlibrary(tidyverse)\nlibrary(ape)\n```\n\n\n# Data check: isolates included in the phylogenetic tree \n\n- getting the tree\n```{r}\ntree <- ape::read.tree(here(params$tree))\ntree$tip.label <- tree$tip.label %>% str_remove_all(., \".*\\\\|\")\nlength(tree$tip.label)\n```\n\n- getting the metadata\n```{r}\nmetadata <- readRDS(here(params$alltree_metadata_rds))\n```\n\n- checking what isolates were used in the tree \n```{r}\ntibble(\"label\" = tree$tip.label) %>%\n left_join(metadata %>%\n select(label, subtype), by = c(\"label\" = \"label\")) %>%\n group_by(subtype) %>%\n summarise(n = n()) \n```\n\n"}}},{"rowIdx":570466,"cells":{"sha256":{"kind":"string","value":"b95b230d336b97206c4d69239235e8f96bd0f14a1c2681a0756ec8d4eb07cf5c"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":1200,"string":"1,200"},"lines":{"kind":"number","value":29,"string":"29"},"content":{"kind":"string","value":"---\ntitle: \"About\"\nimage: profile.jpg\nabout:\n template: jolla\n links:\n - icon: twitter\n text: Twitter\n href: https://twitter.com/mcbwrs\n - icon: linkedin\n text: LinkedIn\n href: https://linkedin.com/in/matt-bowers/\n - icon: github\n text: Github\n href: https://github.com/mcb00\n\n---\n\n\nWelcome to Random Realizations!\nThis blog is a celebration of the fascinating world of data science.\nI hope you find the content useful, and I hope you enjoy reading along and learning with me!\n\nAnd me? Well, I'm Matt Bowers.\nI'm an ex data scientist at Uber where I solved problems using statistical modeling and machine learning. \nOver the years I've driven product decisions through controlled experimentation and user analytics, built ML products like ETA prediction on large-scale telematics data, and cracked long-standing ML problems in marketplace pricing.\nBefore Uber I earned an MS in Applied Statistics and a PhD in Atmospheric Sciences at Purdue University where I studied the spatial-temporal correlation structure of tropical convection systems and developed statistical methods for climate research.\n\nBut enough about me. Let's get down with some data science!\n"}}},{"rowIdx":570467,"cells":{"sha256":{"kind":"string","value":"d678a1d7bb015ae3553d60f8671d22976819a4ac11794fdfa072b0e4c1d8adaa"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":1206,"string":"1,206"},"lines":{"kind":"number","value":48,"string":"48"},"content":{"kind":"string","value":"---\ntitle: \"Update tree metadata\" \nauthor: \"Eve Zeyl Fiskebeck\"\ndate: \"`r format(Sys.time(), '%d %B, %Y')`\"\nparams:\n save_dir: \"data/prepared_data/\"\n all_tree_metadata: \"all_tree_metadata_CAB.RDS\"\n new_metadata: \"all_tree_metadata_CAB_2023-03-07.xlsx\"\nexecute: \n echo: false\n cache: false\nformat: html\nknitr:\n opts_chunk: \n collapse: true\n comment: \"#>\" \n R.options:\n knitr.graphics.auto_pdf: true\neditor_options: \n chunk_output_type: console\n---\n\n```{r setup}\nlibrary(here)\nlibrary(tidyverse)\nlibrary(xlsx)\n```\n\n\n```{r}\nall_tree_metadata <- xlsx::read.xlsx(\n file = here(params$save_dir, params$new_metadata),\n sheetName = \"all_tree_metadata\") %>%\n dplyr::mutate_at(vars(Locality_code, lon, lat, artkode, efsakode), \n ~as.numeric(.x)) %>%\n dplyr::mutate_at(c(\"year\", \"month\", \"day\"), ~as.integer(.x)) %>%\n # changing last column name - to match (easier that to correct everything)\n dplyr::select(-Host_latin) %>%\n dplyr::rename(Host_latin = Host_latinsk_navn) %>%\n # to remove the duplicates\n dplyr::distinct()\n\n# exporting so we can use as RDS \nsaveRDS(all_tree_metadata,\n file = here(params$save_dir, params$all_tree_metadata)\n )\n```\n\n"}}},{"rowIdx":570468,"cells":{"sha256":{"kind":"string","value":"b16426a2ce6ff247e5a8adde33f92637a5a2ace63404308c3742fc72754cf39e"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":1356,"string":"1,356"},"lines":{"kind":"number","value":30,"string":"30"},"content":{"kind":"string","value":"---\ntitle: \"Gradient Boosting\"\nlisting:\n contents:\n - posts/gradient-boosting-machine-from-scratch\n - posts/get-down-with-gradient-descent\n - posts/how-gradient-boosting-does-gradient-descent\n - posts/gradient-boosting-machine-with-any-loss-function\n - posts/consider-the-decision-tree\n - posts/decision-tree-from-scratch\n - posts/xgboost-explained\n - posts/xgboost-from-scratch\n - posts/xgboost-for-regression-in-python\n - posts/gradient-boosting-multi-class-classification-from-scratch\n - posts/xgboost-for-classification-in-python\n \n sort: \"date\"\n type: default\n categories: true\n sort-ui: false\n filter-ui: false\n fields: [date, title, description, categories, image]\npage-layout: full\ntitle-block-banner: false\n---\n\nAhh, gradient boosting. In addition to having a totally kickass name, this family of machine learning algorithms is currently among the best known approaches for prediction problems on structured data. \nThis series of posts strives to give a comprehensive understanding of gradient boosting by providing intuitive mathematical explanations, from-scratch implementations of key algorithms, and examples of how to apply modern gradient boosting libraries to solve practical data science problems.\n\nI recommend reading through the series in order, since concepts tend to build on earlier ideas."}}},{"rowIdx":570469,"cells":{"sha256":{"kind":"string","value":"eb3d75b1f5e4b5b4c8ea8901ba08a19e635f3bf274319d69f5e9ce807f48891d"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":2395,"string":"2,395"},"lines":{"kind":"number","value":63,"string":"63"},"content":{"kind":"string","value":"---\ntitle: \"Electrostatics\"\ndescription: \"Colour a molecular surface by an electrostatic potential from APBS.\"\nexecute:\n daemon: false\n---\n\n[APBS](https://www.poissonboltzmann.org/) solves the Poisson-Boltzmann equation for a molecule in solvent and writes the electrostatic potential on a grid, usually as a `.dx` file. Its values are in kT/e: thermal energy per elementary charge, about 25.7 mV at 25 °C.\n\nThis example uses the potential of one barnase-barstar pair from [1BRS](https://www.rcsb.org/structure/1BRS) (barnase chain A, barstar chain D), made with pdb2pqr and APBS by `docs/dev/apbs_1brs.py`. Barnase is positively charged and barstar negatively charged.\n\n```{python}\nimport molecularnodes as mn\nfrom molecularnodes.nodes import geometry as mg\n\ncanvas = mn.Canvas()\n```\n\n## Load the potential\n\nLoad the grid as a density and show it with `Density Style ISO Surface`, which meshes the potential at +1 kT/e (blue) and -1 kT/e (red).\n\n```{python}\nesp = mn.entities.density.Grids.load(\"../../tests/data/1brs_AD_apbs.dx.gz\")\n\nwith esp.tree.reset(input=\"Volume\") as (volume, join):\n (\n volume\n >> mg.DensityStyleISOSurface(\n threshold=1.0,\n positive=(0.1, 0.3, 1.0, 1.0),\n negative=(1.0, 0.1, 0.1, 1.0),\n material=mn.material.Transparent(\n transparency=0.7, fresnel=True, outline_color=(0.85, 0.85, 0.85, 1.0)\n ).material,\n )\n >> join\n )\n```\n\n## Colour the surface\n\nPassing the density as `color` samples the potential at every vertex of the surface, from -5 kT/e (red) to 5 kT/e (blue). The density keeps its source volume in the `MN/Volume` bundle of its styled output, so it can be sampled while it shows its own style. The coordinates of 1BRS line up with the grid because pdb2pqr keeps the input coordinates.\n\n```{python}\nmol = mn.Molecule.fetch(\"1BRS\")\nmol.add_style(\"surface\", selection=\"chainID A D\", color=esp)\n```\n\n```{python}\ncanvas.look_at(esp, viewpoint=(106.5, 0.0, -37.7))\ncanvas.snapshot()\n```\n\nHide the potential's own style to see only the coloured surface. Barnase (left) is mostly positive and barstar (right) mostly negative.\n\n```{python}\nesp.object.hide_render = True\ncanvas.look_at(mol, viewpoint=(106.5, 0.0, -37.7))\ncanvas.snapshot()\n```\n\nThe colour range is set on the `Color Sample Grid` node in the molecule's tree, through its `Min` and `Max` inputs.\n"}}},{"rowIdx":570470,"cells":{"sha256":{"kind":"string","value":"98594e793a0625798c67b0d4b16034843c27b4e813cf760ee71a0045e5f707c4"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":2531,"string":"2,531"},"lines":{"kind":"number","value":131,"string":"131"},"content":{"kind":"string","value":"---\ntoc: true\ntoc-depth: 3\nfig-align: center\n---\n# Data Tables\n\nThe different lookup tables that are used to conver strings to integers in Molecular Nodes.\n\nCode for this can be found on the [GitHub Page](https://github.com/BradyAJohnston/MolecularNodes/blob/main/molecularnodes/assets/data.py)\n\n### Residue Names\n\n| Name | Integer |\n|----------:|:------------|\n| UNK | `-1::Int` |\n| ALA | `0::Int` |\n| ARG | `1::Int` |\n| ASN | `2::Int` |\n| ASP | `3::Int` |\n| CYS | `4::Int` |\n| GLU | `5::Int` |\n| GLN | `6::Int` |\n| GLY | `7::Int` |\n| HIS | `8::Int` |\n| ILE | `9::Int` |\n| LEU | `10::Int` |\n| LYS | `11::Int` |\n| MET | `12::Int` |\n| PHE | `13::Int` |\n| PRO | `14::Int` |\n| SER | `15::Int` |\n| THR | `16::Int` |\n| TRP | `17::Int` |\n| TYR | `18::Int` |\n| VAL | `19::Int` |\n| SNC | `15::Int` |\n| MSE | `12::Int` |\n| ASH | `3::Int` |\n| CYM | `4::Int` |\n| CYX | `4::Int` |\n| GLH | `5::Int` |\n| HID | `8::Int` |\n| HIE | `8::Int` |\n| HIP | `8::Int` |\n| HYP | `8::Int` |\n| LYN | `11::Int` |\n| DA | `30::Int` |\n| DC | `31::Int` |\n| DG | `32::Int` |\n| DT | `33::Int` |\n| PST | `33::Int` |\n| A | `40::Int` |\n| C | `41::Int` |\n| G | `42::Int` |\n| U | `43::Int` |\n| T | `43::Int` |\n\n\n### Atom Names\n\n| Name | Integer |\n|----------:|:------------|\n| N | `1::Int` |\n| CA | `2::Int` |\n| C | `3::Int` |\n| O | `4::Int` |\n| CB | `5::Int` |\n| CG | `6::Int` |\n| CG1 | `7::Int` |\n| CG2 | `8::Int` |\n| OG | `9::Int` |\n| OG1 | `10::Int` |\n| SG | `11::Int` |\n| CD | `12::Int` |\n| CD1 | `13::Int` |\n| CD2 | `14::Int` |\n| ND1 | `15::Int` |\n| ND2 | `16::Int` |\n| OD1 | `17::Int` |\n| OD2 | `18::Int` |\n| SD | `19::Int` |\n| CE | `20::Int` |\n| CE1 | `21::Int` |\n| CE2 | `23::Int` |\n| CE3 | `24::Int` |\n| NE | `25::Int` |\n| NE1 | `26::Int` |\n| NE2 | `27::Int` |\n| OE1 | `28::Int` |\n| OE2 | `29::Int` |\n| CH2 | `30::Int` |\n| NH1 | `31::Int` |\n| NH2 | `32::Int` |\n| OH | `33::Int` |\n| CZ | `34::Int` |\n| CZ2 | `35::Int` |\n| CZ3 | `36::Int` |\n| NZ | `37::Int` |\n| OXT | `38::Int` |\n| P | `50::Int` |\n| O1P | `51::Int` |\n| OP1 | `51::Int` |\n| OP2 | `52::Int` |\n| O2P | `52::Int` |\n| O5' | `53::Int` |\n| C5' | `54::Int` |\n| C4' | `55::Int` |\n| O4' | `56::Int` |\n| C3' | `57::Int` |\n| O3' | `58::Int` |\n| C2' | `59::Int` |\n| O2' | `60::Int` |\n| C1' | `61::Int` |\n| N1 | `62::Int` |\n| N9 | `63::Int` |\n| N3 | `64::Int` |\n| C8 | `65::Int` |\n| N7 | `66::Int` |\n| C5 | `67::Int` |\n| C6 | `68::Int` |\n| N6 | `69::Int` |\n| C2 | `70::Int` |\n| C4 | `71::Int` |\n| O6 | `72::Int` |\n| N2 | `73::Int` |\n| N4 | `74::Int` |\n| O2 | `75::Int` |\n| O4 | `76::Int` |\n| C7 | `77::Int` |\n\n"}}},{"rowIdx":570471,"cells":{"sha256":{"kind":"string","value":"73c5f770daea12793c2c259522c06671133ef8157c9db3aeedcd37ca643f41df"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":2554,"string":"2,554"},"lines":{"kind":"number","value":77,"string":"77"},"content":{"kind":"string","value":"---\ntitle: Small Molecules from PubChem\ndescription: \"Fetching 3D conformers of small molecules from PubChem\"\njupyter: python3\nexecute:\n daemon: false\n---\n\nSmall molecules can be fetched from [PubChem](https://pubchem.ncbi.nlm.nih.gov/) with\n[](`~mn.Molecule.fetch`) and `database=\"pubchem\"`. The code is either a compound CID or a\ncompound name, and the compound's 3D conformer is downloaded as an `.sdf` file, with its\nhydrogens and bonds. Like other downloads, the file is cached and reused on later fetches.\n\n## Setup\n\n```{python}\nimport molecularnodes as mn\n\ncanvas = mn.Canvas(mn.scene.Cycles(samples=32), resolution=(800, 600), transparent=True)\n```\n\n## Fetch by CID or Name\n\nA code made only of digits is a CID, anything else is looked up by name. Aspirin is CID\n`2244`, so both of these fetch the same structure.\n\n```{python}\naspirin = mn.Molecule.fetch(\"2244\", database=\"pubchem\")\naspirin.object.hide_render = True\naspirin = mn.Molecule.fetch(\"aspirin\", database=\"pubchem\")\naspirin.add_style(\"ball_and_stick\")\ncanvas.look_at(aspirin, viewpoint=\"pca\")\ncanvas.snapshot()\n```\n\n## Several Molecules\n\nEach fetched molecule is its own object, so it can be moved to sit beside the others in\na grid. Combine their views with `+` to frame all of them at once.\n\n```{python}\naspirin.object.hide_render = True\n\nnames = [\"caffeine\", \"dopamine\", \"ATP\", \"protoporphyrin IX\"]\nmols = []\nfor i, name in enumerate(names):\n mol = mn.Molecule.fetch(name, database=\"pubchem\")\n mol.add_style(\"sticks\" if name == \"ATP\" else \"ball_and_stick\")\n mol.object.location = ((i % 2) * 1.9, -(i // 2) * 1.3, 0)\n mols.append(mol)\n\nview = mols[0].get_view()\nfor mol in mols[1:]:\n view = view + mol.get_view()\ncanvas.look_at(view, viewpoint=\"top\")\ncanvas.snapshot()\n```\n\n## Compounds Without a 3D Conformer\n\nPubChem only computes 3D conformers for compounds that are a single molecule (not a\nsalt or mixture), have at most 50 non-hydrogen atoms and 15 rotatable bonds, contain\nonly H, C, N, O, F, Si, P, S, Cl, Br and I, use only atom types the MMFF94s force field\nrecognises, and have at most five undefined stereocentres\n([Kim et al. 2013](https://doi.org/10.1186/1758-2946-5-1)). For any other compound\nthe fetch raises an error rather than falling back to a flat 2D structure. Heme is one\nof them because of its iron, while its metal-free ring, protoporphyrin IX, has a 3D\nconformer.\n\n```{python}\nfrom molecularnodes.download import FileDownloadPDBError\n\ntry:\n mn.Molecule.fetch(\"heme\", database=\"pubchem\")\nexcept FileDownloadPDBError as e:\n print(e)\n```\n"}}},{"rowIdx":570472,"cells":{"sha256":{"kind":"string","value":"b94f8950f072209561cbc659a7e2b32ec96191cbcd3aad463fae0c32d2414853"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":2592,"string":"2,592"},"lines":{"kind":"number","value":69,"string":"69"},"content":{"kind":"string","value":"---\ntitle: \"README_WORKFLOW\"\ndate: \"`r format(Sys.time(), '%d %B, %Y')`\"\nauthor: \"Eve Zeyl Fiskebeck\"\n\nformat: html\neditor: visual\n\nparams:\n---\n\n# Workflow scripts\n\n1. Metadata and aknowledgments: `PRE_collate_raw_metata_aknowlegments.qmd`\n\n- collates metadata for isolates\n- link to previously collated metadata for all trees to get back the isolate name\n- collates aknowledgments for isolates\n\n2. Data check - included in the phylogenetic tree\n\n3. Metadata update - updates the metadata - when people add things manually, reformat and re-export\n\n- [x] UPDATE_metadata.qmd\n\n3. Visualisation phylogenetic tree:\n\n- [x] `SEP_H5N1_HA_phylogenetic_tree.qmd`\n- [x] `SEP_H5N1_HA_all_phylogenetic_tree.qmd` - rerun according to same model - with all isolates to be able to compare with NA - I think NA was best - This is the one used in the paper\n- [x] `PRERUN_SEP_H5N1_NA_phylogenetic_tree.qmd` - where I select what to filter out (mammals and eventually other isolates on the tree)\n- [x] `SEP_H5N1_NA_phylogenetic_tree.qmd`\n- [x] `SEP_H5N5_HA_phylogenetic_tree.qmd`\n- [x] `SEP_H5N5_NA_phylogenetic_tree.qmd`\n\n4. Packages used citat\n\n<!-- (had to re-install all R ! RRRrr ) -->\n\n``` \n- at revision : add renv and make lock file for package info and reproducibility\n```\n\n5. (last) Preparation metadata and acknowledgments for publication: `POST_preparation_aknowledgments_metadata_for_publication.qmd`\n\n# check list of what needs to be done before publication\n\n- [x] Metadata- info to include so they can be recovered\n\n- [x] all trees\n\n- [x] preparation files for MSA -\n\n- [x] removed sequences for H5N1 HA as explained\n\nAcknowledgements and formatting checks - \\[x\\] all trees - only for HA and NA segments - - \\[x\\] removed sequences for H5N1 HA - - \\[x\\] preparation files for MSA - \\[x\\] formatting\n\n# Diverse\n\n- [x] Open github repository with scripts before submission\n- [x] Add a licence - Ask Karin what type I should use so I can use that to make package afterwards\n- [x] clean dependencies that are absolutely necessary for the scripts to work (let the scales as it was used to create the scales) - might still be some relics there\n- [x] organize and move the prep scripts into the folder so its accessible\n\nRevision \n- [x] rerun with modifications \n- [x] added renv for reproducibility (lock file should allow to recreate env) \n- [ ] need to recreate zenodo and then upload - aslo upload on the journal zenodo\n- [ ] export the savedata/image and everything redone to project for archive (all git repo - code and data to a same place) \n\n"}}},{"rowIdx":570473,"cells":{"sha256":{"kind":"string","value":"773de5dc9ad714225f7763ec6a06dcaa5f5f65947698c6b872c675df339bf5c3"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":3021,"string":"3,021"},"lines":{"kind":"number","value":35,"string":"35"},"content":{"kind":"string","value":"---\ntitle: Random Realizations Resurrected\ndate: 2023-08-02\ndescription: The world's favorite data science blog is back.\ncategories: blogging\nimage: thumbnail.jpg\nimage-alt: Christ the Redeemer towers into an empty blue sky\ntoc: false\ndraft: false\ncomments:\n utterances:\n repo: mcb00/blog\n issue-term: title\n---\n\n![Christ the Redeemer towers into a vast blue Brazilian sky.!](main.jpg \"\")\n\n\nWell it's been over a year since I posted anything here. You see, a lot has been going on here at the Random Realizations Remote Global Headquarters that has distracted from producing the high-quality data science content that you're used to. Mostly I went on hiatus from work and started traveling, which turns out to be it's own full time job. I had aspirations of writing more after leaving work, but of course, after leaving, I couldn't be bothered to sit down at my laptop and type stuff about data science to yall. After all, life is bigger than that.\n\nWhen I finally felt like opening up my laptop, I was confronted with an email from the maintainers of [fastpages](https://github.com/fastai/fastpages), the open source content management system (CMS) I originally used to create this blog, notifying me that the project was being deprecated and that I would need to migrate my content to some other platform. \n\nBoo. \n\nThat didn't sound like much fun, so I spent another few months ignoring the blog. But eventually, dear reader, I decided it was time to roll up my sleeves and get this blog thriving once again.\n\nOk so fastpages was going to be deprecated, and I needed to find a new CMS. My requirements were pretty simple: I wanted to write the blog posts with jupyter notebook, and I wanted to host the site on my own domain. Helpfully, the former maintainers of fastpages recommended an alternative CMS called [Quarto](https://quarto.org/) which I had never heard of. Apparently I had been living under a rock because Quarto appears to be all the rage. Quarto's website says it's an open-source scientific and technical publishing system. I think it's fair to think of it as a way to render plain text or source code from languages like python, R, and julia into a variety of different published formats like websites, books, or journal articles. It was developed by the good folks over at RStudio, and the project has a pretty active following over on [github](https://github.com/quarto-dev), so I think it's less likely to suddenly disappear like fastpages.\n\nSo anyway, I've been migrating my content over into this new quarto universe. \n\nYou mayofficially consider this blog resurrected from the dead, because this is the first new post published after the migration. The site has a bit of a new look and feel, so I hope you like it. Do let me know in the comments if you find anything amiss with the new website. Otherwise we'll just assume it's fabulous.\n\nI'm working on a post about how to create a blog with quarto using jupyter and python, so you can too!\n\nSee you in more posts real soon! Love, Matt.\n"}}},{"rowIdx":570474,"cells":{"sha256":{"kind":"string","value":"1355a1b24ecdf3f1beb5b4399edca39fc6c5b133506fd7d3602d53ddaa94a15c"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":3039,"string":"3,039"},"lines":{"kind":"number","value":135,"string":"135"},"content":{"kind":"string","value":"---\ntitle: \"Collate Metadata - Aknowledgments\"\ndate: \"`r format(Sys.time(), '%d %B, %Y')`\"\nauthor: \"Eve Zeyl Fiskebeck\"\n\nformat: html\neditor: visual\n\nparams:\n metadata_files_dir: \"data/raw_metadata/metadata\"\n metadata_rds: \"data/raw_metadata/collated_metadata.RDS\"\n \n acknowledgments_files_dir: \"data/raw_metadata/acknowledge\"\n acknowledgments_rds: \"data/raw_metadata/collated_acknowledgements.RDS\"\n\n SEP_metadata_rds_in: \"data/\"\n SEP_metadata_rds_out: \"data/prepared_data/SEP_metadata.csv\"\n \n prepared_data_dir: \"data/prepared_data\"\n all_tree_metadata_rds: \"data/prepared_data/all_tree_metadata_CAB.RDS\"\n \n---\n\n\n<!-- previous script \n2023-10-18_GISAID_data_prepare.Rmd \n\n--> \n\n\n```{r setup }\nlibrary(here)\nlibrary(tidyverse)\nlibrary(readxl)\nlibrary(xlsx)\nlibrary(plyr)\n```\n\nPreparing this script, so we can rerun in case things are missing.\nAnd allow reruning when all datasets are checked. \n\n# Collate Metadata\n\nGet list of files with the metadata\n```{r}\nmetadata_list <- \n list.files(here::here(params$metadata_files_dir), \n full.names = TRUE,\n pattern = \".xls\"\n )\n```\n\n\n```{r}\nmetadata_df <- \n plyr::ldply(\n metadata_list,\n readxl::read_excel,\n col_types = \"text\",\n skip = 0) %>%\n # ensure no white lines \n dplyr::filter_all(., dplyr::any_vars(!is.na(.)))\n```\n\n\nSaving as RDS in our directory \n```{r}\nsaveRDS(metadata_df, here::here(params$metadata_rds))\n```\n\n## update the all tree metadata with the isolate names\n<!-- ONLY do once \nnow we need to update the already prepared metadata for all trees\n```{r}\nall_tree_metadata <- readRDS(here::here(params$all_tree_metadata_rds)) \n\nall_tree_metadata <- \n all_tree_metadata %>%\n dplyr::left_join(metadata_df %>%\n select(Isolate_Id, Isolate_Name), \n by = c(\"Isolate_Id\" = \"Isolate_Id\")) \n```\n\nNow we need to check if missing - This is the Norwegian isolates\n```{r}\nall_tree_metadata %>%\n filter(is.na(Isolate_Name)) %>%\n View()\n```\n\nnow we can resave as RDS \n```{r}\nsaveRDS(all_tree_metadata, here::here(params$all_tree_metadata_rds))\n```\n\nand we need to re-export for the people \n```{r}\nxlsx::write.xlsx(all_tree_metadata, \n file = here::here(params$prepared_data_dir, \"all_tree_metadata_CAB_IsolateNames.xlsx\"), \n sheetName = \"all_tree_metadata\",\n row.names = FALSE,\n showNA = FALSE)\n\n```\n\n-->\n\n# Collate Aknowledgments\n\n- [ ] ! the two first lines need to be added again to the final file \n- [ ] Acknowledgments only for the samples and segments we are using\n\n```{r}\nacknowledge_list <- \n list.files(here::here(params$acknowledgments_files_dir), \n full.names = TRUE,\n pattern = \".xls\")\n\nacknowledge_df <- \n plyr::ldply(\n acknowledge_list,\n readxl::read_excel,\n col_types = \"text\",\n skip = 2) %>%\n # ensure no white lines \n dplyr::filter_all(., dplyr::any_vars(!is.na(.))) \n\ndim(acknowledge_df)\n```\n\nSaving as RDS in our directory \n```{r}\nsaveRDS(acknowledge_df, here::here(params$acknowledgments_rds))\n```\n\n"}}},{"rowIdx":570475,"cells":{"sha256":{"kind":"string","value":"ba68761ec30b8264c5b33b06d388a9e2624deb4889c5e13cf78cf9be83c2dd07"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":3101,"string":"3,101"},"lines":{"kind":"number","value":76,"string":"76"},"content":{"kind":"string","value":"---\ntitle: \"Installation\"\naliases:\n - ../installation.html\n---\n\n::: callout-warning\n# Blender 4.2\nFor Blender 4.2, we no longer have to download anything externally. Molecular Nodes can be installed and updated from _within_ Blender, using the new [`extensions`](https://https://extensions.blender.org/) platform. The installation instructions below are now much simpler.\n:::\n\n## Downloading Blender\n\nMolecular Nodes is an add-on for the 3D animation program Blender. It runs inside of Blender, so the first step for installation is to [download and install](https://www.blender.org/download/) the latest version of blender.\n\n{align=\"center\" width=\"800px\"}\n\n\n## Installing the Addon\n\nFollow the screenshots below to install the add-on. These steps should be the same across platforms.\n\nOpen the preferences window.\n\n\n\nSelect the `Get Extensions` panel and search for `Molecular Nodes`.\n\n\n\nClicking the `Install` button will download and install the add-on. The download is ~100 MB so may take some minutes depending on internet connection.\n\n\n\n### That's it!\n\nAll of the additional python packages now come pre-installed. To update the add-on, use the same panel and Blender should automatically detect updates.\n\n### From the command line\n\nFor headless machines and CI, the extension can be installed without opening Blender. Blender refuses to reach the extensions platform unless online access is enabled, so pass `--online-mode`:\n\n```bash\nblender --online-mode --command extension sync\nblender --online-mode --command extension install --enable molecularnodes\n```\n\n## Installing the Startup Template\n\nThere are a number of settings that are useful to have tweaked on startup. You can install the `Molecular Nodes` startup template in the add-on preferences or uninstall the template.\n\nTHis adds it as an option on the startup splash screen, or when starting a new file.\n\n## Start Importing Structures!\n\nMolecular nodes should be fully installed. See the [Getting Started](importing.qmd) page on how to start importing into Blender!\n\n## Troubleshooting\n\n### Installation error: missing biotite module\n\nIn some Linux systems, trying to install `Molecular Nodes` through the `Get Extensions` panel in Blender may lead to the following error:\n\n```bash\nReport: Error\nNo module named 'biotite.structure.bonds'\n```\nThis is likely due to a mismatch in the Python versions installed in your system and in what Blender expects (see issue [#629](https://github.com/BradyAJohnston/MolecularNodes/issues/629)).\n\nTo avoid this issue, try installing Blender through a self-contained package system such as Flatpack or Snap. These package managers usually need to be installed and/or activated in your system.\n\nFor example, after installing Snap in Fedora 40, you can install Blender through Snap with the command which solves the `biotite` dependency error.\n\n```bash\nsudo snap install blender --classic\n```\n"}}},{"rowIdx":570476,"cells":{"sha256":{"kind":"string","value":"d945bcbb9722122327e09880d1d8184d3ab4d35be0b8007fcf821348ebe96d8a"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":3492,"string":"3,492"},"lines":{"kind":"number","value":80,"string":"80"},"content":{"kind":"string","value":"---\ntitle: Selections\nauthor: Brady Johnston\naliases:\n - \"02_selections.html\"\n---\n\nSelections power every part of Molecular Visualisation.\nYou are probably used to using selections via manually pointing and clicking with your mouse, or typing long strings of complex selection algebra.\n\n## A Simple Example\n\nTo create a selection of residues ALA, CYS and TRP, only inside of chain A, you would use the following code inside of PyMol:\n\n``` python\nselect my_selection, chain A and (resn ALA+CYS+TRP)\n```\n\nInside of Molecular Nodes, you combine different *selection* nodes.\nThe outputs of these are are a *boolean* `True / False` for each atom.\nThe logical test being performed depends on the node.\nTo recreate the selection above, we can use the `Select Chain` and `Select Res Name` nodes.\n\n\n\nThis might initially seem quite verbose.\nThe good thing however is that these selections can always be updated and tweaked.\nThey can even be created procedurally based on the values of other selections or animations.\n\nIn the example video below we are using the selection to apply the `Atoms` style.\nWe can change the residues and chains that are selected by ticking and unticking the boxes.\nEach of these selections also take a *field* input, so they can use the output of other selection nodes.\nThe selections from the two nodes are being combined with the `Boolean Math` node set to `And`.\nOther boolean math operations are also available.\n\n\n\n## Different Styles Combined\n\nIn this example, we apply the `Surface` style to one chain, and the `Atoms` style to another chain.\nWe can join the two styles together with the `Join Geometry` node.\n\n\n\n## Combining Selections\n\nSelections can be combined not just through `Boolean Math` nodes, but also by connecting the output from one node to the input of another selection.\nIn this example we are selecting two chains for applying the `Atoms` style to.\n\nThe selection of `Chain A` we then control though the `Cube` selection node.\nThe addition of this node creates a new object called `Empty_Cube` which we can transform by Grabbing, Rotating and Scaling to change what is inside of the cube.\nThe style is then applied to the updated selection, which varies for `Cahin A` but not for `Chain F`.\n\n\n\n## More Examples\n\nYou can use these dynamic selections for any kind of boolean input.\nIt can be used to select the atoms to apply a style to or even to change different aspects of that style itself.\nIn this example, we first choose the atoms to apply the style to; then instead, we change whether or not we use cylinders for the cartoon style.\n\n\n\nSelecting based on the `entity_id`, to reveal the rotary axel of the protein complex underneath.\n\n\n\n## Open a Local File\n\nTo open a `.pdb`, `.mmCIF`, `.pdbx` or other similar files, use the <kbd>Local File</kbd> tab.\n\nYou can set the name for the molecule, which will become the object's name once imported.\nUse the folder icon to select the file you would like to import.\n\n\n\nThe 'Default Style' was changed to 'Cartoon', but no other default import options were changed.\nThe Default Cube was deleted with <kbd>X</kbd> as the protein was imported *inside* of the cube.\n\n\n"}}},{"rowIdx":570477,"cells":{"sha256":{"kind":"string","value":"7819fe4be6e8275076f77a34cc0f6256db3264348aa47c42ec67d39becbf9123"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":3768,"string":"3,768"},"lines":{"kind":"number","value":64,"string":"64"},"content":{"kind":"string","value":"---\ntitle: CryoEM Maps\nauthor: Brady Johnston\naliases:\n - \"04_cryoem.html\"\n\n---\n\n\n\nWe can import CryoEM maps, which are imported into Blender as a 'volume' object. As Blender is designed to work with data for movies and TV shows, this is implemented in a way to emulate smoke or gas. We aren't looking to annimate these volumes, but when we import the volume we can create a surface based on a cutoff for the density, the same as you do with ChimeraX.\n\n## Importing Panel\n\n\n\n- **Name** The name that will be given to the newly created object inside of Blender.\n- **File** The `.map` or `.map.gz` file to be imported to Blender.\n\n- **Style** The initial style to apply to the volume object when imported into Blender.\n- **Invert Data** Whether to invert the data when importing (usually used for CryoET data)\n- **Centre Density** Translates the data so that the world origin is in the middle, rather than the corner of the density.\n\n::: {.callout-warning}\nBlender _currently_ can't directly import the `.map` files. Instead we convert the file to a `.vdb` volume file, then import that to Blender. This intermediate file will be saved next to the `.map` file. If you move or delete this intermediate file, then it will break the volume inside of Blender, so be careful moving files around after import.\n:::\n\n\n## Example Data\n\nAs an example, we are going to use the structure and the map correlating to `8E3Z` as an example. You can import the structure using the PDB import in Molecular Nodes, and the map you can download from the [EMDB page](https://www.ebi.ac.uk/emdb/EMD-27874). Ensure you download the `.map.gz` file.\n\n## Importing\nSelect the file and import to Blender.\n\nThere should be a surface that has appeared, which has a goemetry nodes modifier with a single node applied.\n\n\n\nYou can adjust the threshold which is the cutoff at which Blender will create a surface mesh.\n\n## Importing Structure\n\nWhen we import a structure, both the density and the structure should align perfectly, as long as neither were imported with the 'centre' option selected.\n\nWe can import the structure `8E3Z` via the PDB tab with `Ribbon` styling, and see how it overlaps with the surface from the density.\n\n\n\n## Sampling Colors\n\nCurrently all of the colors on the mesh are uniform. There is the field input `Color`, so we can _sample_ the color from the atoms of a structure, and use that information to color the surface.\n\nWe will use the `Sample Nearest Attribute` node from the `Molecular Nodes -> Density` menu. This node will take the atoms of a structure and can transfer the information from that structure onto the surface created for that volume.\n\nThe structure which we have currently imported however isn't showing the atoms - it is displaying the ribbon style. We can duplicate the structure with <kbd>Shift</kbd> + <kbd>D</kbd> in the 3D viewport, and change the node group used so that it displays the atoms rather than applying a style.\n\n\n\nIn the demo video, we duplicate the object. When duplicating, both objects are using the same node tree, so we then also duplicate the node tree. Now we can remove the style from the new node tree, and use this object to sample colors for our surface. The original structure object remains unchanged and still contains a node tree which applies the ribbon style.\n\nIf you want to change the colors which are applied to the surface, we change the node tree of the object which contains the atoms. Changing thesse nodes will change the colors that are applied, and thus change the colors which are sampled to the surface for coloring.\n\n\n"}}},{"rowIdx":570478,"cells":{"sha256":{"kind":"string","value":"da735b8c741f6c4fb30da5ddb6866137fdd7d0ab4eec6052433185cce7580bde"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":3871,"string":"3,871"},"lines":{"kind":"number","value":115,"string":"115"},"content":{"kind":"string","value":"---\ntitle: \"Rendering\"\ndescription: \"Rendering images and animations with the Canvas.\"\nexecute:\n daemon: false\n---\n\nThe [](`~mn.Canvas`) is used to render images and animations of any [](`~mn.Molecule`),\nwhether it is a single static structure or a multi-frame trajectory. Annotations can be\nattached to the molecule and will appear in the render.\n\n```{python}\nimport molecularnodes as mn\nimport MDAnalysis as mda\nfrom MDAnalysis.tests.datafiles import PSF, DCD\n\ncanvas = mn.Canvas()\n```\n\n## Add a Molecule to Blender\n\n```{python}\n#| warning: false\nu = mda.Universe(PSF, DCD)\nmol = mn.Molecule(u).add_style(\"cartoon\")\n\n# add resid 1 and 129 with spheres style, using mesh geometry\nmol.add_style(\"spheres\", selection=\"resid 1 129\", sphere=\"Instance\")\n```\n\n## Add annotations\n\n```{python}\nmol.annotations.add_universe_info()\nmol.annotations.add_com_distance(\n selection1=\"resid 1\", selection2=\"resid 129\", text1=\"r1\", text2=\"r129\"\n)\n```\n\n## Render an Image\n\n[](`~mn.Canvas.snapshot`) renders a still image and returns it as an\n`IPython.display.Image`, which displays automatically when it is the result of a notebook\ncell. Pass `path=` to also save the image to disk, `render_scale=` for a quick lower-resolution\npreview, and `frame=` to render a specific frame of the scene.\n\n```{python}\ncanvas.look_at(mol)\ncanvas.snapshot(frame=75)\n```\n\nTo show a render that isn't the last expression in a cell (e.g. inside a loop), wrap the\ncall with `display()`:\n\n```python\nfor frame in [1, 50, 90]:\n display(canvas.snapshot(frame=frame))\n```\n\n## Render an Animation\n\n[](`~mn.Canvas.animation`) renders the scene's frame range and returns the result for\ndisplay in the notebook — an `IPython.display.Video` for MP4 (the default) or an\n`IPython.display.Image` for GIF output (`format=\"gif\"`, which requires the `pillow`\npackage). As with `snapshot()`, pass `path=` to also save the file to disk; a ``.gif``\nsuffix on the path selects GIF output automatically. The frame rate can be overridden\nper-call with `fps=`.\n\n```{python}\ncanvas.animation(frame_start=10, frame_end=50, render_scale=50)\n```\n\n## Deterministic Renders\n\nRenders can be made fully reproducible, which is useful for regression testing a\nproject that uses MolecularNodes as a library — comparing today's render of a scene\nagainst a committed reference image. Three settings pin down the sources of\nnondeterminism:\n\n- render with Cycles on the CPU (`device=\"CPU\"`) — GPU output varies between\n drivers and hardware\n- disable denoising (`denoise=False`) — OpenImageDenoise output is not stable\n across versions and platforms\n- run the compositor on the CPU (`canvas.compositor.device = \"CPU\"`)\n\nCycles renders from a fixed sampling seed by default, so with a set sample count the\nsame scene then renders to the same pixels every time:\n\n```{python}\nimport io\nimport numpy as np\nfrom PIL import Image\n\ncanvas.engine = mn.scene.Cycles(samples=32, device=\"CPU\", denoise=False)\ncanvas.compositor.device = \"CPU\"\ncanvas.resolution = (320, 180)\n\n\ndef pixels(snapshot):\n return np.asarray(Image.open(io.BytesIO(snapshot.data)))\n\n\nnp.array_equal(pixels(canvas.snapshot()), pixels(canvas.snapshot()))\n```\n\nTwo caveats on the scope of that exactness:\n\n- Renders are pixel-identical, but the written PNG files are not byte-identical\n (metadata differs between writes), so compare decoded pixels rather than files.\n- Pixel-exactness holds per platform and Blender version. Across operating systems\n and versions the same render drifts slightly, so cross-platform comparisons need a\n small tolerance. Blender's own render tests count a pixel as failing when a channel\n differs by more than ~4/255 and fail the image when more than 1% of pixels fail;\n MolecularNodes' render regression tests (`tests/test_render_images.py`) use the\n same model and its macOS-rendered reference images pass on Linux and Windows.\n"}}},{"rowIdx":570479,"cells":{"sha256":{"kind":"string","value":"a61837b906efe8e4e145e041fdd31184b8dd5695bb0f309da7fb7201ed28019f"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":3936,"string":"3,936"},"lines":{"kind":"number","value":134,"string":"134"},"content":{"kind":"string","value":"---\ntitle: \"Materials\"\njupyter: python3\nfig-align: center\n---\n\nMolecularNodes ships a small set of pre-built materials, available as classes in\n`mn.material`. Instantiating one creates a new, independent Blender material —\nstarting values are set through the constructor, and every parameter remains\ntweakable on the instance afterwards.\n\n```{python}\nimport molecularnodes as mn\nfrom molecularnodes.nodes import geometry as g\nfrom nodebpy import shader as s\n\ncv = mn.Canvas(engine=\"EEVEE\", resolution=(800, 800), transparent=True)\n```\n\n## Pre-built materials\n\nPass a preset directly to `add_style()`. The parameters map onto the inputs of\nthe nodes inside the material, so changing them updates the render immediately.\n\n```{python}\n# | label: fig-material-preset\n# | fig-cap: The Ambient Occlusion material is performant and aesthetically pleasing. Parameters can be tweaked on the instance after creation.\n# | layout-ncol: 2\n# | fig-subcap:\n# | - \"distance=2.0\"\n# | - \"distance=0.15\"\n\nmat = mn.material.AmbientOcclusion(distance=2.0)\nmol = mn.Molecule.fetch(\"8H1B\").add_style(\"surface\", material=mat)\n\ncv.look_at(mol)\ndisplay(cv.snapshot())\n\nmat.distance = 0.15\ncv.snapshot()\n```\n\nTo swap the material on an already-created style, assign a new material to the\nstyle node's material socket:\n\n```{python}\n# | label: fig-change-material-flat\n# | layout-ncol: 1\n# | fig-subcap:\n# | - \"Flat material with outline\"\nflat = mn.material.Flat(thickness=0.2)\nmol.tree.tree.nodes[\"Style Surface\"].inputs[\"Material\"].default_value = (\n flat.material\n)\ncv.snapshot()\n```\n\nEach preset is a separate material datablock, so tweaking one style's material\nnever affects another's. The underlying Blender material is always available as\n`.material`, and the nodes used to build it are kept as attributes on the\ninstance (e.g. `mat.ao`, `mat.bsdf`) for access to anything not exposed as a\nparameter.\n\n## Custom materials\n\nFor full control, build a material from scratch with the\n[`nodebpy`](https://bradyajohnston.github.io/nodebpy) shader nodes. Anything\nbuilt inside the `with s.material()` block becomes the material's node tree,\nand the `MNColor` node provides the per-atom colors.\n\n```{python}\n# | label: fig-material-custom\n# | fig-cap: A custom material built with nodebpy's shader nodes.\n\nwith s.material(\"Custom Material\") as custom:\n custom.nodes.clear()\n col = mn.nodes.shader.MNColor()\n ao = s.AmbientOcclusion(col, distance=1.0, samples=128)\n ao.o.color >> s.MaterialOutput().i.surface\n\n# regular node lookup and property assignment\nmol.tree.tree.nodes[\"Style Surface\"].inputs[\"Material\"].default_value = (\n custom.material\n)\ncv.snapshot()\n```\n\n## Render Engines and Materials\n\nThe different render engines handle materials diferently.\n```{python}\nimport MDAnalysis as mda\nimport itertools\nfrom MDAnalysis.tests.datafiles import PSF, DCD\n\ncv = mn.Canvas(resolution=(800, 800), transparent=True)\n```\n\n\n```{python}\n# | warning: false\n# | label: fig-eevee-raytracing\n# | fig-cap: \"How different materials behave in each render engine\"\n# | layout-ncol: 2\n# | fig-subcap:\n# | - \"Cycles + Default\"\n# | - \"EEVEE + Default\"\n# | - \"Cycles + Ambient Occlusion\"\n# | - \"EEVEE + Ambient Occlusion\"\n# | - \"Cycles + Flat\"\n# | - \"EEVEE + Flat\"\n# | - \"Cycles + Squishy\"\n# | - \"EEVEE + Squishy\"\n\nmol = mn.Molecule.load(PSF, DCD)\nwith mol.tree.reset() as (atoms, join):\n style = g.StyleSpheres(sphere=\"Instance\")\n style.i[\"Quality\"].default_value = 4\n color = g.ColorElement(c=g.RandomColor(g.ChainID()))\n atoms >> g.SetColor(color=color) >> style >> join\n\ncv.look_at(mol)\nengines = [mn.scene.Cycles(samples=32), mn.scene.EEVEE()]\nmaterials = [\n mn.material.Default(),\n mn.material.AmbientOcclusion(),\n mn.material.Flat(),\n mn.material.Squishy(),\n]\nfor mat, eng in itertools.product(materials, engines):\n cv.engine = eng\n style.i.material.default_value = mat.material\n cv.snapshot()\n```\n"}}},{"rowIdx":570480,"cells":{"sha256":{"kind":"string","value":"25fda9bbbcd5043c5b59fcc94ac5a33f4cf9b827d56e7eba9f458340c417af3a"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":3993,"string":"3,993"},"lines":{"kind":"number","value":110,"string":"110"},"content":{"kind":"string","value":"---\ntitle: \"Examples\"\nnumber-sections: true\nnumber-depth: 3\n---\n\n## Animating ATP Synthase\n\n\n\nTo create the animation of ATP synthase you can morph between separate states of ATP synthase and export them from your favourite molecular graphics program.\nIn this example, we will use ChimeraX.\n\nUse the following code in ChimeraX's console to import and create a morph between the 3 different protein conformations that will create the animation:\n\n``` html\nopen 6n2y 6n2z 6n30\nmorph #1,2,3 wrap true\n```\n\nYou can now play back the animation inside of ChimeraX.\n\nTo save the animation as a multi-pdb file, use the following code to save the `.pdb` file:\n\n``` html\nsave atp-frames.pdb #4 allCoordsets true\n```\n\nYou can now open the multi-frame pdb file inside of Molecular Nodes using the 'Open Local File' tab.\n\n\n\nThis example is taken directly from the ChimeraX twitter account:\n\n<blockquote class=\"twitter-tweet\">\n\n<p lang=\"en\" dir=\"ltr\">\n\nTo morph between 3 conformations of ATP synthase use ChimeraX commands \"open 6n2y 6n2z 6n30\" and \"morph #1,2,3 wrap true\".\n<a href=\"https://twitter.com/hashtag/ChimeraXHowTo?src=hash&ref_src=twsrc%5Etfw\">#ChimeraXHowTo</a> <a href=\"https://t.co/2u4Au6Tvpf\">pic.twitter.com/2u4Au6Tvpf</a>\n\n</p>\n\n--- ChimeraX (@UCSFChimeraX) <a href=\"https://twitter.com/UCSFChimeraX/status/1258888093068701696?ref_src=twsrc%5Etfw\">May 8, 2020</a>\n\n</blockquote>\n\n```{=html}\n<script async src=\"https://platform.twitter.com/widgets.js\" charset=\"utf-8\"></script>\n```\n## Animating COVID Spike Protein\n\n::: callout-caution\n# Requires MDAnalysis Installed\n\nTo follow this particular tutorial, ensure you have first [installed Molecular Nodes properly](tutorials/installation.qmd), including the optional MDAnalysis python package.\n:::\n\nDownload the trajectory files from the the [CHARMM-GUI website](https://charmm-gui.org/?doc=archive&lib=covid19):\n\nYou will want to download the `.xtc` files for the spike protein in membrane.\n\n::: callout-caution\nThe .xtc file will be large, \\~2GB so the download may take a while.\n:::\n\n\n\nUnzip the files (you may need to unzip the unzipped file) and then inside of Molecular Nodes, in the MD Trajectory tab, select the tpology file (`last_frame_nos.pdb`) and the trajectory file (`trj_nos.xtc`).\\\n\\\nGive the molecule a name, and your panel should look like this:\n\n\n\n### Frames to Import\n\nIn the frame import options, we can choose which frames from the trajectory to import.\nImporting every frame (`interval = 1` ) will take long time to load, and balloon-out the save-file size of the `.blend` file.\nIt's best to work with some subset of the trajectory frames, and interpolate between them with Molecular Nodes.\\\n\n::: callout-note\nMolecular nodes is still surprisingly performant with thousands of frames loaded, but the `.blend` file takes a long time to save and load due to several GBs worth of data being save and loaded each time.\n:::\n\nFor this example we are going to import every 10th frame.\n\n### Click 'Import Frames' and Wait\n\nMolecular Nodes is working through a lot of data, so this load step can take a few minutes - be patient.\nOnce it's all loaded, the structure along with the atomic properties are available for use inside of Geometry Nodes!\n\n### Click space to play the animation.\n\nEnsure to change over to `Cycles` & `rendered view` to view the coloured atoms, but otherwise the animation should be playing if press `space`!\n\n*Just Points*\n\n\n\n*Rendered View* In this scene I additionally added a Sun light.\n\n\n\n### Geometry Nodes\n\nNow that the animation is imported into geometry nodes, you can work with the points as you would otherwise.\n\nTry applying some noise, or in this example, scaling the points to `0` based on the proximity to an empty.\n\n\n"}}},{"rowIdx":570481,"cells":{"sha256":{"kind":"string","value":"12d9ba7dc4288ea685c405ba336bc16e46511ec29845591c0fe5e16a95db2905"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":4216,"string":"4,216"},"lines":{"kind":"number","value":62,"string":"62"},"content":{"kind":"string","value":"---\ntitle: Streaming Trajectories\nsubtitle: Load and stream a currently running simulation using MDanalysis and IMDClient.\nauthor: Brady Johnston\nfig-align: center\n---\n\nWith the MDAnalysis [2.10 release](https://www.mdanalysis.org/2025/10/26/release-2.10.0/) we can now stream information (positions, velocities etc) from an actively running simulation.\nThis is useful for doing analysis 'on-the-fly' and even getting access to timesteps that would normally be discarded when saving to disk for size constraints.\n\nThanks to MDAnalysis handling everything 'under the hood' - we can also stream these results directly into Blender using Molecular Nodes.\n\nThis example uses the [2025 IMD workshop materials](https://github.com/amruthesht/imd-workshop-2025) which provides example code for setting up and running a simulation that provide results for streaming. The simulation I show below is the example [GROMACS simulation](https://github.com/amruthesht/imd-workshop-2025/tree/main/workshop/sample_simulation/GROMACS).\n\nStreaming isn't supported in a GROMACS release at time of writing (but will be soon) - I followed [these instructions](https://github.com/amruthesht/imd-workshop-2025?tab=readme-ov-file#from-source) for how to compile it myself with the new IMDClient functionality.\n\n## Running the Simulation\n\nOnce installed GROMACS and running the simulation should just be `./run.sh` inside of the `sample_simulation/GROMACS` folder. By defualt it is set up to run on a single core (this demo was intended to run on GitHub codespaces), so we can make a quick change to increase the core count or else it won't simulate fast enough for our purposes:\n\n```{filename=\"run.sh\"}\n # Run simulation with IMD streaming\n-gmx mdrun -v -nt 1 \\\n+gmx mdrun -v -nt 8 \\\n```\n\nAfter increasing the number of cores - we can start kick off the simulation and it will setup and wait for a client to connect.\n\n\n\nOnce we have the simulation up and running - we can connect to it remotely with something using IMD Client. This is the point where you can do analysis with MDAnalysis, and also the point where we can connect to it with Molecular Nodes.\n\n## Connecting to the Simulation\n\n::: {.callout-warning}\n# Experimental\nConnecting to streaming trajectories in MN should still be considered experimental. As it becomes more mature in the simulation packages themselves we should be able to better decide on how this should all work.\n:::\n\nOnce we have a simulation that is up and running, we can import it to Molecular Nodes like we would a normal trajecotry. For the coordinates, instead of a filepath we give the IMD streaming server. By default on your own machine it will be `imd://localhost:8889`.\n\nAfter we load the trajectory, every time the frame changes inside of Blender it will fetch the latest frame from the simulation and the 3D model will be updated.\n\n\n\nIf the simulation is dependent on GO / PAUSE orders then pausing Blender will pause the simulation, but we could just as easily link this to a running simulation which doesn't wait for us and just visually inspect / render some frames from the simulation as it runs.\n\nEverything inside of Molecular Nodes like adding styles & selections should all work as normal, just that the positions come from a currently updating simulation.\n\n::: {.callout-warning}\n# Timeline\n\nIf you scrub around on the timeline (move to the first frame) it will still advance to the next frame in the simulation. There is no caching of positions currently enabled so even going back in 'time' will move forward in time for the simulation.\n:::\n\n### Potential Features\n\nLike said above - this is very experimental. Potential features could be:\n - caching some number of frames of the simulation so that scrubbing back N number of frames does acces old positions rather than always fetching new ones\n - IMD protocol supports sending forces back to the simulation. We could interact with the simulation via Blender\n\nIf you have other suggestions of questions around it, please open an issue or discussion on the [GitHub page](https://github.com/BradyAJohnston/MolecularNodes) and discuss it there.\n"}}},{"rowIdx":570482,"cells":{"sha256":{"kind":"string","value":"24e22081aec5950f95b8a86da3e7fa5f64761ea75bf44f604302584ba3dc97c3"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":4322,"string":"4,322"},"lines":{"kind":"number","value":57,"string":"57"},"content":{"kind":"string","value":"---\ntoc: false\ntitle: Hello World! And Why I'm Inspired to Start a Blog\ndate: \"11/22/2020\"\ndescription: A reflection on what inspired me to start a blog and three reasons I think it could be a good\n idea.\ncategories: blogging\nimage: thumbnail.jpg\nimage-alt: Matt raises his arms in joy at the world.\n---\n\n![Matt raises his arms in joy at the world.!](main.jpg \"\")\n\n\nWell, I've been thinking about getting this blog started for months now. \nI guess a combination of inertia, up-front investment in blogging platform selection/setup, and spending a little too much time writing and rewriting the first content post has drawn out the period from initial inspiration to making the blog a reality.\nNeedless to say, I'm pretty excited to finally get things going.\n\nBefore we dive headlong into the weeds of ML algorithms, statistical methods, and whatever I happen to be learning and teaching at the moment, I figured it would be good to articulate why I've felt inspired to get started blogging in the first place. \nHopefully this will serve the dual purpose of clarifying my intentions and introducing a vastly underappreciated concept in data science that I hope to weave through the posts to come.\n\n\n## Learning\n\nThe initial inception about blogging probably originated from some comments about learning that Jeremy Howard makes in the [Practical Deep Learning](https://course.fast.ai/) course from [fastai](https://www.fast.ai/).\nDuring one of the lectures, he mentions that it's a great idea to start blogging. \nTo paraphrase Jeremy:\n\n> The thing I really love about blogging is that it helps you learn; by writing things down, you synthesize your ideas.\n\nBeautiful. That definitely rings true for me. I tend to take notes and play around with code when learning new concepts anyway. One of my key hypotheses about this blogging experiment is that making the effort to transform those notes into blog posts will help me learn more effectively. \n\n\n## Teaching\n\nAh, teaching. Yes, sometimes it's that thing that takes time away from your research, forcing you to sit alone in a windowless room squinting at hand-written math on a fat stack of homework assignments. But sometimes it actually involves interacting with students, endeavoring to explain a concept, and watching them light up when they get it. The latter manifestation of teaching was one of my favorite things about grad school and academia in general. \nWhile I certainly still get to do some teaching as an industry data scientist, I could see myself returning to a more teaching-centric gig somewhere off in the future.\nThus we have our second key hypothesis about the blogging experiment, that the writing will entertain my inclination to teach.\n\n\n## Contributing\n\nWorking in the field of data science today is a bit like standing in front of a massive complimentary all-you-can-learn buffet. \nThere is an abundance of free material out on the interwebs for learning pretty much anything in data science from hello world python tutorials to research papers on cutting-edge deep learning techniques.\nI've personally benefited from many a blog post that helped me unpack a new concept or get started using a new tool.\nAnd let's not forget the gigantic cyber warehouse full of freely available open source software tools that volunteer developers have straight-up donated to humanity. \n\nI realize that up to now, I've simply been consuming all of this free goodness without giving anything substantive back in return. \nWell then, it's time to start evening the score.\nWhich brings us to key hypothesis number three, that through these blog posts, I might be able to create something helpful, thereby being of service to a community that has freely given so much to me.\n\n\n## Live Long and Prosper, Blog\n\nPhew, there it is, the original source of inspiration for this blogging experiment, and three reasons I think it might be a good idea. The astute reader will have noticed that these three assertions have been formulated as hypotheses which are to be tested in the laboratory of experience. And thus, we also have our first glimpse of the [scientific method](https://en.wikipedia.org/wiki/Scientific_method), an underrated concept that is going to help us put the _science_ back in data science.\n\nWith that, blog, I christen thee, _Random Realizations_. \n"}}},{"rowIdx":570483,"cells":{"sha256":{"kind":"string","value":"bbb40f8e33fb0237b72632ef82e57af8536740aef6bbe53c171bfda38a82dcc6"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":4559,"string":"4,559"},"lines":{"kind":"number","value":195,"string":"195"},"content":{"kind":"string","value":"---\ntitle: \"Solvent Density\"\ndescription: \"An example for visualizing solvent density.\"\nexecute:\n daemon: false\n---\n\nThis example follows the [Calculating the solvent density around a protein](https://userguide.mdanalysis.org/stable/examples/analysis/volumetric/density_analysis.html) example from the MDAnalysis user guide.\n\n```{python}\nimport MDAnalysis as mda\nimport molecularnodes as mn\nfrom MDAnalysis import transformations as trans\nfrom MDAnalysis.analysis import density\nfrom MDAnalysis.tests.datafiles import TPR, XTC\n\ncanvas = mn.Canvas()\n```\n\n## Load and transform Universe\n\n```{python}\n# | warning: false\nu = mda.Universe(TPR, XTC)\n\nprotein = u.select_atoms(\"protein\")\nwater = u.select_atoms(\"not protein\")\n\nworkflow = [\n trans.unwrap(u.atoms), # unwrap all fragments\n trans.center_in_box(\n protein,\n center=\"geometry\", # move atoms to center protein\n ),\n trans.wrap(\n water,\n compound=\"residues\", # wrap each water back into box\n ),\n trans.fit_rot_trans(\n protein,\n protein,\n weights=\"mass\", # align protein to first frame\n ),\n]\n\nu.trajectory.add_transformations(*workflow)\n```\n\n## Analyse and Export `.dx`\n\n```{python}\now = u.select_atoms(\"name OW\")\ndens = density.DensityAnalysis(ow, delta=4.0, padding=2)\ndens.run()\n# convert density unit to TIP4P\ndens.results.density.convert_density(\"TIP4P\")\ndens.results.density.export(\"water.dx\")\n```\n\n## Add Universe to Blender\n\nImport universe as a `Molecule` and add a ribbon style to represent the protein. We also add a style to the non-protein atoms, sliced along the `y` axis. After taking the snapshot for visual reference we rebuild the tree with just the ribbon style, as we will be showing the water as a density.\n```{python}\nfrom molecularnodes.nodes import geometry as mg\nfrom nodebpy.nodes import geometry as g\n\nt = mn.Molecule(u)\n\nwith t.tree.reset() as (atoms, join):\n (\n atoms\n >> mg.StyleRibbon(quality=5, peptide_radius=2)\n >> mg.StyleSpheres(\n selection=g.Position().o.position.y < mg.Centroid(),\n sphere=\"Instance\"\n )\n >> g.SetMaterial(material=mn.material.Default().material)\n >> join\n )\n atoms >> mn.nodes.geometry.StyleRibbon(quality=5, peptide_radius=2) >> join\n\ncanvas.look_at(t, (90, 0, 60))\ncanvas.snapshot()\n```\n\n## Add density component\n\nWe can load the density that was written-out from the analysis performed previously as a `Density` entity.\nIts values are stored at 32-bit float precision in Blender, even though `.dx` files hold 64-bit floats: Blender's grid nodes (`Get Named Grid`, `Sample Grid`) cannot read 64-bit grids.\n```{python}\n# load density file\ncanvas.engine = \"EEVEE\"\nd = mn.entities.density.Grids.load(\n file_path=\"water.dx\",\n overwrite=True,\n)\n\nwith d.tree.reset() as (geo, join):\n (\n geo\n >> mg.DensityStyleISOSurface()\n >> g.SetMaterial(material=mn.material.Transparent().material)\n >> join\n )\n\n\nda = d.annotations.add_density_info()\nda.show_origin = da.show_delta = da.show_shape = False\n```\n\n## Visualization\n\n### Set density style values\n\n```{python}\n# get the density style\n# ds = d.styles[0]\n# set the positive color to blue with 50% opacity\n# ds.positive_color = (0, 0, 1, 0.5)\n```\n\n### ISO Value 0.5\n\n```{python}\n# set ISO value to 0.5\n# ds.iso_value = 0.5\n```\n\n```{python}\n# frame the density component and render\ncanvas.look_at(d, viewpoint=\"front\")\ncanvas.snapshot()\n```\n\n### ISO Value 0.5 with Contours\n\n```{python}\n# set ISO value to 0.5\n# ds.iso_value = 0.5\n# # enable contours\n# ds.show_contours = True\n# # set contour thickness\n# ds.contour_thickness = 0.25\n# # set contour colors\n# ds.contour_color = (1, 1, 1, 1)\n```\n\n```{python}\n# frame the density component and render\ncanvas.look_at(d.get_view(), viewpoint=\"front\")\ncanvas.snapshot()\n```\n\n### From Top with Grid Axes\n\n```{python}\n# add grid axes annotation\nga = d.annotations.add_grid_axes_3d()\n```\n\n```{python}\n# set viewpoint to top\ncanvas.look_at(d.get_view(), viewpoint=\"top\")\ndisplay(canvas.snapshot())\n# hide grid axes\nga.visible = False\n```\n\n### ISO Value 0.5 sliced from Left\n\n```{python}\n# slice the grid from the left 50%\n# ds.slice_left = 50\n```\n\n```{python}\n# set viewpoint to left\ncanvas.look_at(d.get_view(), viewpoint=\"left\")\ncanvas.snapshot()\n```\n\n### Only Contours\n\n```{python}\n# reset slicing\n# ds.slice_left = 0\n# # only show contours\n# ds.only_contours = True\n```\n\n```{python}\n# set viewpoint to front\ncanvas.look_at(d.get_view(), viewpoint=\"front\")\ncanvas.snapshot()\n```\n"}}},{"rowIdx":570484,"cells":{"sha256":{"kind":"string","value":"5c636b173a20b0950d161966251365bb8a203f14c859179921ba5bb3f593c609"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":4586,"string":"4,586"},"lines":{"kind":"number","value":173,"string":"173"},"content":{"kind":"string","value":"---\ntitle: \"Manually assigning regulators\"\nformat:\n html:\n fig-width: 7\n fig-height: 4\n fig-dpi: 100\n---\n\n```{r}\n#| include: false\nknitr::opts_chunk$set(\n collapse = TRUE,\n comment = \"#>\"\n)\n```\n\nThe purpose of this vignette is to show how to manually configure the\n`is_regulator` vector, e.g. when you want to run *scregclust* on a custom set of\nregulators (not TFs or kinases), or if your data is from an organism other than\nhuman, e.g. mouse. This vignette will show how to do this using a data set from\nthe mouse brain ([GSE60361](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60361)), and a\n[list of mouse TFs provided by Aertslab](https://resources.aertslab.org/cistarget/tf_lists/).\n\nWe use [Seurat](https://satijalab.org/seurat/) for pre-processing of the data.\n\n```{r}\n#| label: load-packages\n#| results: hide\n#| message: false\n\n# Load required packages\nlibrary(GEOquery)\nlibrary(Seurat)\nlibrary(scregclust)\n```\n\nRead in the data and preprocess it in *Seurat*. Here, we simply use the\nfull dataset. In practice, you would perform additional quality checks and,\ne.g., investigate PCA, UMAP, or TSNE plots of the data. We use the\npackage *GEOquery* to download meta data for the data.\n\n```{r}\n#| label: load-data\n#| results: hide\n#| message: false\n#| warning: false\n\n# Download the gene expression data\nurl <- paste0(\n \"https://www.ncbi.nlm.nih.gov/geo/download/\",\n \"?acc=GSE60361&format=file&\",\n \"file=GSE60361%5FC1%2D3005%2DExpression%2Etxt%2Egz\"\n)\nexpr_path <- file.path(tempdir(), \"Expression.txt.gz\")\ndownload.file(url, expr_path, cacheOK = FALSE, mode = \"wb\")\n\n# Load the gene expression data\nexpr <- read.table(\n expr_path,\n header = TRUE,\n sep = \"\\t\",\n stringsAsFactors = FALSE,\n fill = TRUE\n)\n\n# A few gene symbols appear as duplicates, make unique.\ngene_symbols <- make.unique(expr[, 1], sep = \"-\")\nexpr <- expr[, -1]\nrownames(expr) <- gene_symbols\n\n# Download meta data\ngse <- getGEO(\"GSE60361\")\nmeta_data <- pData(phenoData(gse[[1]]))\n# Sample names are stored in the meta data's row names\nsample_names <- rownames(meta_data)\ncolnames(expr) <- sample_names\n\n# Create Seurat object and preprocess the data using SCTransform\nmouse <- CreateSeuratObject(\n counts = expr,\n min.cells = 3,\n min.features = 500,\n meta.data = meta_data\n)\nmouse <- SCTransform(mouse, verbose = TRUE)\n```\n\nThe built in transcription factor lists in *scregclust* are for human\ntranscription factors (TFs) and kinases. Download and read in a list of\nmouse-specific TFs.\n\n```{r}\n#| label: load-tfs\n#| results: hide\n#| message: false\n\nurl <- \"https://resources.aertslab.org/cistarget/tf_lists/allTFs_mm.txt\"\ntfs_path <- file.path(tempdir(), \"allTFs_mm.txt\")\ndownload.file(url, tfs_path, cacheOK = FALSE, mode = \"w\")\ntfs <- read.table(\n tfs_path,\n header = FALSE,\n sep = \"\\t\",\n stringsAsFactors = FALSE\n)\ntfs <- tfs[, 1]\n```\n\nExtract `gene x cells` table\n\n```{r}\n#| label: extract-gene-cells-table\nz <- GetAssayData(mouse, layer = \"scale.data\")\ndim(z)\n```\n\nMake sure data is in the format for *scregclust*\n\n```{r}\n#| label: scregclust-format\nout <- scregclust_format(z, mode = \"TF\")\n\ngenesymbols <- out$genesymbols\nsample_assignment <- out$sample_assignment\n```\n\nManually create the indicator vector `is_regulator`\n\n```{r}\n#| label: manual-is-regulator\nis_regulator <- rep(0, length = length(genesymbols))\nis_regulator[which(genesymbols %in% tfs)] <- 1\n```\n\nFinally, run scregclust to estimate the model. The run can be reproduced with\nthe command below. A pre-fitted model can be downloaded from\n[GitHub](https://github.com/scmethods/scregclust/raw/main/datasets/mouse_scregclust.rds)\nfor convenience.\n\n```{r}\n#| label: run-scregclust\n\n# # Run scregclust\n# set.seed(8374)\n# fit <- scregclust(\n# z, genesymbols, is_regulator, penalization = seq(0.1, 0.5, 0.05),\n# n_modules = 10L, n_cycles = 50L, noise_threshold = 0.05\n# )\n# saveRDS(fit, file = \"datasets/mouse_scregclust.rds\")\n\nurl <- paste0(\n \"https://github.com/scmethods/scregclust/raw/main/datasets/\",\n \"mouse_scregclust.rds\"\n)\nfit_path <- file.path(tempdir(), \"mouse_scregclust.rds\")\ndownload.file(url, fit_path)\nfit <- readRDS(fit_path)\n```\n\nVisualize the fit\n\n```{r}\n#| label: viz-fit\n#| fig-alt: >\n#| Boxplots of predictive R^2 per module (bottom) and\n#| regulator importance (top) over the penalization parameters\n#| specified during model estimation. A decreasing trend can\n#| be seen in R^2 per module until about 0.35 with a drop from 0.4.\n#| In addition, a slow and steady increase in regulator importance\n#| is followed by an increase from around 0.4 penalization.\nplot(fit)\n```\n"}}},{"rowIdx":570485,"cells":{"sha256":{"kind":"string","value":"3544c9aa3bbaaab4368fcb29d2f8c379d488dece13ac7fb83907b2d71cd56ae0"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":5329,"string":"5,329"},"lines":{"kind":"number","value":240,"string":"240"},"content":{"kind":"string","value":"---\ntitle: Density\ndescription: \"Examples of using the Density entity API\"\njupyter: python3\nexecute:\n daemon: false\n---\n\nThe Density entity can be used to load density grids into Blender. It uses MDAnalysis [`GridDataFormats`](https://www.mdanalysis.org/GridDataFormats/) library and supports a wide variety of [formats](https://www.mdanalysis.org/GridDataFormats/gridData/formats.html).\n\n## Setup Molecular Nodes\n\n```{python}\nimport molecularnodes as mn\n\n# create a canvas object\ncanvas = mn.Canvas()\n```\n\n### Add 1EHE Molecule\n\n```{python}\n# fetch 1EHE and add cartoon style with uniform color and transparency\nmol = mn.Molecule.fetch(\"1EHE\").add_style(\"cartoon\", color=(0.2, 1.0, 0.3, 0.5))\n```\n\n```{python}\n# frame molecule and render\ncanvas.look_at(mol)\ncanvas.snapshot()\n```\n\n## Add APBS density example\n\n```{python}\nfrom pathlib import Path\n\napbs_sample = Path(\"../../\") / \"tests/data/apbs.dx.gz\"\n\n# load density file\nd = mn.entities.density.io.load(\n file_path=apbs_sample,\n style=\"density_iso_surface\",\n overwrite=True,\n)\n```\n\n### Set ISO Value\n\n```{python}\n# get the density style\nds = d.styles[0]\n```\n\n```{python}\n# set iso value\nds.iso_value = 1\n```\n\n## Show Density Grid and Info\n\n```{python}\n# add density grid annotation\ndg = d.annotations.add_grid_axes()\n# add density info annotation\ndi = d.annotations.add_density_info()\n```\n\n```{python}\n# frame molecule and render\ncanvas.look_at(mol.get_view(), viewpoint=\"front\")\ncanvas.snapshot()\n```\n\n```{python}\n# hide density grid and info\ndg.visible = di.visible = False\n```\n\n## Density View\n\n```{python}\n# frame density and render\ncanvas.look_at(d.get_view(), viewpoint=\"front\")\ncanvas.snapshot()\n```\n\n### ISO Value = 25\n\n```{python}\n# set iso value\nds.iso_value = 25\n```\n\n```{python}\n# frame density and render\ncanvas.look_at(d.get_view(), viewpoint=\"front\")\ncanvas.snapshot()\n```\n\n### Contours\n\n```{python}\n# reset ISO value to 1\nds.iso_value = 1\n```\n\n```{python}\n# enable contours\nds.show_contours = True\n# set contour thickness\nds.contour_thickness = 0.1\nds.contour_color = (1.0, 1.0, 1.0, 0.5)\n# use ds.only_contours to only show contours\n```\n\n```{python}\n# frame density and render\ncanvas.look_at(d.get_view(), viewpoint=\"front\")\ncanvas.snapshot()\n```\n\n### Slicing\n\nDensity grids can be sliced from `left`, `right`, `front`, `back`, `top` and `bottom` as a percentage of the grid width along that direction.\n\n```{python}\n# remove molecule style\nmol.styles[0].remove()\n```\n\n```{python}\n# slice the grid from the left 35%\nds.slice_left = 35\n```\n\n```{python}\n# frame density and render\ncanvas.look_at(d.get_view(), viewpoint=\"front\")\ncanvas.snapshot()\n```\n\n### Rotated View\n\n```{python}\n# rotate camera 45 degress to left from front view\ncanvas.camera.rotation = (90, 0, -45)\n```\n\n```{python}\n# frame density and render\ncanvas.look_at(d.get_view())\ncanvas.snapshot()\n```\n\n### Material\n\n```{python}\n# disable contours\nds.show_contours = False\n```\n\n```{python}\n# set material\nds.material = \"Fresnel Outline\"\n```\n\n```{python}\n# frame density and render\ncanvas.look_at(d.get_view())\ncanvas.snapshot()\n```\n\n### Colors\n\n```{python}\n# set color for positive ISO values\nds.positive_color = (0.7, 1.0, 0.7, 1.0)\n# set color for negative ISO values\nds.negative_color = (0.7, 0.0, 1.0, 1.0)\n```\n\n```{python}\n# frame density and render\ncanvas.look_at(d.get_view())\ncanvas.snapshot()\n```\n\n## Annotations\n\nThe bundled annotation types for Densities are:\n\n- `density_info` - For info about the density grid\n- `grid_axes` - For displaying the grid axes\n- `label_2d` - For adding a generic 2d label in viewport / render\n- `label_3d` - For adding a generic 3d label\n\n### density_info\n\nDisplay the density grid info.\n\nThe input parameters for this annotation are:\n\n- `location` - Normalized 2d location (0.0 - 1.0) to show the info wrt viewport / render\n- `show_filename` - Whether to display the grid filename\n- `show_threshold` - Whether to display the ISO value\n- `show_origin` - Whether to display the origin coordinates of the grid\n- `show_delta` - Whether to display the grid lengths along the axes\n- `show_shape` - Whether to display the grid size along the axes\n\n### grid_axes\n\nDisplay the grid axes.\n\nThe input parameters for this annotation are:\n\n- `show_length` - Whether to show the length of each axis\n- `units` - The units to display for the axis length\n\n### label_2d\n\nDisplay a generic 2d label in the viewport / render.\n\nThe input parameters for this annotation are:\n\n- `text` - Text to display\n- `location` - Normalized location (0.0 - 1.0) to show the text wrt viewport / render\n\n### label_3d\n\nDisplay a generic 3d label on the density grid.\n\nThe input parameters for this annotation are:\n\n- `text` - Text to display\n- `location` - 3d coordinates in grid to display text\n\n### Custom Annotations\n\nA custom density annotation class has to extend `DensityAnnotation` and implement the `draw` method. Optional `defaults` method can be used to set defaults for the annotation and a `validate` method that can validate inputs when they change can be implemented. The custom class will have access to the density entity via `self.density`, the density grid object (`Grid`) via `self.density.grid` and the annotation params via `self.interface`. Please see the Annotations API for all the methods available to draw onto the viewport / renders from the `draw` code.\n"}}},{"rowIdx":570486,"cells":{"sha256":{"kind":"string","value":"2c13ba6461d2ff630137257897f5290b9ca354abbd95c57edadb83741838d762"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":6088,"string":"6,088"},"lines":{"kind":"number","value":149,"string":"149"},"content":{"kind":"string","value":"---\ntitle: Python API\ndescription: Introduction to the API and the quirks of programming in Blender\njupyter: python3\n---\n\n::: {.callout-warning}\n# The API is Experimental\n\nMolecular Nodes was first built as an add-on for Blender, intended to be used through the GUI.\n\nA lot of improvements have been made to now allow a scriptable interface, but a lot of these changes should still be considered experimental and may change in future versions. The version is pinned to the versions of Blender it is intended for, so while MN is currently version `520.0`, it should not be considered that stable.\n\nThe quirks of progamming within Blender are numerous and will likely remain regardless of version.\n:::\n\n\n## Working in Blender\n\nWhen scripting with Molecular Nodes, it's important to remember that everything we do doesn't work in isolation. All of the objects we add & changes we make happen within a \"3D scene\" inside of Blender, so deleting one thing or another doesn't always clean up and reset everything you might expect.\n\nBlender itself has a very extensive python API, accessible through the `bpy` module. Molecular Nodes is designed so that for most general usage you shouldn't have to ever directly use `bpy`, but all of the complex settings and controls are still always accessible through that module.\n\nBlender does release the [`bpy`](https://pypi.org/p/bpy) module on PyPI, which lets us use Blender as a regular python package — no Blender install required.\n\n## Installation\n\nIf installed inside of Blender as an add-on, you can script using MN in the python console or when running scripts through Blender. The extension is not importable as `molecularnodes` there; import it through Blender's extension namespace instead:\n\n```python\nimport bl_ext.blender_org.molecularnodes as mn\n```\n\nYou can also install and use inside of your own python projects.\n\n::: { .callout-warning}\n# Python Versions\n\n`molecularnodes` is only compatible with `python==3.13`\n\nBlender is very particular about python versions (and especially `numpy` versions). Blender adheres to the [VFX Reference platform](https://vfxplatform.com/) which determines python and common package versions to be used across multiple VFX software for consistency.\n\nBecause of this, the `bpy` package upon which `molecularnodes` is build is locked to `python==3.13` and in particular `numpy>=2.3`. Going outside of these very particular versions is recipe for disaster.\n\n:::\n\nBlender (`bpy`) is listed as an optional dependency for MN so if you install inside of Blender itself, there isn't a double up with the bunlded `bpy`. This means you'll need to always manually request it when installing or add it as a requirement for separate projects.\n\n```bash\npip install molecularnodes[bpy]\n```\n\n## Getting Started\n\nThe main class you will interact with is the [](`~mn.Molecule`). A single `Molecule`\nrepresents any structure — a static structure downloaded from the PDB, a local file, or\na molecular dynamics trajectory — using an [](`MDAnalysis.Universe`) as its underlying\ndata model.\n\n- [](`~mn.Molecule`)\n - Load static structures with [](`mn.Molecule.fetch`) / [](`mn.Molecule.load`), or\n wrap an existing [](`MDAnalysis.Universe`) directly\n - Structure files are parsed with [](`biotite`) and converted to a `mda.Universe`\n - Supports MDAnalysis selection strings on every structure\n - A multi-model structure or trajectory simply has more than one frame, updated from\n the `Universe` as the scene frame changes\n\n\n::: {.callout-note}\nPreviously static structures and trajectories were separate `Molecule` and `Trajectory`\nclasses; these have now been [unified](https://github.com/BradyAJohnston/MolecularNodes/discussions/949)\ninto the single `Molecule` from `v520+`.\n:::\n\n## Molecules\n\nTo get started, with import the module start a new scene be creating out [](`~mn.Canvas`) object. This class lets us more easily control the 3D scene that we create our models and do rendering with. Changing render engine, camera resolution and controlling the camera is all done through this class.\n\n::: {.callout-important}\n\nYou _must_ a new canvas first, before loading any structures.\n\n:::\n\n```{python}\nimport molecularnodes as mn\n\ncanvas = mn.Canvas(mn.scene.Cycles(samples=32), resolution=(720, 480))\n```\n\n### Add a `Molecule`\n\nThe [](`mn.Molecule`) can take a `AtomArray` from the `biotite` package directly, or it has the [](`mn.Molecule.fetch`) and [](`mn.Molecule.load`) class methods to download directly from the PDB or load a local file.\n\nAfter loading the molecule, to be able to see anything we must first add a style. Then we can frame the camera on the object and take a snapshot.\n\n```{python}\nmol = mn.Molecule.fetch(\"4ozs\")\nmol.add_style(\"cartoon\")\ncanvas.look_at(mol)\ncanvas.snapshot()\n```\n```{python}\ncanvas.clear()\ncanvas.background = (0, 0.6, 0.6, 1.0)\ncanvas.camera.lens = 130\n\nmol = mn.Molecule.fetch(\"9MD2\")\nmol.add_style(\"cartoon\", loop_radius=0.6, material=mn.material.AmbientOcclusion())\ncanvas.look_at(mol)\ncanvas.snapshot()\n```\n\n```{python}\ncanvas.look_at(mol, (-180, 0, 0))\ncanvas.snapshot()\n```\n\n\n```{python}\ncanvas.clear()\ncanvas.engine = \"eevee\"\ncanvas.resolution = (800, 800)\ncanvas.transparent = True\n\nmol = mn.Molecule.fetch(\"Q8W3K0\", database=\"alphafold\")\nwith mol.tree.reset() as (atoms, join):\n (\n atoms\n >> mn.nodes.geometry.SetColor(color=mn.nodes.geometry.ColorPLDDT())\n >> mn.nodes.geometry.StyleCartoon(\n material=mn.material.Default().material\n )\n >> join\n )\ncanvas.look_at(mol)\ncanvas.snapshot()\n```\n\n## Next steps\n\nA `Molecule` behaves the same whether it holds a single structure or a full trajectory. From\nhere:\n\n- [Styles](styles.qmd) — the full set of representations, selections and node-tree styling.\n- [Trajectories](trajectories.qmd) — playback, per-frame updates and colouring by computed\n values for multi-frame molecules.\n- [Rendering](rendering.qmd) — producing images and animations with the `Canvas`.\n- [Annotations](annotations.qmd) — adding measurements and labels to a render.\n"}}},{"rowIdx":570487,"cells":{"sha256":{"kind":"string","value":"a3bf8f6df4dfe21c8badafbd3bc8789bf0373f79893ca37439d72191f179bb59"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":6377,"string":"6,377"},"lines":{"kind":"number","value":249,"string":"249"},"content":{"kind":"string","value":"---\ntitle: \"Check stats datasets\"\nauthor: \"Eve Zeyl Fiskebeck\"\ndate: \"`r format(Sys.time(), '%d %B, %Y')`\"\nparams:\n prepdata_dir: \"data/prepared_data\" \n results_dir: \"results\"\nexecute: \n echo: false\n cache: false\nformat: html\nknitr:\n opts_chunk: \n collapse: true\n comment: \"#>\" \n R.options:\n knitr.graphics.auto_pdf: true\neditor_options: \n chunk_output_type: console\n---\n\n```{r setup }\nlibrary(here)\nlibrary(tidyverse)\nlibrary(readxl)\nlibrary(xlsx)\nlibrary(plyr)\nlibrary(seqinr)\n```\n\n\n```{r}\nHA_init_file <- here::here(\"results\", \"H5N1_HA_all\", \"msa\", \"H5N1_initial_ref_MSA_jalview.fasta\")\nHA_init <- read.fasta(HA_init_file)\n\nnames_HA_init <- unlist(getAnnot(HA_init)) \nlength(names_HA_init)\n\nseq_HA_init <- unlist(getSequence(HA_init, as.string = T)) %>%\n str_remove_all(., \"-\")\n\nHA_init_df <- tibble(\"header\" = names_HA_init, \n \"sequence\" = seq_HA_init) %>%\n mutate(length = str_length(sequence))\n\nmax(HA_init_df$length)\n```\n\n\n```{r}\nNA_init_file <- here::here(\"results\", \"H5N1_NA\", \"MSA\", \"NA_N1_initial_alignment.fasta\")\nNA_init <- read.fasta(NA_init_file)\n\nnames_NA_init <- unlist(getAnnot(NA_init)) \nlength(names_NA_init)\n\nseq_NA_init <- unlist(getSequence(NA_init, as.string = T)) %>%\n str_remove_all(., \"-\")\n\nNA_init_df <- tibble(\"header\" = names_NA_init, \n \"sequence\" = seq_NA_init) %>%\n mutate(length = str_length(sequence))\n\nmax(NA_init_df$length)\n```\n\n# checking length for NVI sequences\n\n```{r}\nall_tree_metadata <- readRDS(here::here(\"data\", \"prepared_data\", \"all_tree_metadata_CAB.RDS\"))\n```\n\nH5N5 HA \n```{r}\nH5N5_HA_file <- here::here(\"results\", \"H5N5_HA\", \"iqtree\", \"H5N5_all_SEP_MSA_jalview.fasta\")\nH5N5_HA_data <- read.fasta(H5N5_HA_file) \nH5N5_HA_names <- unlist(getAnnot(H5N5_HA_data)) %>%\n str_remove_all(\">.*\\\\|\") %>%\n str_remove_all(\"\\\\/.*$\") %>%\n str_remove_all(\"(c$)|(_tr$)\")\n\nH5N5_HA_seq <- unlist(getSequence(H5N5_HA_data, as.string = T)) %>%\n str_remove_all(., \"-\")\n \nH5N5_HA_df <- tibble(\"names\" = H5N5_HA_names, \n \"seq\" = H5N5_HA_seq) \n\nH5N5_HA_df %>% \n left_join(all_tree_metadata, by = c(\"names\" = \"label\")) %>%\n filter(country == \"Norway\") %>%\n pull(seq) %>%\n str_length() %>%\n range()\n\nH5N5_HA_df %>% \n left_join(all_tree_metadata, by = c(\"names\" = \"label\")) %>%\n filter(country != \"Norway\") %>%\n #filter(Isolate_Id != \"EPI_ISL_177506\", Isolate_Id != \"EPI_ISL_1254\") %>%\n pull(seq) %>%\n str_length() %>%\n range()\n```\n\nThe H5N5 sample that was removed (dup)\n```{r}\nH5N5_HA_df %>% \n left_join(all_tree_metadata, by = c(\"names\" = \"label\")) %>%\n filter(Isolate_ID == \"Norway\") %>%\n #filter(Isolate_Id != \"EPI_ISL_177506\", Isolate_Id != \"EPI_ISL_1254\") %>%\n pull(seq) %>%\n str_length() %>%\n range()\n\n# the lengt of removed isolate\nH5N5_HA_df %>% \n left_join(all_tree_metadata, by = c(\"names\" = \"label\")) %>%\n filter(Isolate_Id == \"EPI_ISL_12028892\") %>%\n pull(seq) %>%\n str_length() \n \nH5N5_HA_df %>% \n left_join(all_tree_metadata, by = c(\"names\" = \"label\")) %>%\n filter(Isolate_Id != \"EPI_ISL_12028892\") %>%\n pull(seq) %>%\n str_length() %>%\n range()\n```\n\n\nH5N5 NA \n```{r}\nH5N5_NA_file <- here::here(\"results\", \"H5N5_NA\", \"iqtree\", \"SEP_NA_H5N5_clean_total_jalview_alignment.fasta\")\nH5N5_NA_data <- read.fasta(H5N5_NA_file) \nH5N5_NA_names <- unlist(getAnnot(H5N5_NA_data)) %>%\n str_remove_all(\">.*\\\\|\") %>%\n str_remove_all(\"\\\\/.*$\") %>%\n str_remove_all(\"(c$)|(_tr$)\")\n\nH5N5_NA_seq <- unlist(getSequence(H5N5_NA_data, as.string = T)) %>%\n str_remove_all(., \"-\")\n \nH5N5_NA_df <- tibble(\"names\" = H5N5_NA_names, \n \"seq\" = H5N5_NA_seq) \n\nH5N5_NA_df %>% \n left_join(all_tree_metadata, by = c(\"names\" = \"label\")) %>%\n filter(country == \"Norway\") %>%\n #filter(is.na(Isolate_Id))\n pull(seq) %>%\n str_length() %>%\n range()\n\nH5N5_NA_df %>% \n left_join(all_tree_metadata, by = c(\"names\" = \"label\")) %>%\n filter(country != \"Norway\") %>%\n pull(seq) %>%\n str_length() %>%\n range()\n\n# the lengt of removed isolate\nH5N5_NA_df %>% \n left_join(all_tree_metadata, by = c(\"names\" = \"label\")) %>%\n filter(Isolate_Id == \"EPI_ISL_12028892\") %>%\n pull(seq) %>%\n str_length() \n \nH5N5_NA_df %>% \n left_join(all_tree_metadata, by = c(\"names\" = \"label\")) %>%\n filter(Isolate_Id != \"EPI_ISL_12028892\") %>%\n pull(seq) %>%\n str_length() %>%\n range()\n```\n\nH5N1 HA\n\n```{r}\nH5N1_HA_file <- here::here(\"results\", \"H5N1_HA_all\", \"iqtree\", \"SEP_HA_H5N1_MSA_nomammals.fasta\")\nH5N1_HA_data <- read.fasta(H5N1_HA_file) \nH5N1_HA_names <- unlist(getAnnot(H5N1_HA_data)) %>%\n str_remove_all(\">.*\\\\|\") %>%\n str_remove_all(\"\\\\/.*$\") %>%\n str_remove_all(\"(c$)|(_tr$)\")\n\nH5N1_HA_seq <- unlist(getSequence(H5N1_HA_data, as.string = T)) %>%\n str_remove_all(., \"-\")\n \nH5N1_HA_df <- tibble(\"names\" = H5N1_HA_names, \n \"seq\" = H5N1_HA_seq) \n\nH5N1_HA_df %>% \n left_join(all_tree_metadata, by = c(\"names\" = \"label\")) %>%\n filter(country == \"Norway\") %>%\n #filter(is.na(Isolate_Id))\n pull(seq) %>%\n str_length() %>%\n range()\n\nH5N1_HA_df %>% \n left_join(all_tree_metadata, by = c(\"names\" = \"label\")) %>%\n filter(country != \"Norway\") %>%\n #filter(Isolate_Id != \"EPI_ISL_177506\", Isolate_Id != \"EPI_ISL_1254\") %>%\n pull(seq) %>%\n str_length() %>%\n range()\n```\n\n\nH5N1 NA\n\n```{r}\nH5N1_NA_file <- here::here(\"results\", \"H5N1_NA\", \"iqtree\", \"SEP_NA_H5N1_MSA_nommals.fasta\")\nH5N1_NA_data <- read.fasta(H5N1_NA_file) \nH5N1_NA_names <- unlist(getAnnot(H5N1_NA_data)) %>%\n str_remove_all(\">.*\\\\|\") %>%\n str_remove_all(\"\\\\/.*$\") %>%\n str_remove_all(\"(c$)|(_tr$)\")\n\nH5N1_NA_seq <- unlist(getSequence(H5N1_NA_data, as.string = T)) %>%\n str_remove_all(., \"-\")\n \nH5N1_NA_df <- tibble(\"names\" = H5N1_NA_names, \n \"seq\" = H5N1_NA_seq) \n\nH5N1_NA_df %>% \n left_join(all_tree_metadata, by = c(\"names\" = \"label\")) %>%\n filter(country == \"Norway\") %>%\n #filter(is.na(Isolate_Id))\n pull(seq) %>%\n str_length() %>%\n range()\n\nH5N1_NA_df %>% \n left_join(all_tree_metadata, by = c(\"names\" = \"label\")) %>%\n filter(country != \"Norway\") %>%\n #filter(Isolate_Id != \"EPI_ISL_177506\", Isolate_Id != \"EPI_ISL_1254\") %>%\n pull(seq) %>%\n str_length() %>%\n range()\n\nH5N1_NA_df %>% \n left_join(all_tree_metadata, by = c(\"names\" = \"label\")) %>%\n filter(country != \"Norway\") %>%\n filter(Isolate_Id != \"EPI_ISL_177506\", Isolate_Id != \"EPI_ISL_1254\") %>%\n pull(seq) %>%\n str_length() %>%\n range()\n```\n\n"}}},{"rowIdx":570488,"cells":{"sha256":{"kind":"string","value":"f292b9242f7f3a3826fb326d012a2768b72c01e183aeda6faceaa2bfe738019c"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":6380,"string":"6,380"},"lines":{"kind":"number","value":236,"string":"236"},"content":{"kind":"string","value":"---\ntitle: Node Trees\nsubtitle: Using `nodebpy` to create and edit node trees\n---\n\nMolecular Nodes uses `nodebpy` for creating and changing the node trees - Geometry, Shader and Compositor.\n\nThis allows for replicating the node-based workflows from the GUI, enabling the full power of Geometry Nodes (GN) and the other node editors from within a script.\n\nThe approach of `nodebpy` is to overload the `>>` operator, which instead acts as a \"node link\" operator when used inside of a node tree. We can access and edit the GN node tree using the `mol.tree` property. This is a `nodebpy.TreeBuilder` subclass for editing the tree.\n\n\n```{python}\nimport math\nimport molecularnodes as mn\nfrom molecularnodes.nodes import geometry as mg\nfrom nodebpy import geometry as g\n\ncv = mn.Canvas(mn.scene.Cycles(32))\n```\n\nEditing of a node tree looks like below.\n```{python}\nmol = mn.Molecule.fetch(\"1bna\")\n\nwith mol.tree.reset() as (atoms, join):\n mat = mn.material.Default().material\n (\n atoms\n >> mg.SeparateAtoms(selection=mg.IsSolvent().o.inverted)\n >> mg.StyleSpheres(material=mat)\n >> join\n )\n\nmol.tree\n```\n\n```{python}\ncv.look_at(mol)\ncv.snapshot()\n```\n\n\nTo use an MDAnalysis selection phrase inside a node tree, turn it into a node with\n[](`~molecularnodes.entities.molecule.selections.SelectionManager.node`). It stores the\nselection as a boolean attribute and returns a `Named Attribute` node reading it, reusing\nan existing selection where possible rather than creating a duplicate.\n\n```{python}\nwith mol.tree.reset() as (atoms, join):\n # 1bna is B-DNA, so split the two base-pair types across different styles\n (\n atoms\n >> mg.StyleSpheres(\n selection=mol.selections.node(\"resname DC DG\"),\n material=mat,\n )\n >> join\n )\n (\n atoms\n >> mg.StyleSticks(\n selection=mol.selections.node(\"resname DA DT\"),\n material=mat,\n )\n >> join\n )\n\nmol.tree\n```\n\n```{python}\ncv.look_at(mol)\ncv.snapshot()\n```\n\n## Non-Linear Node Trees\n\nThe of Geometry Nodes comes from the non-linear nature of working with node trees. We can express complex systems with relatively simple node setups.\n\n```{python}\nwith mol.tree.reset() as (atoms, join):\n atoms = (\n atoms\n >> mg.SeparateAtoms(selection=~mg.IsSolvent())\n >> mg.CentreOnSelection()\n >> mg.GeometryToPlanar()\n >> g.TransformGeometry(rotation=(0, math.pi/2, 0))\n )\n\n for x in [-1, 0, 1]:\n (\n atoms\n >> g.TransformGeometry(\n translation=(x * 3, 0, 0),\n rotation=(0, 0, x * (math.pi / 4))\n )\n >> mg.StyleSpheres(material=mat)\n >> join\n )\n\nmol.tree\n```\n\n```{python}\ncv.look_at(mol)\ncv.snapshot()\n```\n\nWe can use this non-linear approach to create two different styles and apply them to the same molecule.\nEach branch applys a new color scheme with the `SetColor` node and then applies one of the two styles.\nBoth style branches are joined back together with the `Join Geometry` node that is added by default\nafter calling the `mol.tree.reset()` to create the tree context.\n\n```{python}\ncv.clear()\ncv.engine = mn.scene.EEVEE()\nmol = mn.Molecule.fetch(\"8H1B\")\n\nmat = mn.material.Flat(threshold=0.2).material\n# mat = mn.material.Default().material\n\nwith mol.tree.reset() as (atoms, join):\n # creating one branch / chain of nodes for Ball and Stick style\n (\n atoms\n >> mg.SetColor(\n color=mg.ColorCommon(carbon=mg.RandomColor(id=mg.ChainID()))\n )\n >> mg.StyleSticks(\n selection=mg.IsSideChain() & mg.IsPeptide(),\n scale=0.6,\n material=mat\n )\n >> mg.StyleSurface(selection=mg.IsNucleic(), material=mat)\n >> join\n )\n\n # creating a second branch, where we apply a different color and style\n # but join it back into the same `Join Geometry` node\n (\n atoms\n >> mg.SetColor(color=mg.ColorRainbow())\n >> mg.StyleCartoon(\n selection=mg.IsPeptide(),\n quality=5,\n loop_radius=0.6,\n material=mat\n )\n >> join\n )\n\nmol.tree\n```\n\n\n\n```{python}\ncv.look_at(mol)\ncv.snapshot()\n```\n\n\n```{python}\ncv.resolution = (1080, 1080)\ncv.look_at(mol)\n\nwith mol.tree.reset() as (atoms, join):\n (\n atoms\n >> mg.SetColor(color=mg.ColorRainbow())\n >> mg.StyleCartoon(selection=mg.IsPeptide(),loop_radius=0.6, material=mat)\n >> mg.StyleSurface(selection=mg.IsNucleic(), material=mat)\n >> mg.StyleSticks(\n selection=mg.SelectProximity(\n atoms,\n subset=mg.IsNucleic(),\n expand=True,\n distance_a=4.0\n )\n & mg.IsPeptide()\n & mg.IsSideChain(),\n scale=0.6,\n material=mat\n )\n >> join\n )\n\ndisplay(cv.snapshot())\n\nmol.tree\n```\n\n\n## When is code evaluated\n\nThe `nodebpy` code is evaluated once, but the tree that is created will be re-evaluated every time a value or the Blender scene's frame changes.\nWe can set up a node tree that will animate as the frames change. The `AnimateValue` node uses the scene's current frame to interpolate between a starting and ending value (by default, `0` to `1`).\n\nThe node tree creation code below is evaluated once, but the selection changes on each frame so during the animation the resulting geometry that is created changes.\n\n\n```{python}\ncv.load_preset()\nanimation_length = 100\ncv.resolution = (600, 480)\ncv.samples = 8\ncv.frame_range = (1, animation_length)\n\nmol = mn.Molecule.fetch(\"4ozs\").add_style(\"cartoon\")\ncv.look_at(mol, viewpoint = \"left\")\nmat = mn.material.Flat(threshold=0.2).material\n\nwith mol.tree.reset() as (atoms, join):\n fac = mg.ChainParameter().o.factor\n frame = g.SceneTime().o.frame\n\n # the `.frame` value changes to reflect current scene.frame_current\n # we map that value to a range of 0 to 1 over the animation length,\n # and use that to select fraction of the chain that is selected and drawn\n sel = fac < g.MapRange.float(frame, 1, animation_length, 0, 1)\n\n (\n atoms\n >> mg.SetColor(color=mg.ColorRainbow())\n >> mg.StyleCartoon(selection=sel, material=mat)\n >> join\n )\n\ncv.animation()\n```\n\n\n```{python}\nmol.tree\n```\n"}}},{"rowIdx":570489,"cells":{"sha256":{"kind":"string","value":"ef24ee4ede39d0df67ea28666512dc2a6dd8f9ec694b1f5070b0a848c794bc75"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":6440,"string":"6,440"},"lines":{"kind":"number","value":68,"string":"68"},"content":{"kind":"string","value":"---\ntitle: \"About\"\ntoc: false\npage-layout: article\n---\n\n\n<img src=\"images/logo.png\" align=\"right\" style = \"height:250px;\"/>\n\n\n[](https://github.com/BradyAJohnston/MolecularNodes/actions/workflows/lint.yml)\n[](https://github.com/BradyAJohnston/MolecularNodes/actions/workflows/build.yml)\n[](https://github.com/BradyAJohnston/MolecularNodes/actions/workflows/docs.yml)\n[](https://app.codspeed.io/BradyAJohnston/MolecularNodes)\n[](https://github.com/BradyAJohnston/MolecularNodes/actions/workflows/tests-5.2.yml)\n[](https://github.com/BradyAJohnston/MolecularNodes/actions/workflows/tests-5.3.yml)\n[](https://github.com/BradyAJohnston/MolecularNodes/actions/workflows/tests-5.4.yml)\n\n[](https://extensions.blender.org/add-ons/molecularnodes/)\n[](https://github.com/BradyAJohnston/MolecularNodes/releases)\n[](https://github.com/BradyAJohnston/MolecularNodes/releases)\n[](https://pypi.org/project/molecularnodes/)\n[](https://pepy.tech/project/molecularnodes)\n\n[](https://github.com/BradyAJohnston/MolecularNodes/blob/main/LICENSE)\n[](https://zenodo.org/badge/latestdoi/485261976)\n[](https://pypi.org/project/biotite)\n[](https://pypi.org/project/MDAnalysis)\n[](https://github.com/BradyAJohnston/MolecularNodes/stargazers)\n\n<a href=\"https://discord.gg/fvw6vT3vY9\"><img src=\"https://img.shields.io/badge/Discord-%235865F2.svg?style=for-the-badge&logo=discord&logoColor=white\" alt=\"Join the scientific visualisation in Blender discord.\" style=\"height:35px\"/></a>\n<a href=\"https://patreon.com/bradyajohnston\"><img src=\"https://img.shields.io/endpoint.svg?url=https%3A%2F%2Fshieldsio-patreon.vercel.app%2Fapi%3Fusername%3Dbradyajohnston%26type%3Dpatrons&style=for-the-badge\" alt=\"Support me on Patreon with ongoing donations\" style=\"height:35px\"/></a>\n<a href=\"https://buymeacoffee.com/bradyajohnston\"><img src=\"https://img.shields.io/badge/Buy%20Me%20a%20Coffee-ffdd00?style=for-the-badge&logo=buy-me-a-coffee&logoColor=black\" alt=\"Support me by buying a couple of coffees as a one-off donation\" style=\"height:35px\"/></a>\n\n\n\nMolecular Nodes (MN) is an addon for the 3D modelling & animation program [Blender](https://blender.org).\nMN enables easy import of molecular data such as `.pdb` & `.mmCIF`, along with a variety of molecular dynamics trajectories and topologies from a variety of simulation sources. Other data formats including electron microscopy (EM) `.map`, EM tomography files such as `.map` and `.star`, with the potential to support more data formats as well.\n\nSee the talk below at the 2022 Blender conference for an overview of the add-on it's capabilities.\n\n::: center\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/adhTmwYwOiA?si=em6xRime7HZ8L44T\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen></iframe>\n:::\n\nMolecular Nodes provides the translation layer that allows importing of molecular data formats, while Blender provides the industry-leading animation and rendering tools to create visually stunning molecular graphics with ease.\n\nThe add-on runs on the [Geometry Nodes](https://docs.blender.org/manual/en/latest/modeling/geometry_nodes/introduction.html) procedural modelling and animation system inside of Blender, which gives the add-on speed and robustness with minimal user input required.\n\n\n|Procedural Animations|Different Stylings|MD Trajectories|\n|---|---| --- |\n|{width=\"400px\"}|||\n|EM Density maps|`.star` mapbacks| |\n| || |\n\n\n## Getting Started\n\nTo get started, checkout the [installation page](#installation) for detailed instructions on how to install the add-on. Next check out some of the tutorials, such as [intro to blender](tutorials/interface.qmd), [basics of downloading](tutorials/importing.qmd) from the PDB or how to [customise selections](tutorials/selections.qmd).\n\nThere are some _outdated_ tutorials on YouTube about how to use the add-on also. MN has been developed further and improved since these videos, but the general workflow remains the same.\n\n::: center\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/CvmFaRVmZRU\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen>\n\n</iframe>\n:::\n"}}},{"rowIdx":570490,"cells":{"sha256":{"kind":"string","value":"7c59508e55d3a3a0b9456127ef14c3cfdbe961d3067ea45ea7677e1acb7f6f6d"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":6464,"string":"6,464"},"lines":{"kind":"number","value":102,"string":"102"},"content":{"kind":"string","value":"---\ntitle: \"Simulation\"\nsubtitle: \"Running simple simulations using Molecular Nodes\"\nauthor: Brady Johnston\ndate: \"20250409\"\ntoc-depth: 3\n---\n\n{autoplay=true loop=true muted=true #fig-larger-structure}\n\nInside of Geometry Nodes, we can run simulations using the **Simulation Zone**.\nIn the most simple form, a simulation is just doing something with data from a previous iteration of the data.\nThe easiest example of this is moving the positions of some points, building on top of the previous positions of the point from earlier time points.\n\n### What is a simulation?\n\nThe approach taken in Molecular Nodes is to include several \"lower-level\" nodes that can run simulations in a more generic way. Simulations are still to be considered experimental, and the nodes will likely change in the future.\n\nAs simulations are improved more generally inside of Geometry Nodes, these nodes will also be updated and improved.\n\nBecause the nodes are likely to change in the future the documenation is not exhaustive, but these examples should be enough to get you started and explain some of how the system works.\n\n\n\n## Simulate Elastic Network\n\n{#fig-disordered autoplay=true loop=true muted=true}\n\nThis node simulates the edges of a mesh. It's intended for simulating an elastic network, but any mesh that is passed in will be simulated. By default the starting length of the edges be used as the rest length and the simulation will attempt to keep the edges at that length.\n\nIn the below examble, we create an elastic network between the alpha carbons of a protein structure, then use the node to simulate the network. This can then be styled using the **Style Ribbon** node, which only requires the alpha carbons of a structure to work.\n\nChanging the `Alpha Carbon::Float` value for the **Build Elastic Network** node increases the search distance. More edges are created leading to more structural stability, but also more computational cost.\n\n\n\n#### All-atom Simulation\n\nWe can create an elastic network that includes all atoms by selecting the `All Atom::Menu` enum for the **Build Elastic Network** node. The `Alpha Carbon::Float` input will still be used for building a network between the alpha carbons for overall structure stability, but the `All Atom::Float` input will be used for building a shorter-range network between all atoms.\n\nWe can't just use the **Style Ball and Stick** node because the edges are now between all of the atoms. Instead, we can sample the positions from the resulting simulation using the **Sample Position** node update the positions of our starting atoms using the **Set Position** node. Then we can style accordingly.\n\n\n\n\n\n\n## Simulate Curve\n\nThis is the simplest simulation nodes, which simulates any _Curve_ geometry that it is given, attempting to maintain the length between points of the curve and also handling point collisions.\n\nThe simplest inputs for this simulation are `Points::Int` which indicate how many points on the curve before and after this current point to check when trying to maintain length, and also the `Straightness::Float` which is a value between 0 and 1 that determines how straight the curve should be.\n\n\n\n\n\n\n\n\n## Simulate on Faces\n\nThis node simulates points with collisions, while also ensuring they remain attached to the faces of a mesh. It's intended for simulating membranes or something similar, where points need to collide side to side, but you can input and shape mesh and it will ensure the points stay attached to it.\n\n\n\n\n\n## Node Inputs\n\n### `Substeps::Int`\n\nThe number of substeps that are simulated for each frame that Blender advances. Usually the higher this number the more accurate the simulation is, but this currently isn't always the case.\n\n### `Simulation::Menu`\n\n#### `Force::Vector`\n\nForces used inside of the simulation are input here as a field that is evaluated on the `Point::Geometry` domain.\n\n#### `Drag::Float`\n\nThe drag value is a multiplier for the velocity of the point. The higher the drag value, the more the point will slow down. Useful to take the energy out of an otherwise unstable simulation.\n\n#### `Alpha::Float`\n\nA value for how _rigid_ the constraint is. The higher the value, the looser the constraint. `0::Float` will try to perfectly maintain the constraint. Even _very small_ values such as `0.001::Float` will lead to a more relaxed constraint. Relaxing constraints can help with simulation stability.\n\n## Forces\n\nForces inside of the simulation are a `Vector::Vector` field that is evaluated on the `Point::Geometry` domain. You can chain multiple forces together by using a **Vector Math** node to just add the outputs, or by puting the `Force::Vector` output of one node into the `Add::Vector` input of the next node in the chain.\n\n\n{#fig-forces}\n\n## Hook & Pin\n\nSometimes we don't want points to be simulated, and instead we want to manually specify their positions. This is done via the `Hook::Menu` and `Pin::Menu` inputs. `Pin::Menu` will stop the point from simulating at all and ensure that the points is always exactly at the `Target::Vector` input. This will default to whatever the current position of the point is inside of the simulation when `Selection::Bool` is set to true. To get an updating position, you can use the **Sample Position** node to sample the position of a point in the simulation and then use that as the `Target::Vector` input.\n\nFor the `Hook::Menu` inputs, it will move the point to the `Target::Vector` over a certain amount of time, essentially exerting a force but one that is more stable. The higher the `Decay::Float` value the quicker the rate of movement.\n\n{#fig-hook-pin}\n"}}},{"rowIdx":570491,"cells":{"sha256":{"kind":"string","value":"1a65bfcfaccaac42c1b02e67b71f020d62152036940430802346e7cb757dfebd"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":6622,"string":"6,622"},"lines":{"kind":"number","value":123,"string":"123"},"content":{"kind":"string","value":"---\ntitle: Trajectories\nsubtitle: Importing Molecular Dynamics data\nauthor: Brady Johnston\nbibliography: references.bib\nfig-align: center\naliases:\n - \"03_molecular_dynamics.html\"\n---\n\n::: {#fig-md-example-render}\n{autoplay=\"true\" loop=\"true\"}\n\nThe protein adenylate kinase (AdK) undergoes a structural change during its catalytic cycle between a closed and an open state that is captured in enhanced sampling simulations [@seyler2015].\nThe protein secondary structure is shown as a round ribbon with individual amino acid sidechains as ball-and-sticks.\nThe trajectory was rendered with Blender and the MolecularNodes plugin (Brady Johnston).\nTrajectory files are available via [MDAnalysisData](https://www.mdanalysis.org/MDAnalysisData/adk_transitions.html#adk-dims-transitions-ensemble-dataset) Copyright CC-BY 2023 Brady Johnston.\n:::\n\nAs well as importing static structures, the results from molecular dynamics simulations can be imported as models in to Blender.\nThis is enabled through the excellent package [`MDAnalysis`](https://www.mdanalysis.org/).\nThe imported structure will have an object created that will act as the topology file, and a connection is maintained the underlying `MDAnalysis.Universe` object that provides coordinates and allows for dynamic calculations to be performed during playback.\n\n::: callout-warning\n## Saving the File\n\nWhen saving the `.blend` file, a corresponding `.MNSession` file will be saved next to your Blender file.\nWhen reloading the file, the `MDAnalysis.Universe` will be reloaded from this file, so ensure you keep it next to the `.blend` file.\n:::\n\n## MD Trajectory Panel\n\nTo import trajectories, change the import method in the Molecular Nodes panel to the `MD` method.\nTo import a trajectory, select the topology and trajectory files.\nYou can choose the initial starting style, but this can easily be changed after import.\n\n{width=\"500px\"}\n\n## Import the Trajectory\n\nClick `Load` to import the selected trajectory with the chosen options.\nThe model will appear in the scene, and when the `Frame` changes inside of Blender, the corresponding frame will be displayed from the imported trajectory.\n\n### Changing Style\n\nTo change which style is displayed, you have to interact with the *Geometry Nodes*,you use the same Geometry Nodes tree that you use for other structures.\nAdding new style nodes and specifying their selections to limit limit the style to particular selections of atoms.\n\n### Subframes\n\nBy default each frame on Blender's timeline corresponds to the frame in the imported trajectory.\nYou can increase the `Subframes` number for this trajectory, and the selected number of frames will be created in between the frames of the loaded trajectory.\nIf Interpolate is selected, the positions will be linearly interpolated between the frames of the trajectory.\nIf subframes are used, the frames of the trajectory will no longer directly correspond to the frames inside of Blender.\nWith 2 subframes, frame 9 inside of Blender will correspond frame 3 of the trajectory.\n\nWhen linearly interpolating through subframes of a trajectory, we can correct for periodic boundary crossing with the `Correct` option enabled.\nThis is only available if your simulation box is cubic.\n\n### Custom Selections\n\nCreating selections through nodes is a very quick and powerful way of working.\nIt can be easier to create selections through text, and some niche selections are currently unable to created through the node system.\n\nWith `Trajectory` object selected, in the `Object` tab in the molecular nodes panel, we can create custom selections using the [MDAnalysis selection language](https://userguide.mdanalysis.org/stable/selections.html).\nThese selections will become available inside of the Geometry Nodes tree as a `Named Attribute`.\n\nThere are toggles for these selections to be updating and / or periodic in how they are calculated.\nThese options correspond directly to the options that are possible through the `MDAnalysis.Universe.select_atoms()` method for creating atom groups.\n\nUse the `Named Attribute` or `Select Attribute` nodes to get access to these selections inside of Geometry Nodes, and use them to selectively apply styles, colors and animations to your trajectory.\n\n{width=\"500px\"}\n\n## Creating the Animation\n\nTo replicate the animation which we see at the top of the tutorial, we can use some of the example datasets which are provided alongside `MDAnalysis` with the `MDAnalysisData` package.\nTo download one of the datasets, use the code below:\n\n``` python\n# pip install MDAnalysisData\nfrom MDAnalysisData import datasets\ndatasets.fetch_adk_transitions_FRODA()\n```\n\n### Loading the Trajectory\n\nWe will load the trajectory, and load all of the frame sin to memory to ensure we can make a smoother trajectory.\n\nIn the video below we have imported the trajectory, and we can adjust the number of frames in the scene, as well as the number of frames the trajectory will play back over.\nWe also enabled `EEVEE` atoms to display in the EEVEE render engine.\n\n\n\n#### Changing Styles\n\nWe can change the style of the imported trajectory, by adding a new style node.\nWe can combine styles with the `Join Geometry`.\nFor more details on adding styles, see the [importing](importing.qmd) tutorial.\n\n\n\nWe can apply the atoms style, only to the side chains of the protein, by using the `Backbone` selection node, and using the `is_side_chain` output.\nThis selectively applies the style to only those atoms in the selection.\nThe combined styles now contain only the atoms for the side chains and a continuous ribbon for the protein.\n\n\n\n### Setting the Scene\n\nWe can set up the scene a bit nicer with a backdrop.\nIn this case we create a plane using <kbd>Shift</kbd> + <Kbd>A</kbd> to add a plane, go in to [edit mode](#01-introduction-edit-mode) and extrude the backbdrop up with the <kbd>E</kbd> key.\nWe can create a slightly curved corner by bevelling the corner.\nSelect the two vertices of the edge and click <kbd>Ctrl</kbd> + <kbd>B</kbd>.\nMove the mouse and use the scroll wheel to adjust the settings, then left click to apply.\n\n\n\n### Rendering the Animation\n\nWe can change some final settings of the style, do a test `Render Image`, change the export settings for where the frames of the animation are going to be saved, then we can click `Render Animation` to render all of the frames of the animation.\n\n\n"}}},{"rowIdx":570492,"cells":{"sha256":{"kind":"string","value":"9ade50d4debe35fa0f9f7cf0afe4aef7f8348032416bc90a395bddbe3e3681e9"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":6872,"string":"6,872"},"lines":{"kind":"number","value":250,"string":"250"},"content":{"kind":"string","value":"---\ntitle: \"SEP H5N1 NA Phylogenetic tree\"\nauthor: \"Eve Zeyl Fiskebeck\"\ndate: \"`r format(Sys.time(), '%d %B, %Y')`\"\nparams:\n strain: \"H5N1\"\n segment: \"HA\"\n all_tree_metadata: \"data/prepared_data/all_tree_metadata_CAB.RDS\"\n tree: \"results/H5N1_NA/prerun/iqtree/SEP_NA_H5N1_MSA_nonamedub.fasta.contree\"\n save_dir: \"results/H5N1_NA/prerun\"\n env_file: \"results/env_files/prerun_H5N1_NA_SEP.RData\"\n source_code_dir: 'C:/Users/vi2067/OneDrive - Veterinaerinstituttet/REINSTALLED/projects_vault/Projects/2023/1_AvianInfluenza/12302_Avian_influenza_2023/code/R'\nexecute: \n echo: false\n cache: true\nformat: html\nknitr:\n opts_chunk: \n collapse: true\n comment: \"#>\" \n R.options:\n knitr.graphics.auto_pdf: true\neditor_options: \n chunk_output_type: console\n---\n\n# Setup\n\nNote this tree not redone removing duplicates \nSo I will have to collapse as is \n\n- sourcing scripts and functions\n\n```{r setup}\n#| include: false\nlibrary(here)\nsource(here::here(params$source_code, \"AI_main.R\"))\n\n# Provisional until we get a package\nfun_files_path <- list.files(here::here(params$source_code, \"fun\"),\n pattern = \"*.R\",\n full.names = T)\n# sourcing all functions\nsapply(fun_files_path,source,.GlobalEnv)\n\n```\n\nNote: if you are modifying the script and want to continue to work after, you can load a previous environment (don't forget to save it at the end of your work).\n\nLoads the environment (to continue previous work)\n\n```{r optional load environment}\n#| include: false\n# Uncomment if you want to load a saved environment file\n#if (file.exists(params$env_file)) {load(params$env_file)}\n```\n\n# Preparation of metadata specific to the tree\n\nThis is the metadata that we combined to do all the trees, and where the host family has been added manually.\n\n```{r}\nall_tree_metadata <- readRDS(file = here(params$all_tree_metadata))\nglimpse(all_tree_metadata)\n\n# I need a copy without filter to retrieve the names at the end\nall_tree_metadata2 <- all_tree_metadata\n```\n\nWe select only the columns that we will use, to reduce complexity\n\n```{r}\nall_tree_metadata <- \n all_tree_metadata %>%\n dplyr::select(\"label\", \"Isolate_Id\", \"subtype\", \n \"continent\", \"country\", \"year\",\n \"Host\", \"Host_latin\", \"Host_family\")\n```\n\n\n# Information:\n\nFor the purpose of homogeneization between plots, for the different phylogenetic trees, palettes and grouping helpers are defined in separate files which are sourced in this script.\n\n- palettes are defined in the file `AI_palettes.R`\n\n<!-- reasoning detail and putting that in place in 2024-01-02_SEP_HA_final_tree_script.Rmd\nlast commit 2024-01-30 in the pilot project. After started to concise in functions.\n-->\n\n# Importing phylogenetic tree, preparing the tree and metdata for plotting\n\n## Rooting the tree\n\n- importing the tree and cleaning the tip labels (removing extension .fasta)\n\n- NB we used the consensus tree\n\n```{r}\ntree <- ape::read.tree(here(params$tree))\ntree$tip.label <- tree$tip.label %>% str_remove_all(., \".*\\\\|\") %>%\n str_remove_all(., \"t$\") \n```\n\n- fast plotting: we see that 2 very long branches, this are the outgroup sequences that we will use for rooting. We root those and remove them from the tree.\n\n```{r}\nggtree::ggtree(tree, layout = \"rectangular\") +\n geom_point(. %>% \n filter(label == \"EPI_ISL_177506\" | label == \"EPI_ISL_1254\"),\n mapping = aes(x = x, y = y),\n color = \"green\",\n size = 3)\n```\n\nOk, the two outgroup are more or less overlapped\n\n- rooting the tree, and dropping the two tips that correspond to the long branch\n\n```{r}\nrooted_tree <- ape::root(tree, \n outgroup = c(\"EPI_ISL_177506\", \"EPI_ISL_1254\"),\n resolve.root = T,\n edgelabel = T)\n\nrooted_tree <- \n ape::drop.tip(rooted_tree, c(\"EPI_ISL_177506\", \"EPI_ISL_1254\"))\n\n```\n\nVerification:\n\n```{r}\nggtree::ggtree(rooted_tree, layout = \"rectangular\")\n```\n\n\n## No duplicate sequences to integrate in this tree\n\n- creating table with metadata\n\n```{r}\ntree_meta <- \n tibble::tibble(\"label\" = tree$tip.label) %>%\n # getting metadata for the tips that are duplicated\n dplyr::left_join(all_tree_metadata, c(\"label\" = \"label\")) \n```\n\n- recheck that we only have the correct subtypes included \n```{r}\ntree_meta %>% \n dplyr::group_by(subtype) %>%\n dplyr::summarise(n = n())\n```\n\nAdding filtering columns. We want to show where Norwegian sequences are,\n\nFor this purpose, we add columns that would allow to tag labels representing isolates from Norway, and those that contain White-tailed Eagles.\n\n> Note here could have been simpler, but do as the same script as possible as H5N1_HA\n\n\n```{r}\ntree_meta <- \n tree_meta %>% \n check_in_var(., \"country\", \n name_new_col = \"inNorway\", \n test_expr = rlang::expr(\"Norway\" %in% .x)) %>% \n check_in_var(., \"Host_latin\", \n name_new_col = \"isWTE\", \n test_expr = rlang::expr(\"Haliceetus albicilla\" %in% .x)) %>%\n dplyr::mutate(category_highlight = !!grouping_helper_category_highligth) %>%\n string_levels_var(var = \"year\") %>%\n dplyr::ungroup() %>%\n dplyr::mutate_at(dplyr::vars(category_highlight), \n ~ factor(.x, levels = category_highligth_levels, ordered = T))\n\n```\n\n# Need to rerun the tree ! \n\n```{r}\nmammal_data <- \n tree_meta %>% \n filter(str_detect(Host_latin, \"mammal\")) \n\n```\n\n```{r}\nmammal_data\n```\n\n\n## Exploration of the phylogenetic tree\n\n\n\n```{r}\nexplor_plot1 <- \n rooted_tree %>%\n ggtree::ggtree(., layout = \"rectangular\", ladderize = T, \n lwd = .1, color = \"grey\") %<+%\n tree_meta +\n # Highlight Norwegian isolates\n ggtree::geom_tippoint(aes(\n color = category_highlight, \n shape = levels_year\n ),\n data = . %>% droplevels(),\n position = ggforce::position_auto()) +\n ggplot2::scale_color_manual(values = category_highlight_pal, name = \"Highlight\") +\n ggplot2::scale_shape_manual(values = SEP_years_shape_pal, name = \"Collection\") \nexplor_plot1\n```\n\n\nSo heere I have the solution either I crop the top \nOR I collapse afterwards. \nNow that we have developped the script for collapsing, I prefer to collapse \nless data that is lost \n\n```{r}\nggsave(filename = here::here(params$save_dir, \"explor_plot1.svg\"),\n plot = explor_plot1,\n dpi = 300,\n units = \"cm\",\n width = 20, height = 30) \n```\n\n\n\n# Writing the isolates to remove \n```{r}\nwrite_tsv(mammal_data, \n here(params$save_dir, \n \"mammal_data_to_remove.tsv\")\n )\n```\n\n# Removing the mammals sequences from the MSA\nnew name for the msa file for iqtree : `SEP_NA_H5N1_MSA_nommals.fasta'\n\n- 4 sequences to remove, I did that manually \n\nRerun iqtree \n```{bash} \nconda activate iqtree \niqtree --version \niqtree -s SEP_NA_H5N1_MSA_nommals.fasta -m MFP+F+I -B 1000 -T AUTO\n```"}}},{"rowIdx":570493,"cells":{"sha256":{"kind":"string","value":"462de9287763c0e86f095d738ef8fa7ca14e46bd282abc24d669658eed75fea4"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":7207,"string":"7,207"},"lines":{"kind":"number","value":214,"string":"214"},"content":{"kind":"string","value":"---\ntitle: \"Demonstration of workflow\"\nformat:\n html:\n fig-width: 7\n fig-height: 4\n fig-dpi: 100\n---\n\n```{r}\n#| include: false\nknitr::opts_chunk$set(\n collapse = TRUE,\n comment = \"#>\"\n)\nrlang::local_options(lifecycle_verbosity = \"quiet\")\n```\n\nThe methods below are described in our article\n\n> Larsson, Held, et al. (2024) Reconstructing the regulatory programs\n> underlying the phenotypic plasticity of neural cancers.\n> Nature Communications 15, 9699\n> DOI [10.1038/s41467-024-53954-3](https://doi.org/10.1038/s41467-024-53954-3)\n\nHere we demonstrate the scregclust workflow using the PBMC data from\n10X Genomics (available [here](https://www.10xgenomics.com/resources/datasets/pbmc-from-a-healthy-donor-granulocytes-removed-through-cell-sorting-3-k-1-standard-2-0-0)).\nThis is the same data used in an [introductory vignette](https://satijalab.org/seurat/articles/pbmc3k_tutorial)\nfor the Seurat package. We use [Seurat](https://satijalab.org/seurat/) for\npre-processing of the data.\n\n```{r}\n#| label: load-packages\n#| results: hide\n#| message: false\n\n# Load required packages\nlibrary(Seurat)\nlibrary(scregclust)\n```\n\n# Download the data\n\nWe are focusing here on the filtered feature barcode matrix available as an\nHDF5 file from the website linked above. The data can be downloaded manually\nor using R.\n\nHowever you obtain the data, the code below assumes that the HDF5 file\ncontaining it is placed in the same folder as this script with the name\n`pbmc_granulocyte_sorted_3k_filtered_feature_bc_matrix.h5`.\n\n```{r}\n#| label: download-data\nurl <- paste0(\n \"https://cf.10xgenomics.com/samples/cell-arc/2.0.0/\",\n \"pbmc_granulocyte_sorted_3k/\",\n \"pbmc_granulocyte_sorted_3k_filtered_feature_bc_matrix.h5\"\n)\ndata_path <- file.path(\n tempdir(), \"pbmc_granulocyte_sorted_3k_filtered_feature_bc_matrix.h5\"\n)\n\ndownload.file(url, data_path, cacheOK = FALSE, mode = \"wb\")\n```\n\n# Load the data in Seurat and preprocess\n\nTo perform preprocessing use Seurat to load the data. The file ships with\ntwo modalities, \"Gene Expression\" and \"Peaks\". We only use the former.\n\n```{r}\n#| label: load-h5\npbmc_data <- Read10X_h5(\n data_path,\n use.names = TRUE,\n unique.features = TRUE\n)[[\"Gene Expression\"]]\n```\n\nWe create a Seurat object and follow the Seurat vignette to subset the\ncells and features (genes).\n\n```{r}\n#| label: create-seurat-object\npbmc <- CreateSeuratObject(\n counts = pbmc_data, min.cells = 3, min.features = 200\n)\n\npbmc[[\"percent.mt\"]] <- PercentageFeatureSet(pbmc, pattern = \"^MT.\")\npbmc <- subset(pbmc, subset = percent.mt < 30 & nFeature_RNA < 6000)\n```\n\n[SCTransform](https://satijalab.org/seurat/articles/sctransform_vignette) is\nused for variance stabilization of the data and Pearson residuals for the\n6000 most variable genes are extracted as matrix `z`.\n\n```{r}\n#| label: apply-var-stabilization\npbmc <- SCTransform(pbmc, variable.features.n = 6000)\n\nz <- GetAssayData(pbmc, layer = \"scale.data\")\ndim(z)\n```\n\n# Use scregclust for clustering target genes into modules\n\nWe then use `scregclust_format` which extracts gene symbols from the\nexpression matrix and determines which genes are considered regulators.\nBy default, transcription factors are used as regulators. Setting `mode`\nto `\"kinase\"` uses kinases instead of transcription factors. A list of the\nregulators used internally is returned by `get_regulator_list()`.\n\n```{r}\n#| label: prep-scregclust\nout <- scregclust_format(z, mode = \"TF\")\n```\n\nThe output of `scregclust_format` is a list with three elements.\n\n1. `genesymbols` contains the rownames of `z`\n2. `sample_assignment` is initialized to be a vector of `1`s of length `ncol(z)`\n and can be filled with a known sample grouping. Here, we do not use it and\n just keep it uniform across all cells.\n3. `is_regulator` is an indicator vector (elements are 0 or 1) corresponding to \n the entries of `genesymbols` with 1 marking that the genesymbol is selected\n as a regulator according to the model of `scregclust_format` (`\"TF\"` or\n `\"kinase\"`) and 0 otherwise.\n\n```{r}\n#| label: extract-scregclust-arguments\ngenesymbols <- out$genesymbols\nsample_assignment <- out$sample_assignment\nis_regulator <- out$is_regulator\n```\n\nRun `scregclust` with number of initial modules set to 10 and test\nseveral penalties. The penalties provided to `penalization` are used during\nselection of regulators associated with each module. An increasing penalty\nimplies the selection of fewer regulators.\n`noise_threshold` controls the minimum $R^2$ a gene has to achieve across\nmodules. Otherwise the gene is marked as noise.\nThe run can be reproduced with the command below. A pre-fitted model can be\ndownloaded from [GitHub](https://github.com/scmethods/scregclust/raw/main/datasets/pbmc_scregclust.rds)\nfor convenience.\n\n```{r}\n#| label: run-scregclust\n\n# set.seed(8374)\n# fit <- scregclust(\n# z, genesymbols, is_regulator, penalization = seq(0.1, 0.5, 0.05),\n# n_modules = 10L, n_cycles = 50L, noise_threshold = 0.05\n# )\n# saveRDS(fit, file = \"datasets/pbmc_scregclust.rds\")\n\nurl <- paste0(\n \"https://github.com/scmethods/scregclust/raw/main/datasets/\",\n \"pbmc_scregclust.rds\"\n)\nfit_path <- file.path(tempdir(), \"pbmc_scregclust.rds\")\ndownload.file(url, fit_path)\nfit <- readRDS(fit_path)\n```\n\n# Analysis of results\n\nResults can be visualized easily using built-in functions.\nMetrics for helping in choosing an optimal penalty can be plotted by calling\n`plot` on the object returned from `scregclust`.\n\n```{r}\n#| label: viz-metrics\n#| fig-alt: >\n#| Boxplots of predictive R^2 per module (bottom) and\n#| regulator importance (top) over the penalization parameters\n#| specified during model estimation. A decreasing trend can\n#| be seen in R^2 per module and a slow and steady increase in\n#| regulator importance is followed by an explosive increase from\n#| around 0.4 penalization.\nplot(fit)\n```\n\nThe results for each penalization parameter are placed in a list, `results`,\nattached to the `fit` object. So `fit$results[[1]]` contains the results\nof running `scregclust` with `penalization = 0.1`. For each penalization\nparameter, the algorithm might end up finding multiple optimal configurations.\nEach configuration describes target genes module assignments and which\nregulators are associated with which modules.\nThe results for each such configuration are contained in the list `output`.\nThis means that `fit$results[[1]]$output[[1]]` contains the results for\nthe first final configuration. More than one may be available.\n\n```{r}\n#| label: n-configs\nsapply(fit$results, function(r) length(r$output))\n```\n\nIn this example, at most two final configurations were found for each\npenalization parameters.\n\nTo plot the regulator network of the first configuration for\n `penalization = 0.1` the function `plot_regulator_network` can be used.\n\n```{r}\n#| label: viz-reg-network\n#| fig-width: 7\n#| fig-height: 7\n#| fig-alt: >\n#| Network visualization of modules (colorful circles) and their top\n#| regulators (grey rectangles). Arrows indicate regulation and their\n#| thickness represents regulation strength. Red arrows indicate positive\n#| regulation and blue arrows indicate negative regulation.\nplot_regulator_network(fit$results[[1]]$output[[1]])\n```\n"}}},{"rowIdx":570494,"cells":{"sha256":{"kind":"string","value":"2d6acbc060c3f5172d29aa061252fee9ade543176a33b919459c6a82be02c361"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":7288,"string":"7,288"},"lines":{"kind":"number","value":222,"string":"222"},"content":{"kind":"string","value":"---\ntitle: Trajectories\ndescription: \"Working with multi-frame molecules and molecular dynamics data\"\njupyter: python3\n---\n\nA trajectory is not a special class — it is simply a [](`~mn.Molecule`) whose underlying\n[](`MDAnalysis.Universe`) has more than one frame. Loading, styling and rendering all work\nexactly as they do for a static structure. This page covers the extra behaviour that only\napplies when there is more than one frame: playback, per-frame updates, and colouring by\nvalues computed from a trajectory.\n\n```{python}\n#| warning: false\nimport molecularnodes as mn\nimport MDAnalysis as mda\nfrom MDAnalysis.tests.datafiles import PSF, DCD\nfrom MDAnalysisData import datasets\nfrom MDAnalysis import transformations\nfrom MDAnalysis.analysis import rms, align\nimport numpy as np\nfrom matplotlib import colormaps\n\ncanvas = mn.Canvas(mn.scene.Cycles(samples=16), (800, 800), transparent=True)\n```\n\n## Initial snapshot of the Universe\n\nThe simplest rendering is just loading the `Molecule`, adding a style and then rendering an\nimage — no different from a static structure.\n\n```{python}\n#| warning: false\n#| label: fig-simple\n#| fig-cap: A simple render of the atoms in the `mda.Universe`.\nu = mda.Universe(PSF, DCD)\ntraj = mn.Molecule(u)\ntraj.add_style(\"spheres\")\ncanvas.look_at(traj)\ndisplay(canvas.snapshot())\ncanvas.clear()\n```\n\n## Playback\n\nThe connection to the `Universe` is maintained as the scene frame changes, so the positions\nupdate live during playback. Several properties on the `Molecule` control how frames are\nsampled and interpolated:\n\n- [](`~mn.Molecule.frame`) — the current frame (synced with Blender's scene frame).\n- [](`~mn.Molecule.subframes`) — interpolation steps inserted between frames.\n- [](`~mn.Molecule.offset`) — a frame offset applied during playback.\n- [](`~mn.Molecule.average`) — number of frames to average for smoothing.\n- [](`~mn.Molecule.interpolate`) — enable position interpolation between frames.\n- [](`~mn.Molecule.correct_periodic`) — apply periodic boundary corrections.\n\n```{python}\ntraj = mn.Molecule(u)\ntraj.subframes = 2\ntraj.interpolate = True\n```\n\n## Selections from the trajectory\n\nWe can reset the node tree to clear existing styles, and selectively add them based on some\nselection. The selection can be an `AtomGroup` or a string. If a string, first the existence\nof a `Named Attribute` is checked and if it exists that is used as a boolean to apply the\nselection. If the attribute doesn't exist, the string is used as an MDAnalysis selection\nstring — equivalent to doing `u.select_atoms()` and passing that atom group as a selection.\n\n::: { .callout-note }\n\n# Materials on Point Clouds\n\nThe default geometry for `StyleSpheres()` is `\"Point\"` which uses point cloud rendering \ninside of Blender. It's a limitation of Blender that we can't have multiple materials\non the same point cloud, so we get issues if we try to assign different materials and join\nthem back together. To get around this we have to use the `\"Instance\"` geometry (which can \nbe combined with a point cloud that has a different material).\n\n:::\n\n```{python}\n#| label: fig-selections\n#| fig-cap: Selectively apply the style using an MDAnalysis selection string.\nfrom molecularnodes.nodes import geometry as mg\nfrom nodebpy.nodes import geometry as g\n\nmedian = np.median(traj.atoms.positions, axis=0)\nsel = traj.selections.from_string(f\"prop x > {median[0]}\")\n\nwith traj.tree.reset() as (atoms, join):\n selection = sel.node()\n (\n atoms\n >> mg.StyleSpheres(\n ...,\n selection,\n \"Instance\",\n quality=5,\n material=mn.material.Flat().material\n )\n >> mg.StyleSpheres(\n ...,\n ~selection,\n material=mn.material.Default().material\n ) >> join\n )\n\ncanvas.look_at(traj)\ncanvas.snapshot()\n```\n\n\n```{python}\ntraj.tree\n```\n\nTransformations applied to the `Universe` (such as rotations or unwrapping) carry through to\nthe rendered molecule.\n\n```{python}\n#| warning: false\ncanvas.clear()\n\nrot = [\n transformations.rotate.rotateby(90, direction=[0, 1, 0], point=np.zeros(3))\n ]\nu = mda.Universe(PSF, DCD, transformations=rot)\n\ng2 = mn.Molecule(u).add_style(\"spheres\")\ncanvas.look_at(g2)\ncanvas.snapshot()\n```\n\n## Colouring by computed values\n\nA common trajectory workflow is to compute a per-atom value with an MDAnalysis analysis and\nuse it to colour the render. Here we align the trajectory and compute the per-residue RMSF.\n\n```{python}\nadk = datasets.fetch_adk_equilibrium()\nu = mda.Universe(adk.topology, adk.trajectory)\n\naverage = align.AverageStructure(u, u, select=\"protein and name CA\", ref_frame=0).run()\nref = average.results.universe\naligner = align.AlignTraj(u, ref, select=\"protein and name CA\", in_memory=True).run()\n\nc_alphas = u.select_atoms(\"protein and name CA\")\nR = rms.RMSF(c_alphas).run()\nu.add_TopologyAttr(\"tempfactors\")\nprotein = u.select_atoms(\"protein\")\n\nfor residue, r_value in zip(protein.residues, R.results.rmsf):\n residue.atoms.tempfactors = r_value\n```\n\nCompute a numpy array of color values `(Red, Green, Blue, Alpha)` that we can then store on\nthe mesh object inside of Blender that will be used for coloring in the final render.\n\n```{python}\nviridis = colormaps[\"inferno\"]\ncol_array = u.atoms.tempfactors\ncol_array /= col_array.max()\ncol_array = viridis(col_array)\ncol_array\n```\n\nCreate the `Molecule` object that will be visualised inside of Blender. After initialising\nit, we store the computed colors as a `Named Attribute` on the mesh with the\n`store_named_attribute()` function, or directly by doing `Molecule[\"Color\"] = col_array`. \nWe have direct access to the named attributes on the mesh of the Molecule object, to get \nand set the values. By defualt the Nodes will use the `Color` named attribute for assigning\nthe final color that is used in the render.\n\n```{python}\n# | label: fig-compute-color\n# | fig-cap: \"We can compute custom color values and then use those in the final render.\"\n# | layout-ncol: 2\n# | fig-subcap:\n# | - \"Spheres each use the atom's color.\"\n# | - \"Ribbon and cartoon use the color from the alpha carbon.\"\ncanvas.clear()\ntraj = mn.Molecule(u)\ntraj[\"Color\"] = col_array\ntraj.add_style(\"spheres\")\n\ncanvas.look_at(traj)\ndisplay(canvas.snapshot())\n\nwith traj.tree.reset() as (atoms, join):\n atoms >> mg.StyleRibbon(\n quality=6,\n peptide_radius=1.5,\n material=mn.material.Default().material\n ) >> join\n\ncanvas.look_at(traj)\ncanvas.snapshot()\n```\n\n## A sub-selection of atoms\n\nWhen adding a style, we can ensure it is only applied to some selection of atoms. The\n`selection` can be an `AtomGroup` or a string. In this case we pass an `AtomGroup` and the\nselection is only applied to those atoms from the group.\n\n```{python}\n# | warning: false\ntraj.tree.clear()\ntraj.add_style(\"spheres\", selection=u.atoms[u.atoms.names == \"CA\"], scale=1.75)\n\ncanvas.look_at(traj.get_view())\ncanvas.snapshot()\n```\n\n## Streaming trajectories\n\nA currently-running simulation can be streamed frame-by-frame rather than loaded from disk,\nusing MDAnalysis and IMDClient. See the [](`~mn.entities.StreamingTrajectory`) reference and\nthe [Streaming Trajectories](../tutorials/streaming-trajectories.qmd) tutorial for a full\nwalkthrough.\n"}}},{"rowIdx":570495,"cells":{"sha256":{"kind":"string","value":"50849726d2b1e9a9cefbf3863b51d24d4f235e02dd6953c0a0d355e6ad6c9bcc"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":7521,"string":"7,521"},"lines":{"kind":"number","value":243,"string":"243"},"content":{"kind":"string","value":"---\ntitle: Styles\ndescription: \"Adding styles and representations to molecules\"\njupyter: python3\nexecute:\n daemon: false\n---\n\nStyles (or *representations*) are how we turn the raw atomic data of a [](`~mn.Molecule`)\ninto something we can actually see. Every `Molecule` is styled the same way, whether it is\na single static structure or a multi-frame trajectory.\n\n## Setup\n\n```{python}\n#| warning: false\nimport molecularnodes as mn\nfrom molecularnodes.nodes import geometry as g\nimport MDAnalysis as mda\nfrom MDAnalysis.tests.datafiles import PSF, DCD\n\nu = mda.Universe(PSF, DCD)\ncanvas = mn.Canvas()\n```\n\n## Add a Style\n\nA style can be specified when adding the molecule to Blender. Styles are given as a\nstring, with any style-specific options passed as keyword arguments to\n[](`~mn.Molecule.add_style`).\n\n```{python}\nmol = mn.Molecule(u).add_style(\"ribbon\", quality=4, peptide_radius=0.5)\ncanvas.look_at(mol)\ncanvas.snapshot()\n```\n\nThe [](`~mn.Molecule.add_style`) API takes the following arguments:\n\n```py\nMolecule.add_style(\n style=\"spheres\",\n selection=None,\n material=\"Default\",\n color=None,\n **kwargs,\n)\n```\n\n- `style` param\n - A string, one of `ball_and_stick`, `cartoon`, `ribbon`, `spheres`, `sticks` or\n `surface`; or a callable returning a style node (see\n [Callables](#callables-for-full-control) below).\n- `selection` param\n - An MDAnalysis selection phrase, an `AtomGroup`, the name of an existing boolean\n attribute, or a callable returning a boolean socket. A selection phrase is stored\n as a named attribute and used to mask the style.\n- `material` param\n - One of the pre-built materials from `mn.material`, e.g.\n `mn.material.AmbientOcclusion(distance=0.5)`, a Blender material, or the name of\n a pre-built material, e.g. `Default` or `Squishy`.\n- `color` param\n - `\"common\"` / `\"default\"`, `\"plddt\"`, an RGBA tuple, the name of an existing colour\n attribute, or a callable returning a colour socket. Anything else warns rather than\n silently rendering black.\n- `**kwargs`\n - Any remaining keyword arguments are passed to the style node, e.g. `geometry`,\n `quality`, `scale`, `peptide_radius`. Names that are not inputs on that node raise\n a `TypeError`.\n\n## Selections\n\nEach call to [](`~mn.Molecule.add_style`) appends another style branch to the node tree, so\ndifferent selections of the molecule can be shown with different styles.\n\nAdd `StyleSpheres` to just residues 1 and 129. The string is used as a selection string to\ncreate an `mda.AtomGroup` which is used to create a boolean attribute, and that is used\ninside of the node tree for rendering.\n\n```{python}\nmol.add_style(\"spheres\", sphere=\"Instance\", selection=\"resid 1 129\")\ncanvas.look_at(mol)\ncanvas.snapshot()\n```\n\n```{python}\nmol.add_style(\"surface\", selection=\"resid 100:150\", material=\"Flat\")\ncanvas.look_at(mol)\ncanvas.snapshot()\n```\n\nA selection can also be an `mda.AtomGroup`.\n\n```{python}\nmol.add_style(\"ball_and_stick\", quality=4, selection=u.select_atoms(\"resid 180:200\"))\ncanvas.look_at(mol)\ncanvas.snapshot()\n```\n\n## Callables for Full Control {#callables-for-full-control}\n\n`style`, `selection` and `color` all accept a **callable**, which is evaluated inside the\nnode tree context. This reaches the full node API without having to write out a whole\ntree, and is the recommended middle ground between `add_style` and\n[building the tree yourself](#building-a-style-tree).\n\nA callable `color` reaches any of the `Color*` nodes:\n\n```{python}\ncanvas.clear()\nmol = mn.Molecule.fetch(\"8H1B\")\nmol.add_style(\"cartoon\", color=lambda: g.ColorSecondaryStructure())\ncanvas.look_at(mol)\ncanvas.snapshot()\n```\n\nA callable `selection` composes the selection nodes with `&`, `|` and `~`:\n\n```{python}\nmol.add_style(\"sticks\", selection=lambda: g.IsPeptide() & g.IsSideChain())\ncanvas.look_at(mol)\ncanvas.snapshot()\n```\n\nA callable `style` sets allows easier setting of the values of the node itself.\n\n```{python}\nmol.add_style(\n lambda: g.StyleSpheres(\n sphere=\"Instance\",\n quality=4,\n scale=0.4,\n material=mn.material.Default().material\n )\n )\ncanvas.look_at(mol)\ncanvas.snapshot()\n```\n\nA callable `style` defines the style node completely, so `selection`, `material` and\nstyle keyword arguments cannot be passed alongside it - set them inside the callable\ninstead. To use an MDAnalysis selection phrase there, turn it into a node with\n[](`~molecularnodes.entities.molecule.selections.SelectionManager.node`):\n\n```{python}\ncanvas.clear()\nmol = mn.Molecule.fetch(\"9EYM\")\nmol.add_style(\"cartoon\", color=lambda: g.ColorRainbow())\nmol.add_style(\n lambda: g.StyleSpheres(\n selection=mol.selections.node(\"not protein\"),\n sphere=\"Instance\",\n )\n)\ncanvas.look_at(mol)\ncanvas.snapshot()\n```\n\n`selections.node()` reuses an existing selection where it can, so calling it repeatedly\n(inside a loop, or on every rebuild of a tree) does not pile up duplicate selections:\n\n```{python}\nbefore = len(mol.selections)\nwith mol.tree:\n for _ in range(5):\n mol.selections.node(\"not protein\")\nprint(f\"{before} selection(s) before, {len(mol.selections)} after\")\n```\n\n## Building a Style Tree\n\nTo take full control — or to start from a clean slate — build the node tree yourself with\nthe [](`~mn.Molecule.tree`) context manager. `tree.reset()` clears the existing tree and\nyields the input `atoms` and output `join` sockets, so you can compose exactly the styles\nyou want with their specific parameters. This is the recommended way to create styling\nthat is more complex than simple styles and their selections.\n\nAttempting to expose the branching tree structure of Geometry Nodes via a Python API is\na truly difficult task, and so instead we build the node tree using [](`nodebpy`) as a\nspecialised scripting interface for node trees.\n\n```{python}\nmat = mn.material.AmbientOcclusion()\n\nmol.calculations.dssp(updating=True)\n# `from_string` creates a managed selection; `.node()` gives a node reading its\n# boolean attribute, ready to plug into a style node's `Selection` input\nsel = mol.selections.from_string(\"resname LYS\")\n\nwith mol.tree.reset() as (atoms, join):\n atoms >> g.StyleSticks(selection = sel.node(), scale=0.6, material=mat.material) >> join\n atoms >> g.SetColor(color=g.ColorRainbow()) >> g.StyleCartoon(material=mat.material) >> join\n\ncanvas.look_at(mol)\ncanvas.snapshot()\n```\n\n## Style Gallery\n\nBecause styling is identical across all molecules, we can loop over structures fetched from\nthe PDB and apply a different style to each.\n\n```{python}\ncanvas.clear()\ncodes = [\"4ozs\", \"8H1B\", \"6N2Y\"]\nstyles = [\"cartoon\", \"ribbon\", \"surface\"]\n\nfor code, style in zip(codes, styles):\n mol = mn.Molecule.fetch(code)\n mol.add_style(style, material=\"Flat\")\n canvas.look_at(mol, viewpoint=\"right\")\n display(canvas.snapshot())\n canvas.clear()\n```\n\nStyles compose, so a single molecule can combine several representations with different\nselections and materials.\n\n```{python}\nmol = (\n mn.Molecule.fetch(\"8H1B\")\n .add_style(\"cartoon\", material=\"Ambient Occlusion\")\n .add_style(\n style=\"surface\",\n selection=\"protein\",\n material=\"Default\",\n )\n)\ncanvas.look_at(mol)\ncanvas.snapshot()\n```\n\n```{python}\ncanvas.clear()\ncanvas.engine = mn.scene.Cycles(samples=32)\nmol = (\n mn.Molecule.fetch(\"9EYM\")\n .add_style(\"cartoon\", selection=\"is_peptide\", material=\"Ambient Occlusion\")\n .add_style(\"ball_and_stick\", selection=\"not protein\", material=\"Default\", bond_find=True)\n)\ncanvas.look_at(mol)\ncanvas.snapshot()\n```\n"}}},{"rowIdx":570496,"cells":{"sha256":{"kind":"string","value":"9368139a943bcdef5b9ce5e753979b8c8806ce5cd93e3322718e2f26f474e71e"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":9141,"string":"9,141"},"lines":{"kind":"number","value":363,"string":"363"},"content":{"kind":"string","value":"---\ntitle: \"Preparation Article Aknkowledgments\"\nauthor: \"Eve Zeyl Fiskebeck\"\ndate: \"`r format(Sys.time(), '%d %B, %Y')`\"\nparams:\n aknowledgments_dir: \"data/raw_metadata/acknowledge\"\n metadata_dir: \"data/raw_metadata/metadata\"\n save_dir: \"data/prepared_data\" \nexecute: \n echo: false\n cache: false\nformat: html\nknitr:\n opts_chunk: \n collapse: true\n comment: \"#>\" \n R.options:\n knitr.graphics.auto_pdf: true\neditor_options: \n chunk_output_type: console\n---\n\n```{r setup }\nlibrary(here)\nlibrary(tidyverse)\nlibrary(readxl)\nlibrary(xlsx)\nlibrary(plyr)\n```\n\n\n# Aknowledgments\n\n- [ ] ! the two first lines need to be added again to the final file\n- [ ] Acknowledgments only for the samples and segments we are using\n\nI merged all the different files of the different projects - to be sure nothing missing for the data prep (has been done in so many steps)\n\n## merging all the aknowledgment files \n\n```{r}\naknowledgments_list <- \n list.files(here::here(params$aknowledgments_dir), \n full.names = TRUE,\n recursive = TRUE,\n pattern = \".xls\"\n )\n```\n\n\n\n```{r}\naknowledgments_df <- \n plyr::ldply(\n aknowledgments_list,\n readxl::read_xls,\n col_types = \"text\",\n sheet = \"Acknowledge Table\",\n skip = 2) %>%\n # ensure no white lines \n dplyr::filter_all(., dplyr::any_vars(!is.na(.))) %>%\n # ensure no duplicates\n dplyr::distinct() \n\n# we need to keep names for reinsertion\nold_names <- names(aknowledgments_df)\nnames(aknowledgments_df) <- stringr::str_replace_all(old_names, \" \", \"_\")\n```\n\n## merging all the metadata files \n```{r}\nmetadata_list <- \n list.files(here::here(params$metadata_dir), \n full.names = TRUE,\n pattern = \".xls\",\n recursive = TRUE\n )\n```\n\n\n```{r}\nmetadata_df <- \n plyr::ldply(\n metadata_list,\n readxl::read_xls,\n col_types = \"text\",\n skip = 0) %>%\n # ensure no white lines \n dplyr::filter_all(., dplyr::any_vars(!is.na(.))) %>%\n # ensure no duplicates\n dplyr::distinct()\n\n#renaming for facility \nnames(metadata_df) <- \n stringr::str_replace_all(names(metadata_df), \" Segment_Id\", \"_Id\")\n```\n\n--> \n## overview file formats\n```{r}\naknowledgments_df %>% \n #dplyr::filter(Segment == \"HA\") %>%\n #dplyr::filter(is.na(Segment)) %>%\n head() \n\nmetadata_df %>%\n head() \n```\n\n\n# extracting aknowledgments per tree\n\nFunctions: \n```{r}\nextract_metadata_aknowledgments <- function(aknowledgments_df, tree_metadata_file, sheet, segment){\n \n tree_metadata_df <- readxl::read_xlsx(tree_metadata_file, sheet = sheet)\n \n print(sprintf(\"tree metadata has %s\", nrow(tree_metadata_df)))\n \n tree_aknowledgments <- \n tree_metadata_df %>%\n # to test only Norwegian not in the merged table\n dplyr::rename(country_tree = country) %>%\n dplyr::select(Isolate_Id, country_tree) %>%\n dplyr::distinct() %>%\n dplyr::left_join(aknowledgments_df %>% \n dplyr::filter(!is.na(Segment_ID), Segment == segment) %>%\n dplyr::distinct(), by = c(\"Isolate_Id\" = \"Isolate-ID\"))\n print(sprintf(\"aknowledgment has %s\", nrow(tree_aknowledgments)))\n \n return(tree_aknowledgments)\n\n}\n```\n\n## H5N1 HA tree data - without the norwegian isolates \n\n```{r}\nH5N1_HA_noNo <- \n extract_metadata_aknowledgments(aknowledgments_df, \n tree_metadata_file = here::here(\"results\", \"H5N1_HA_all\", \"H5N1_HA_metadata.xlsx\"), \n sheet = \"H5N1_HA_all_metadata\",\n segment = \"HA\") %>%\n dplyr::filter(country_tree != \"Norway\")%>% \n dplyr::select(-country_tree) \n \n# data check its ok \ndim(H5N1_HA_noNo)\n\nH5N1_HA_noNo %>%\n #filter(Isolate_Id == \"EPI_ISL_16183656\")\n filter(is.na(Country)) \n```\n\n\n```{r}\n# stupid does not like tibbles\nxlsx::write.xlsx(as.data.frame(H5N1_HA_noNo), \n file = here::here(params$save_dir, \"aknowledgments_per_treedata.xlsx\"),\n sheetName = \"H5N1_HA_without_Norway\",\n showNA = FALSE,\n row.names = FALSE,\n append = FALSE)\n \n```\n\n## H5N1 NA tree data - without the norwegian isolates \n```{r}\nH5N1_NA_noNo <- \n extract_metadata_aknowledgments(aknowledgments_df, \n tree_metadata_file = here::here(\"results\", \"H5N1_NA\", \"H5N1_NA_metadata.xlsx\"), \n sheet = \"H5N1_NA_all_metadata\",\n segment = \"NA\") %>%\n dplyr::filter(country_tree != \"Norway\")%>% \n dplyr::select(-country_tree) \n \n# data check its ok \ndim(H5N1_NA_noNo)\n\nH5N1_NA_noNo %>%\n #filter(Isolate_Id == \"EPI_ISL_16183656\")\n filter(is.na(Country)) \n```\n\n\n```{r}\n# stupid does not like tibbles\nxlsx::write.xlsx(as.data.frame(H5N1_NA_noNo), \n file = here::here(params$save_dir, \"aknowledgments_per_treedata.xlsx\"),\n sheetName = \"H5N1_NA_without_Norway\",\n showNA = FALSE,\n row.names = FALSE,\n append = TRUE)\n \n```\n\n## H5N5 NA tree data - without the norwegian isolates \n```{r}\nH5N5_NA_noNo <- \n extract_metadata_aknowledgments(aknowledgments_df, \n tree_metadata_file = here::here(\"results\", \"H5N5_NA\", \"H5N5_NA_metadata.xlsx\"), \n sheet = \"H5N5_NA_metadata\",\n segment = \"NA\") %>%\n dplyr::filter(country_tree != \"Norway\")%>% \n dplyr::select(-country_tree) \n \n# data check its ok \ndim(H5N5_NA_noNo)\n\nH5N5_NA_noNo %>%\n filter(is.na(Country)) \n```\n\n\n```{r}\n# stupid does not like tibbles\nxlsx::write.xlsx(as.data.frame(H5N5_NA_noNo), \n file = here::here(params$save_dir, \"aknowledgments_per_treedata.xlsx\"),\n sheetName = \"H5N5_NA_without_Norway\",\n showNA = FALSE,\n row.names = FALSE,\n append = TRUE)\n \n```\n\n## H5N5 HA tree data - without the norwegian isolates \n```{r}\nH5N5_HA_noNo <- \n extract_metadata_aknowledgments(aknowledgments_df, \n tree_metadata_file = here::here(\"results\", \"H5N5_HA\", \"H5N5_HA_metadata.xlsx\"), \n sheet = \"H5N5_HA_metadata\",\n segment = \"HA\") %>%\n dplyr::filter(country_tree != \"Norway\")%>% \n dplyr::select(-country_tree) \n \n# data check its ok \ndim(H5N5_HA_noNo)\n\nH5N5_HA_noNo %>%\n filter(is.na(Country)) \n```\n\n\n```{r}\n# stupid does not like tibbles\nxlsx::write.xlsx(as.data.frame(H5N5_HA_noNo), \n file = here::here(params$save_dir, \"aknowledgments_per_treedata.xlsx\"),\n sheetName = \"H5N5_HA_without_Norway\",\n showNA = FALSE,\n row.names = FALSE,\n append = TRUE)\n \n```\n\n# Aknowledgments for the preparation files for cropping and aligment \n\n\n```{r}\nlibrary(\"seqinr\")\n```\nWe used the genotype and representative sequence for the initial alignment -> this is the longest file where all need to be aknowledged\n\n\n# HA \n\n\n```{r}\nHA_repr_file <- here::here(\"results\", \"intermediary_files_copy\", \"HA_repr_ref_inframe_extended.fasta\")\nHA_repr <- read.fasta(HA_repr_file)\n\nnames_HA_repr <- unlist(getAnnot(HA_repr)) %>%\n str_replace_all(., \">.*EPI ISL \", \"EPI_ISL_\")\n \nnames_HA_repr\n```\n\nNow we need to retrieve the IDS\n- create df \n\n\n```{r}\nHA_repr_df <- \n tibble::tibble(\"Isolate-ID\" = names_HA_repr) \n\ndim(HA_repr_df %>% distinct())\n```\n\ngetting acknowledgments\n```{r}\nhead(aknowledgments_df)\n\n\nHA_aknowledgments <- \n HA_repr_df %>% \n dplyr::distinct() %>%\n dplyr:::left_join(aknowledgments_df %>%\n dplyr::filter(Segment == \"HA\") %>%\n distinct()) \n```\n\n\n```{r}\nxlsx::write.xlsx(as.data.frame(HA_aknowledgments), \n file = here::here(params$save_dir, \"aknowledgments_preparation_files.xlsx\"),\n sheetName = \"HA_prep_files\",\n showNA = FALSE,\n row.names = FALSE,\n append = FALSE)\n \n```\n\n\n## NA \n\n\n```{r}\nNA_repr_file <- here::here(\"results\", \"intermediary_files_copy\", \"HA_repr_ref_inframe_extended.fasta\")\nNA_repr <- read.fasta(NA_repr_file)\n\nnames_NA_repr <- unlist(getAnnot(NA_repr)) %>%\n str_replace_all(., \">.*EPI ISL \", \"EPI_ISL_\")\n \nnames_NA_repr\n```\n\nNow we need to retrieve the IDS\n- create df \n\n\n```{r}\nNA_repr_df <- \n tibble::tibble(\"Isolate-ID\" = names_NA_repr) \n\ndim(NA_repr_df %>% distinct())\n```\n\ngetting acknowledgments\n```{r}\nNA_aknowledgments <- \n NA_repr_df %>% \n dplyr::distinct() %>%\n dplyr:::left_join(aknowledgments_df %>%\n dplyr::filter(Segment == \"NA\") %>%\n distinct()) \n\ndim(NA_aknowledgments)\n```\n\n\n```{r}\nxlsx::write.xlsx(as.data.frame(NA_aknowledgments), \n file = here::here(params$save_dir, \"aknowledgments_preparation_files.xlsx\"),\n sheetName = \"NA_prep_files\",\n showNA = FALSE,\n row.names = FALSE,\n append = TRUE)\n \n```\n\n# Editing manually \nAdding the first 2 lines acknowledgments manually \nand the colnames\n"}}},{"rowIdx":570497,"cells":{"sha256":{"kind":"string","value":"ed50511273a28b69d5540af9b28724b5631c773086ffd2b36559dcd6f1f2b103"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":9354,"string":"9,354"},"lines":{"kind":"number","value":129,"string":"129"},"content":{"kind":"string","value":"---\ntitle: Blender's Interface\nalasses:\n - \"00_interface.html\"\n---\n\nBlender has a _very_ overwhelming interface. There is no getting around this. Blender can do _practically anything_, and it is sometimes hard to do simple things.\n\n### A New File\n\nStarting a new file, you are greeted by the splash screen. You can click `General` to start a new session with a general-purpose setup.\n\nThis 3D viewport will seem familiar if you have used other programs such as PyMol, ChimeraX & VMD. You can move the camera around just like in those other programs.\n\nIn the 3D viewport, much like in real life, there are three (3) axes of possible movement and direction. Inside Blender are `X`, `Y`, and `Z`. The floor is made of `X` and `Y`, which form the flat grid you see in the viewport coloured red and green/yellow, while `Z` is the blue vertical axis. These are labelled and highlighted on the axis widget, which is present in the top right corner of the viewport. This will rotate as you rotate the view, so it can always be used as a reference point.\n\n\n\nYou can rotate the camera view either using the middle mouse button <kbd>MMB</kbd> or using your touchpad if you are on a laptop. You can also click and drag the axis widget to rotate the camera.\n\nYou can pan the camera using <kbd>Shift</kbd> + <kbd>MMB</kbd>\n\n\n\n## Transforming Objects\n\nThe main difference is that you can also manipulate the 3D scene in front of you. You can select objects with a left click of the mouse <kbd>LMB</kbd>, and move them around by *Grabbing* them with the <kbd>G</kbd> key.\n\nThe main actions that you use the 3D Viewport for are:\\\n\n- <kbd>G</kbd> - **Grabbing:** Moving an object around in 3D space.\n\n- <kbd>S</kbd> - **Scaling:** Changing the relative size of an object.\n\n- <kbd>R</kbd> - **Rotating:** Rotating the object in 3D space.\n\n\n\n### Locking to an Axis\n\nWhen transforming by grabbing, rotating or scaling, you can lock the transformation to a particular axis. Click <kbd>X</kbd> / <kbd>Y</kbd> / <kbd>Z</kbd> after starting the transformation to lock it to those axes, or <kbd>Shift</kbd> + <kbd>X</kbd> / <kbd>Y</kbd> / <kbd>Z</kbd> to lock the transformation to be *perpendicular* to that axis.\n\n\n\n## Rendered View\n\nBy default you start in `3D View`, which is characterised by everything being gray and not rendered properly.\n\nYou can change to the different views via the render view buttons, or by holding <kbd>Z</kbd> and selecting one of the options.\n\nEach of the views are useful in their own way. If you have a powerful enough computer, you can spend the majority of your time in either `Rendered` or `Material Preview` view which will ensure everything is shaded and lighting is calculated. If a scene is becoming complex and your computer is slow, you can switch back to `3D View` which should improve performance.\n\n\n\n## Rendering Engines\n\nAlready there are different ways to view the scene, through the 'rendered view' and the other methods. Even with the option of rendered view, there are multiple different rendering engines. Two rendering engines come pre-installed with Blender, with the possibility to use more. The two rendering engines that come pre-installed are Eevee and Cycles. By default Eevee is enabled, but you can change to Cycles in the `Render Properties` tab.\n\nEevee is much faster and more responsive than Cycles, but a lot of the time doesn't 'look as nice'. Eevee is a real-time rendering engine built for performance and interactivity, while Cycles is a path-traced rendering engine built for physically accurate light calculations. This is much slower to calculate but often results in 'nicer' lighting. Both rendering engines are excellent and are helpful in different scenarios depending on the desired outcomes.\n\nThe default style `Atoms Cycles` inside of Molecular Nodes is only visible inside of Cycles for performance reasons. All other styles are visible inside of both rendering engines. If your atoms seem to be invisible, check the rendering engine and style that you are using.\n\n> If you have the option available, ensure to switch to `GPU Compute` instead of `CPU` when using the Cycles render engine. This will usually always be the better option, and the perforamnce will be much better.\n\n\n\n## Adding Objects\n\nYou can add new objects by <kbd>Shift</kbd> + <kbd>A</kbd> or by using the `Add` menu in the top left.\n\nThere are a number of different object types that can be added. The most common that you will use will be `Mesh` and `Light`. Try adding some new objects, _**G**rabbing_ them to move them around, _**R**otating_ them and _**S**caling_ them to different sizes. If you have just added a new object and it isn't immediately visible, it might be inside another object, or it could be *too big* or *too small* to be immediately visible. Try moving the camera or the object around to get a better view.\n\n\n\n\n\n## Rendering an Image\n\nTo render an image, you can click <kbd>F12</kbd> or use the `Render -> Render Image` menu. Blender will not render what you are immediately seeing. Instead, it will render whatever the **Camera**** is seeing. This can be frustrating at first, but it makes sense when you can have multiple cameras in a single scene.\n\nTo see what the camera sees, you can click the Camera widget in the top right, or click backtick (<kbd>`</kbd>) + <kbd>1</kbd>.\n\n> The backtick is directly under the <kdb>Esc</kbd> key on most American / Enlgish keyboards. It can vary in other language layouts.\n\n\n\nOnce you are happy with the camera framing, click render.\n\n\n\nA window should appear, showing the rendered image. Isn't it magnificent?\n\nThe rendered image is not saved. You have to manually save the image by clicking `Image` -> `Save As` and choose where to save the image.\n\n\n\n## Add More Lights\n\nWe can make our 3D scene more interesting by adding more lights. When the light object is selected, the green `Light Settings` tab becomes available, appearing as a small green lightbulb. Under these settings, we can change the colour of our light, along with the `Power`. Try tweaking these settings to your liking. You can duplicate an object without adding a new one by clicking <kbd>Shift</kbd> + <kbd>D</kbd>.\n\n\n\n## Materials\n\nOne of the hardest things to get used to while working in 3D is the idea of materials. What if we want to make the monkey head **Red**? In 3D we can't just _make things red_. Instead we have to assign it a _material_. This material can have the property that it is primarily red, but it is also metallic, has a sheen, reflects light, can be partially transparent as well as a whole range of other properties.\n\nComplex shading is an entire career itself, so we won't get too much in to it. In this example we will just add a material to the monkey to make it red, and a different material to the cube to make it blue.\n\n> If you can't see the colors changing, ensure you are in Rendered view.\n\nIn the example below, we create a _new material_ called `Material.001` for the monkey which we change to red. The cube already has a material called `Material`. We change the material being used to the monkey's material, before changing it back and editing the starting material so that it is blue.\n\nTry playing around with some of the other material settings, to see what effects you can create.\n\n\n\n## Edit Mode\n\nThe last thing to be aware of when working inside of a 3D program like Blender, is that you can manually edit the 3D objects that are inside of the scene. In the top left hand corner of the 3D viewport. With an object selected, you can change between the different modes. You can edit the physical shape of a mesh, by moving individual or groups of vertices, by going into `Edit Mode`. You can also use the keyboard shortcut <kbd>Tab</kbd>\n\n\n\nWith a mesh object selected, you can change in to edit mode. While in edit mode, different options are available to you, and you can phsyically edit the mesh. Try selecting some vertices of the mesh and moving them around individually.\n\n\n\nYou now know the _very basics_ of adding new objects, changing the materials, adding some lights and rendering an image. The same principles apply to everything that you you do in Blender, and often all of the different workspaces will contain similar keyboard shortcuts and ways of working. Try playing around some more inside of the 3D scene, rendering some different 3D images, before moving on to the section which includes actual proteins.\n\nMost things are best learnt by doing, so load some proteins in and try to follow along with the other tutorials.\n"}}},{"rowIdx":570498,"cells":{"sha256":{"kind":"string","value":"27546f85ff6d62104b4e0453b36ee4ea90a986a2889010d871a33d7c6ce8ddfc"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":10063,"string":"10,063"},"lines":{"kind":"number","value":144,"string":"144"},"content":{"kind":"string","value":"---\ntitle: Fetch from the PDB\nfig-align: center\naliases:\n - \"01_importing.html\"\n---\n\nMolecules, simulations and other file formats can all be imported via the `Molecular Nodes` subpanel. This panel can be found under the `Scene Properties` panel. This is the small cone and spheres icon on the right. This contains all of the different options for importing molecular data currently available inside of Molecular Nodes.\n\n\n\nYou can download a structure directly from the PDB, just from the accession code. Molecular Nodes will download the file as a `.bcif` file and cache it locally on your computer, then open the file in to Blender. This is the quickest way to import structures that are already available via the wwPDB. Currently this downloads via the RCSB.\n\nTo download directly from the PDB, you can use the <kbd>PDB</kbd> tab.\n\n## PDB Tab\n\nIn this tab you can input the PDB ID to be used for downloading the structure. You can also change where Molecular Nodes caches the downloaded structures. By default it will be in a hidden `.MolecularNodes` folder in your home directory.\n\n\n\nType in the code, choose your import options such as starting style, and click download. The new object will appear in the outliner. It will appear inside of the `MolecularNodes` collection and should have the name of the PDB ID used to fetch the structure. If an object already exists with that name, it will be appended with a `.001` or similar suffix.\n\nThe molecule may appear inside of the default cube, so you may have to delete it before the protein becomes visible.\n\n\n\n## Edit Mode\n\nThe protein is imported as a 3D mesh, with a vertex for each atom and an edge for each bond. What you are seeing is the result of passing that data through a geometry-processing pipeline. The underlying data is still available, and you can even look at it (and edit it if you wish) by going using edit mode with <kbd>Tab</kbd>\n\nYou are editing the _underlying_ atomic data, not the cartoon itself. The cartoon is rebuilt on top of the edited atomic data, which is why part of it also moves with the atoms when they are moved.\n\nIt is probably ill-advised to manually edit the atomic data by hand, but it is important to understand the structure of the data inside of Blender & Molecular Nodes.\n\n\n\n## Rendered Views\n\nWhile the molecule is imported, we are in 3D view by default. We can change to a rendered view to view the molecule with more accurately calculated lighting effects.\n\n\n\n## Changing Styles\n\nThe protein is now imported in the scene. To change the way the protein is displayed, we have to use the `Geometry Nodes` editor. This is where we change the nodes that are use to process the underlying atomic data, resulting in the final 3D object we can see in front of us.\n\nTo access `Geometry Nodes` panel, we can either select the `Geometry Nodes` workspace, or split the current viewport and change one of the workspaces into the node editor.\n\n\n\nYou can change the workspace through the tabs in the top of Blender. You can split workspaces by hovering over the corner of a workspace, and then clicking and dragging. You can merge to workspaces by clicking and dragging from one into another, while dragging into the same workspace will split it and create two workspaces.\n\n\n\nGeometry node trees are explained more in the next section.\n\nIn short, the atomic data comes through the `Group Input` on the left, and the final 3D model goes out the `Group Output` to the right. Each node in between performs some actions on the data. The first node changes the colours of the atoms. The last node applies the given style. When importing we chose the `Cartoon` style default, so we have a cartoon node.\n\nWe can add a new node through the `Add` menu, or we can use <kbd>Shift</kbd> + <kbd>A</kbd> to add new nodes. We can click and drag on the noodle outputs and inputs to remove and create new connections. Notice how when the complete 'circuit' or 'flow diagram' goes through the ball and stick node, the style changes to ball and stick. When it flows through the cartoon node, the style changes to cartoon.\n\n\n\n## Geometry Nodes Editor\n\nThis is where we edit the node graph for Molecular Nodes. The node graph is a geometry processing program you create, that is read and executed by Blender from left to right. Each node performs an action on the geometry that is passed into it, and the result of that action is passed out to the right. Think of it as creating a set of rules for the computer to follow. You define the rules, and the computer can follow those same rules, even if you start with a different data set.\n\nThe data flows like a river, along the green noodles of the node graph. There must be only 1 final output. If you are using the molecular data, then there must also be a single input.\n\n\n\nThe data flows from left to right, like water through a river.\n```{mermaid}\nflowchart LR\n A{Atomic\\nData} --> B[Manipulation]\n B --> D(Style)\n D --> G{Geometry\\nOutput}\n```\n\n\n\nThe starting style `MOL_style_atoms_cycles` is only visible via inside of the Cycles render engine. You can add other styles manipulate the data through other nodes, by adding them with <kbd>Shift</kbd> + <kbd>A</kbd> and navigating to the MolecularNodes panel at the bottom. There are several categories of nodes for different animations and styles. You can add the `Ribbon Protein` node, which will create a ribbon representation based on the alpha carbons in the structure. If there is not colour in the structure, ensure that the node has a material `MOL_atomic_material` at the bottom of the node.\n\n\n\nThe data flows through the node graph from left to right. There should always be one input, which is the atomic data that is stored in the 3D mesh of the atoms and bonds. There should also be only one output, which is the final geometry that has been created throughout the node graph.\n\n```{mermaid}\n\nflowchart LR\n A{Atomic\\nData} --> B[Manipulation]\n\n B --> D(Style 1)\n B --> E(Style 2)\n B --> S(Style 3)\n\n sS[Selection] --> S\n\n E --> F[Join\\nGeometry]\n D --> F\n S --> F\n\n F --> G{Geometry\\nOutput}\n```\n\nAs demonstrated in the diagram, the flow of the data can split across multiple different branches of the tree, with the result of each parallel computation being combined before being exported.\n\nEach of these different branches can be a different style, in this example shown two different styles are applied, and the third style has a custom selection. All of the styles are the result of the same data manipulation.\n\nThe node graph in between can be as complex as you want to make it, as long as there is a continuous connection to the output of the geometry, it should produce a result.\n\n{width=\"500px\" width=\"300\"}\n\nCombining multiple different nodes you can create protein models and complex 3D scenes. In the example below, there is a node tree which should be present on every new structure imported via MolecularNodes. The atoms and bonds are passed in from the left, the atoms are given a colour based on their element and their `chain_id`, and then the atoms are styled as atoms, scaling the atom radii to `1.00`.\n\nThe data flows through the node tree from left to right, along the bright green lines. The atomic data comes in, goes through layers of manipulation, a style is applied, and the created geometry is then outputted from the node tree.\n\n\n\n### Editing the Styles\n\nTo change how each style looks, you can edit the default values of the nodes. By changing the values, you change the numbers that are used in the creation of the 3D models. The style will only change if it is being output through the node tree. You can click and drag to change the values smoothly. You can hold <kbd>Shift</kbd> while dragging the values to change them more accurately.\n\n\n\n## Combining Styles\n\nTo complete this tutorial, we will combine the use of `Selection` and `Join Geometry` to create a style which shows the side chains as *ball and stick* while also showing the cartoon style.\n\nThe style should usually be the last node that you apply to a model. You can't apply a style after another style, as the output of a style is 3D geometry and not 'atom-like' geometry. Instead, we can combine two styles with a `Join Geometry` node. That way, we create both styles in parallel and then join them together before outputting them all through the `Group Output` node.\n\nThe node can be found under `Geometry` or by going through the search for `Join Geometry`\n\n\n\n## Selections\n\nMany nodes will have a `Selection` input, which you can't manually edit. This input is a `Boolean` attribute and says whether or not to apply an operation to atoms based on if the value is `True` or `False`.\n\nYou can use selections via the `Molecular Nodes` -\\> `Selection` menu. In this example we use the `Backbone` node to select atoms based on if they are part of the backbone, the side chain, or just alpha carbons.\n\nYou can see as we connect different outputs from the `Backbone` node, we get different results in the final style.\n\n\n"}}},{"rowIdx":570499,"cells":{"sha256":{"kind":"string","value":"104e381819f7def0816b9856085f43379cdc87bfc1a120b17427f9ce01c76880"},"language":{"kind":"string","value":"Quarto"},"size":{"kind":"number","value":14004,"string":"14,004"},"lines":{"kind":"number","value":495,"string":"495"},"content":{"kind":"string","value":"---\ntitle: \"Sampling distribution check\"\nauthor: \"Eve Zeyl Fiskebeck\"\ndate: \"`r format(Sys.time(), '%d %B, %Y')`\"\nparams:\n sampling_data: \"data/raw_metadata/HPAI_all_samples20201001_20230815_dev.xlsx\"\n save_dir: \"results\"\n source_code: 'code/R'\nexecute: \n echo: false\n cache: false\nformat: html\nknitr:\n opts_chunk: \n collapse: true\n comment: \"#>\" \n R.options:\n knitr.graphics.auto_pdf: true\neditor_options: \n chunk_output_type: console\n---\n\n# Setup\n\n- sourcing scripts and functions\n\n```{r setup}\n#| include: false\nlibrary(here)\nsource(here::here(params$source_code, \"AI_main.R\"))\n\n# Do not need that here I think\n# fun_files_path <- list.files(here::here(params$source_code, \"fun\"),\n# pattern = \"*.R\",\n# full.names = T)\n# # sourcing all functions\n# for (i in 1:length(fun_files_path)) {\n# print(fun_files_path[i])\n# source(fun_files_path[i])\n# }\n\n```\n\n# Getting the data \n## Reading file \n```{r}\nsampling_data <- xlsx::read.xlsx(here::here(params$sampling_data), sheetIndex = 1)\n```\n\n## wrangling data \n\n```{r}\nsampling_df <- \n sampling_data %>%\n dplyr::rename(PJS = Saksnr, Received_date=Mottatt, Diagnostic = Konklusjon, Species = Art) %>%\n dplyr::select(PJS, Received_date, Diagnostic, Species) %>%\n dplyr::mutate(Received_date = lubridate::ymd(Received_date))\n```\n\n```{r}\nlevels(as_factor(sampling_df$Diagnostic))\n```\n\n\n```{r}\nsampling_df <- \n sampling_df %>%\n dplyr::mutate(Diagnostic = \n case_when(\n Diagnostic == \"Ikke påvist Influensa A\" ~ \"Neg.\",\n Diagnostic == \"Påvist andre Influensa A\" ~ \"Other/unknown type HPAI\",\n Diagnostic == \"Uegnet\" ~ \"Not Poss. / Not done / Other pathogen\",\n Diagnostic == \"Annet\" ~ \"Not Poss. / Not done / Other pathogen\",\n Diagnostic == \"Påvist H5Nx\" ~ \"Other/unknown type HPAI\",\n Diagnostic == \"Påvist H5N8\" ~ \"Other/unknown type HPAI\",\n Diagnostic == \"Påvist HPAI H5N8\" ~ \"Other/unknown type HPAI\",\n Diagnostic == \"Påvist H5N1\" ~ \"H5N1\",\n Diagnostic == \"Påvist HPAI H5N1\" ~ \"H5N1\",\n Diagnostic == \"Påvist HPAI H5Nx\" ~ \"Other/unknown type HPAI\",\n Diagnostic == \"Påvist HPAI H5N5\" ~ \"H5N5\",\n Diagnostic == \"Påvist H7Nx\" ~ \"Other/unknown type HPAI\",\n Diagnostic == \"Mottatt, ikke avsluttet\" ~ \"Not Poss. / Not done / Other pathogen\",\n TRUE ~ NA_character_\n )) %>% \n dplyr::mutate(Abbr = \n case_when(\n Diagnostic == \"Neg.\" ~ \"Neg.\",\n Diagnostic == \"Other/unknown type HPAI\" ~ \"Div. HPAI\",\n Diagnostic == \"Not Poss. / Not done / Other pathogen\" ~ \"Other\",\n Diagnostic == \"H5N1\" ~ \"H5N1\",\n Diagnostic == \"H5N5\" ~ \"H5N5\",\n TRUE ~ NA_character_\n ))\n\n \n \n\n```\n\n```{r}\nlevels(as_factor(sampling_df$Species))\n```\n\n\n```{r}\nsampling_df <- \n sampling_df %>% \n dplyr::mutate(Species_type = case_when(\n Species == \"Havørn\" ~ \"Bird of prey\",\n Species == \"Kongeørn\" ~ \"Bird of prey\",\n Species == \"Spurvehauk\" ~ \"Bird of prey\",\n Species == \"Rovfugler\" ~ \"Bird of prey\",\n Species == \"Hønsehauk\" ~ \"Bird of prey\",\n Species == \"Haukefamilien\" ~ \"Bird of prey\",\n Species == \"Rev (rødrev)\" ~ \"Mammal\",\n Species == \"Kattugle\" ~ \"Bird of prey\",\n Species == \"Fiskeørn\" ~ \"Bird of prey\",\n Species == \"Hvalross\" ~ \"Mammal\",\n Species == \"Steinkobbe (fjordsel)\" ~ \"Mammal\",\n Species == \"Tårnfalk\" ~ \"Bird of prey\",\n Species == \"Hornugle\" ~ \"Bird of prey\",\n Species == \"Svin\" ~ \"Mammal\",\n Species == \"Spurveugle\" ~ \"Bird of prey\",\n Species == \"Uglefamilien\" ~ \"Bird of prey\",\n Species == \"Seihval\" ~ \"Mammal\",\n TRUE ~ \"Other Avian\"\n )) %>%\n dplyr::mutate(WTE = case_when(\n Species == \"Havørn\" ~ \"WTE\",\n Species_type == \"Mammal\" ~ \"Mammal\",\n TRUE ~ \"Other Avian\"\n )\n )\n \n```\n\n\ncreating intervals and binning for the dates\n\n```{r}\nrange(sampling$Received_date)\n```\n\n```{r}\nmonths_v <- seq.Date(from = min(sampling_df$Received_date), to = max(sampling_df$Received_date) + months(1), by = \"month\")\n\nmonths_df <- \n tidyr::tibble(\"start_date\" = months_v) %>%\n dplyr::mutate(end_date = lead(start_date - days(1), default = NULL)) %>%\n dplyr::mutate(end_date = lead(start_date - days(1), default = NULL)) %>%\n dplyr::mutate(date_interval = lubridate::interval(start_date, end_date)) %>%\n dplyr::filter(!is.na(date_interval)) %>%\n tibble::add_column(interval_number = seq(1, nrow(.)))\n \n#View(months_df)\n```\n\nnow creating option to join the data within the intervals \nCreate a function that look up if the sample is within an interval, if so add the interval number to the data \n\n```{r}\nhead(sampling)\n```\n\n```{r}\nwhich_interval <- function(received_date, interval_df) {\n interval_index <- which(received_date %within% interval_df$date_interval)\n return(interval_df$interval_number[interval_index])\n \n}\n\n# tEST\nwhich_interval(sampling_df$Received_date[1], months_df)\nsampling_df$interval_number <- unlist(lapply(sampling_df$Received_date, which_interval, months_df))\n```\n\n\n```{r}\nsampling_df <- \n sampling_df %>% \n dplyr::left_join(months_df, by = c(\"interval_number\" = \"interval_number\")) \n```\n\n```{r}\nsampling_df <- \n sampling_df %>%\n mutate(interval = as.character(date_interval)) \n```\n\n```{r}\nlevels(as.factor(sampling_df$Abbr))\n```\n\n```{r}\nsampling_df <- \n sampling_df %>%\n mutate(\n HPAI_all = case_when(\n Abbr == \"H5N1\" ~ \"YES\",\n Abbr == \"H5N5\" ~ \"YES\",\n Abbr == \"Neg.\" ~ \"NO\",\n Abbr == \"Other\" ~ \"NO\",\n Abbr == \"Div. HPAI\" ~ \"YES\",\n TRUE ~ \"NO\"\n ))\n```\n\n\ngetting the interval in ym for visualisation \n\n```{r}\nsampling_df <- \n sampling_df %>% \n arrange(start_date) %>%\n mutate(year = as.character(year(start_date)),\n month = as.character(month(start_date))) %>% \n unite(year_month, year, month, sep = \"-\") %>%\n mutate(year_month = factor(year_month, \n ordered = TRUE, \n levels = unique(.$year_month)\n ))\n \n \n \n```\n\n\n## plotting \n```{r}\nsampling_plot <- \n sampling_df %>% \n ggplot(aes(x = year_month, \n fill = WTE)) +\n geom_bar(stat = \"count\") +\n expand_limits(x = c(-2, length(levels(sampling_df$interval))+2)) +\n scale_fill_manual(values = c(\"WTE\" = \"deeppink\", \"Mammal\" = \"grey\", \"Other Avian\" = \"blue\")) +\n theme_bw() +\n theme(axis.text.x = element_text(angle = 45, hjust = 1)) +\n labs(title = \"Number of samples per date\", \n x = \"Date\", \n y = \"Count\", \n fill = \"Type Sample\") \n\nsampling_plot \n```\n\n\nneed to get the factors \n\n```{r}\nsampling_postive_negative_plot <- \n sampling_df %>% \n ggplot(aes(x = year_month, \n fill = WTE, alpha = HPAI_all)) +\n geom_bar(stat = \"count\") +\n expand_limits(x = c(-2, length(levels(sampling_df$interval))+2)) +\n scale_fill_manual(values = c(\"WTE\" = \"deeppink\", \"Mammal\" = \"grey\", \"Other Avian\" = \"blue\")) +\n scale_alpha_manual(values = c(\"NO\"= 0.3, \"YES\"= 1), name = \"Influenza Positive\") +\n theme_bw() +\n theme(axis.text.x = element_text(angle = 45, hjust = 1)) +\n labs(title = \"Positive vs AI negative & total sampling\", \n x = \"Date\", \n y = \"Count\") \n\nsampling_postive_negative_plot \n```\n\n\n```{r}\nsampling_postive_negative_plot2 <- \n sampling_postive_negative_plot +\n facet_grid(rows = vars(HPAI_all), scales = \"free\")\n\nsampling_postive_negative_plot\n```\n\n```{r}\ntest1 <- sampling_postive_negative_plot / sampling_postive_negative_plot2\ntest1\n```\n\n\n```{r}\nggsave(here(params$save_dir, \"sampling_pos_neg_detail.png\"),\n plot = test1,\n width = 15,\n height = 10)\n```\n\nI want only the positive and ordered - with the different influenza in same scale \n```{r}\nsampling_df %>%\n add_count(interval) %>%\n pull(n) %>%\n range(.)\n# 19 427 \n```\n\n\ncreating the positive and the interaction \n\n```{r}\nsampling_positive_interaction_df <- \n sampling_df %>% \n filter(HPAI_all == \"YES\") %>%\n unite(Interaction, c(\"WTE\", \"Abbr\"), sep = \": \", remove = F) \n```\n\n\n\n```{r}\nggplot(sampling_positive_interaction_df, \n aes(x = year_month, \n fill = Interaction, \n color = Interaction\n ) \n ) +\n geom_bar(stat = \"count\") +\n expand_limits(x = c(-2, length(levels(sampling_df$interval))+2))+\n scale_fill_manual(values = c(\"WTE: H5N1\" = rgb(1,0,1,1),\n \"WTE: H5N5\" = rgb(0.5,0,0.5, 0.5),\n \"WTE: Div. HPAI\" = rgb(0.5,0,0.5, 0.2),\n \"Mammal: H5N1\" = rgb(.5,.5,.5,1),\n \"Other Avian: H5N1\" = rgb(0,0,1, 1),\n \"Other Avian: H5N5\" = rgb(0,0,1,0.5),\n \"Other Avian: Div. HPAI\" = rgb(0,0,1,0.2)\n ),\n name = \"Types positive Influenza A\") +\n ggplot2::scale_color_manual(values = c(\"WTE: H5N1\" = rgb(1,0,1,1),\n \"WTE: H5N5\" = rgb(0.5,0,0.5, 0),\n \"WTE: Div. HPAI\" = rgb(0,0,0,1),\n \"Mammal: H5N1\" = rgb(.5,.5,.5,1),\n \"Other Avian: H5N1\" = rgb(0,0,1,1),\n \"Other Avian: H5N5\" = rgb(0,0,1,0),\n \"Other Avian: Div. HPAI\" = rgb(0,0,0,1)\n ),\n name = \"Types positive Influenza A\") +\n theme_bw() +\n theme(axis.text.x = element_text(angle = 45, hjust = 1)) +\n labs(title = \"Positive AI & types incl. unknown\", \n x = \"Date\", \n y = \"Count\") \n```\n\nfacet for that \n```{r}\npositive_AI <- \n ggplot(sampling_positive_interaction_df, \n aes(x = year_month, \n fill = Interaction, \n color = Interaction\n ) \n ) +\n geom_bar(stat = \"count\", position = \"dodge\") +\n expand_limits(x = c(-2, length(levels(sampling_df$interval))+2))+\n scale_fill_manual(values = c(\"WTE: H5N1\" = rgb(1,0,1,1),\n \"WTE: H5N5\" = rgb(0.5,0,0.5, 0.5),\n \"WTE: Div. HPAI\" = rgb(0.5,0,0.5, 0.2),\n \"Mammal: H5N1\" = rgb(.5,.5,.5,1),\n \"Other Avian: H5N1\" = rgb(0,0,1, 1),\n \"Other Avian: H5N5\" = rgb(0,0,1,0.5),\n \"Other Avian: Div. HPAI\" = rgb(0,0,1,0.2)\n ),\n name = \"Types positive Influenza A\") +\n ggplot2::scale_color_manual(values = c(\"WTE: H5N1\" = rgb(1,0,1,1),\n \"WTE: H5N5\" = rgb(0.5,0,0.5, 0),\n \"WTE: Div. HPAI\" = rgb(0,0,0,1),\n \"Mammal: H5N1\" = rgb(.5,.5,.5,1),\n \"Other Avian: H5N1\" = rgb(0,0,1,1),\n \"Other Avian: H5N5\" = rgb(0,0,1,0),\n \"Other Avian: Div. HPAI\" = rgb(0,0,0,1)\n ),\n name = \"Types positive Influenza A\") +\n theme_bw() +\n theme(axis.text.x = element_text(angle = 45, hjust = 1)) +\n labs(title = \"Positive AI & types incl. unknown\", \n x = \"Date\", \n y = \"Count\") +\n facet_grid(rows = vars(WTE), scales = \"free\") \n\npositive_AI\n```\n\n\n```{r}\nggsave(here::here(params$save_dir, \"positive_AI.png\"), \n positive_AI, width = 20, height = 15)\n\n\n```\n\n\n```{r}\npositive_H5N1_5_plot <- \n sampling_positive_interaction_df %>% \n filter(Abbr == \"H5N1\" | Abbr == \"H5N5\") %>%\n droplevels() %>%\n ggplot(aes(x = year_month,\n fill = Interaction)) +\n #geom_bar(stat = \"count\", position = \"dodge\", width = 0.7) +\n geom_bar(stat = \"count\") +\n expand_limits(x = c(-2, length(levels(sampling_df$interval))+2)) +\n scale_fill_manual(values = c(\"WTE: H5N1\" = rgb(1,0,1,1),\n \"WTE: H5N5\" = rgb(0.5,0,0.5, 0.3),\n \"Mammal: H5N1\" = rgb(.5,.5,.5,1),\n \"Mammal: H5N1\" = rgb(.5,.5,.5, 0.3),\n \"Other Avian: H5N1\" = rgb(0,0,1, 1),\n \"Other Avian: H5N5\" = rgb(0,0,1,0.3)\n ),\n name = \"Types positive Influenza A\") +\n theme_bw() +\n theme(axis.text.x = element_text(angle = 45, hjust = 1)) +\n labs(title = \"Number of positive H5N5 and H5N1 samples\", \n x = \"Date\", \n y = \"Count\") \n \npositive_H5N1_5_plot\n```\n\n\n```{r}\npositive_H5N1_5_plot_wrap <- \n positive_H5N1_5_plot + \n facet_grid(rows = vars(WTE), cols = vars(Abbr), scales = \"fixed\") \n\npositive_H5N1_5_plot_wrap \n```\n\n\n```{r}\nggsave(here::here(params$save_dir, \"positive_H5N1_5_plot.png\"), \n positive_H5N1_5_plot_wrap, width = 15, height = 10)\n\n```\n\n\n<!-- \n--> \nTrying to put the plot together\n\n\n\n```{r}\nlibrary(patchwork)\ndevtools::load_all(\"~/packages/patchwork/\")\n```\n\n\n```{r}\nposititive_AI1 <- positive_AI \nsampling_plot1 <- sampling_plot + scale_y_continuous(limits = c(0, 450))\n\n\nplot1 <- \n sampling_plot1 + posititive_AI1 + \n plot_layout(guides = \"collect\", \n axes = \"collect\") & theme(legend.position = 'bottom')\n\nplot1\n```\n\n```{r}\nggsave(\n here::here(params$save_dir, \"plot_sampling_all_and_positive.png\"),\n plot1, width = 20, height = 15)\n```\n\n\n<!--\nnow we zoom on the AI positive \n```{r}\nmylayout <- \"\nAC\nBC\n\"\n\npositive_AI / positive_H5N1_5_plot \n\n\npositive_H5N1_5_plot_wrap + \n plot_layout(guides = \"collect\", axes = \"collect\", design = mylayout)\n```\n\n```{r}\nsampling_postive_negative_plot + positive_H5N1_5_plot +\n plot_layout(guides = \"collect\", axes = \"collect\", design = mylayout)\n```\n\n--> \n\nnb birds tested -> 2020 - 2021 - 2022 \nshould have doubled 2021 and 2022 \nbut here start in october 2020 \n\n\n"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":5704,"numItemsPerPage":100,"numTotalItems":664582,"offset":570400,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sInJlcG9JZHMiOlsiNmFiODVhOGYzODBjYmQ4YzAzYWQ5MjY3Il0sImlhdCI6MTc5MzMxNTM1MCwianRpIjoiOTBjMjAyNjYtOTMyMC00MDIyLWI2MWYtNzVlNzljMjhjMWQ5Iiwic3ViIjoiL2RhdGFzZXRzL09wZW5TY2llbnRpZmljQ29kZVJlZ2lzdHJ5L0RhdGFiYXNlIiwiZXhwIjoxNzkzMzE4OTUwLCJpc3MiOiJodHRwczovL2h1Z2dpbmdmYWNlLmNvIn0.o76vpmRyjo78ncqXWkjfg4VVpC4J1B5NRrqvzYpPr7Y8R32OeP0yb8UfYlfUOafrlQWi06va2bak9UZYm_KtCg","displayUrls":true,"splitSizeSummaries":[{"config":"scripts","split":"train","numRows":664582,"numBytesParquet":1123206516}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
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27d793685a46d6ce9ad12157d8ae1a1fd1fd75ca710f924ffac3144814fc90b3 | Python | 147,483 | 3,080 | """pyqtgraph GUI for the CCF boundary editor.
See the previous module-level docstring in git history for the conceptual
overview. This revision adds:
* Logging that actually reaches the user (configured in ``launch.py``).
* Close-window guard for unsaved edits.
* Per-instance autosave path so multiple editors ... |
18903c9ec51bfaa30951f7811de8bfb99420f0e69160a8714806bfebd81f23d1 | Python | 148,556 | 2,442 | # -*- coding: utf-8 -*-
"""
Created on Tue May 31 10:09:38 2022
@author: Dalton
"""
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from matplotlib import colormaps
import dill
import os
import math
import re
import seaborn a... |
f08d0cba90993a047d7926dc847f5cd7414c0ea33070d08965a8f228126291c7 | Python | 148,603 | 3,897 | #!/usr/bin/env python3
# ******************************************
#
# SHARC Program Suite
#
# Copyright (c) 2023 University of Vienna
#
# This file is part of SHARC.
#
# SHARC is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by... |
579fb615ba7de25318c53b7901d3cd72a9316e03ba154103d6b60b1cc35dd6aa | Python | 148,994 | 4,164 | #
#
# Copyright (C) from 2009 to Present EPAM Systems.
#
# This file is part of Indigo toolkit.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... |
1df66af8479ea77fc6dfc82c2df727f1595269c36f096bb9a9174c5ec1c7d94c | Python | 158,440 | 3,325 | # coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... |
ab771de14cc5070f41cc1fabfcf6b6060bd128efd877fa19fe2ae8f287dc0c50 | Python | 160,366 | 4,487 | #!/usr/bin/env python3
# ******************************************
#
# SHARC Program Suite
#
# Copyright (c) 2023 University of Vienna
#
# This file is part of SHARC.
#
# SHARC is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by... |
92a343d03e2fd2cbc199819a356854f4a65e77c594dc8ec1b378d641b69148b9 | Python | 162,937 | 7,117 | from __future__ import annotations
# MG_RESUME_INSTANCE_POSTPROCESSING_V1
import io
import zipfile
from dataclasses import dataclass
from pathlib import Path
import errno
import hashlib
import json
import math
import os
import shutil
import time
from uuid import uuid4
import cv2
import numpy as np
import pandas as ... |
2142e3fc4ef5cc482eab0633b6f7fa3def82ff82e98b8aec25dc9c5ac2f8150c | Python | 163,729 | 4,638 | #!/usr/bin/env python3
#******************************************
#
# SHARC Program Suite
#
# Copyright (c) 2023 University of Vienna
#
# This file is part of SHARC.
#
# SHARC is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
... |
11f1272fa111f50951fef90588d54b3f9f211d369930f3bc170a12e69384b9fe | Python | 164,368 | 2,679 | """Version: 57.26813.20241028
We use ctypes to call into the toupcam.dll/libtoupcam.so/libtoupcam.dylib API,
the python class Toupcam is a thin wrapper class to the native api of toupcam.dll/libtoupcam.so/libtoupcam.dylib.
So the manual en.html(English) and hans.html(Simplified Chinese) are also applicable for progr... |
862e8d2352d46aadd1cee9d89e9dadf655d318965f10d7c9baf4e9b2283099f0 | Python | 165,688 | 2,695 | """Version: 57.27348.20241224
We use ctypes to call into the toupcam.dll/libtoupcam.so/libtoupcam.dylib API,
the python class Toupcam is a thin wrapper class to the native api of toupcam.dll/libtoupcam.so/libtoupcam.dylib.
So the manual en.html(English) and hans.html(Simplified Chinese) are also applicable for progr... |
bc9a236be117c0860b2af1271dea1bff56e99b1de71045f78a6b5a7be86edf2c | Python | 167,976 | 3,349 | #!/usr/bin/env python
# coding: utf-8
# In[1]:
versionString="49" # version string of this application
# # NanoDiP all-in-one Jupyter Notebook
# *J. Hench, C. Hultschig, J. Brugger, and S. Frank, Neuropathology, IfP Basel, 2021-2023*
#
# This software is provided free of charge a... |
c885fa3db4dc47f67f737374e6ce11b4fb169afb4907595c579c15777f182ffe | Python | 168,258 | 2,136 | # -*- coding: utf-8 -*-
"""
Created on Tue Nov 30 16:20:11 2021
@author: alpha
"""
def get_datasets():
'''
Dataset dict should be structured as:
key = dataset_ID
dict = {
url: list of STR in decending resolution order
scale: tuple (z,y,x) of the highest resolution ... |
92505f4338c9019129faac17b0e82d8e3bbdb789d27b9176add49e3cd03b42bd | Python | 168,342 | 3,331 | #!/usr/bin/env python
# coding: utf-8
# In[1]:
versionString="47" # version string of this application
# # NanoDiP all-in-one Jupyter Notebook
# *J. Hench, C. Hultschig, J. Brugger, and S. Frank, Neuropathology, IfP Basel, 2021-2023*
#
# This software is provided free of charge a... |
b6e83725373e9b8ebc66bcc2c57cd8c3cf2bafe09af748263e111b2fdde00b98 | Python | 168,617 | 4,484 | from reuse import *
json_file = '/groups/branson/bransonlab/mayank/apt_cache_2/multitarget_bubble/mmpose/view_0/alice_randsplit_round_7/train_TF.json'
im_dir = ''
bdir = '/groups/branson/bransonlab/mayank/apt_cache_2/alice_inc'
##
import json
val_frac = 0.2
J = pt.json_load(json_file)
n_full = len(J['annotations'])
ra... |
03bc15c2d9537075e8f6157fde74052b87af9a1c559460ddd2414dbbb082af43 | Python | 169,192 | 3,636 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
3a1d759477e1749faddadd46860528b9e81beeb1aed9273ded7150d04de20d0c | Python | 169,932 | 4,855 | #!/usr/bin/env python3
# ******************************************
#
# SHARC Program Suite
#
# Copyright (c) 2023 University of Vienna
#
# This file is part of SHARC.
#
# SHARC is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by... |
f6ce69a234e095ccb41425445312c1713f0929fa1b88ea6d760fb8bfee48f0ba | Python | 170,640 | 2,791 | # -*- coding: utf-8 -*-
"""
Created on Tue May 31 10:09:38 2022
@author: Dalton
"""
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from matplotlib import colormaps
import dill
import os
import math
import re
import h5py
imp... |
3dc453d03696da9a7f565799c9b269ac221093c918ff95dd68b57630d429a2be | Python | 170,968 | 4,443 | """
Classes for doing the spatial analysis of clouds of points in a 2D plane
# Author: Antonio Martinez-Sanchez (Max Planck Institute for Biochemistry)
# Date: 12.06.15
"""
__author__ = 'martinez'
import sys
import vtk
import shutil
from pyorg.globals import *
from .variables import *
# from globals import FilVisito... |
6c2f955aae99437877a8b0cdff743e8eaaed1f5a4df10ce475c41e3afa871233 | Python | 170,996 | 4,445 | """
Classes for doing the spatial analysis of clouds of points in a 2D plane
# Author: Antonio Martinez-Sanchez (Max Planck Institute for Biochemistry)
# Date: 12.06.15
"""
__author__ = 'martinez'
import sys
import shutil
from pyseg.globals import *
from .variables import *
from .globals import FilVisitor2
from abc ... |
79e3b0911aac583131f54c3c2293bafb6090380026d06d880b1c6d81707d6f6d | Python | 171,035 | 2,791 | # -*- coding: utf-8 -*-
"""
Created on Tue May 31 10:09:38 2022
@author: Dalton
"""
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from matplotlib import colormaps
import dill
import os
import math
import re
import h5py
imp... |
bb569c89a0d01af7ac2cae25faf8c9780be453810c0a32b6787f1d6637238243 | Python | 183,561 | 4,853 | #!/usr/bin/env python3
# ******************************************
#
# SHARC Program Suite
#
# Copyright (c) 2023 University of Vienna
# Copyright (c) 2023 University of Minnesota
#
# This file is part of SHARC.
#
# SHARC is free software: you can redistribute it and/or modify
# it under the terms o... |
350090641264e22612bfcb294219aafd5707b2a0ed2434d638b27de99aa1980a | Python | 188,865 | 3,762 | #!/usr/bin/env python
# coding: utf-8
# In[1]:
versionString="53" # version string of this application
# # NanoDiP all-in-one Jupyter Notebook
# *J. Hench, C. Hultschig, J. Brugger, and S. Frank, Neuropathology, IfP Basel, 2021-2023*
#
# This software is provided free of charge a... |
a3d9663520de84e6693ee87b4ee51c5374ca020919d68623bfb3161e265ed35f | Python | 189,990 | 4,959 | from __future__ import print_function
import collections
import colorsys
import inspect
import json
import math
import os
import pickle
import platform
import signal
import numpy as np
import pybullet as p
import random
import sys
import time
import datetime
import shutil
import cProfile
import pstats
from collection... |
2a39b6f63b39fa647e9a3ed9a68fbc1b040fd959013137955e158864ac8de2db | Python | 190,076 | 4,959 | from __future__ import print_function
import collections
import colorsys
import inspect
import json
import math
import os
import pickle
import platform
import signal
import numpy as np
import pybullet as p
import random
import sys
import time
import datetime
import shutil
import cProfile
import pstats
from collection... |
3fbd05ef534f074165f7322174f013c4097fdb85d70ddabb1553ddbed47bf2b3 | Python | 192,816 | 5,428 | #!/usr/bin/env python3
# ******************************************
#
# SHARC Program Suite
#
# Copyright (c) 2023 University of Vienna
#
# This file is part of SHARC.
#
# SHARC is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by... |
2f5eb6ebd95bdc3810eb28dce372ab289cb6d89308d0a6e6ca32820d7f88f15b | Python | 193,351 | 5,083 | #!/usr/bin/env python3
# ******************************************
#
# SHARC Program Suite
#
# Copyright (c) 2023 University of Vienna
#
# This file is part of SHARC.
#
# SHARC is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by... |
cfe88f4383402b4a81257e452eb26c10021d5850cda8d910675d295e43d7734d | Python | 193,581 | 5,080 | from typing import (
Tuple, Callable, Iterator, Union, TypeVar, Optional, List, Dict, Type,
NamedTuple,
)
from itertools import product, combinations, permutations
from functools import partialmethod
import warnings
from scipy import sparse
from scipy.linalg import svdvals
import numpy as np
from ase import At... |
9d407df36f3063a0c3e0dfe5f8132a2f48183ca50718f24fb677db70dcb4ada2 | Python | 193,641 | 5,819 | #!/usr/bin/env python
# txt2tags - generic text conversion tool
# http://txt2tags.sf.net
#
# Copyright 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008 Aurelio Jargas
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the... |
11c283cedd76b4af5051726f4bb67b226e5d8c7e756284f9ee6e33d02338fa65 | Python | 200,000 | 6,389 | # PyNest File -- Export from neuroVIISAS
import nest
nest.ResetKernel()
import nest.voltage_trace
import nest.raster_plot
import pylab
import sys
import random
from numpy.numarray import *
regionsCount=362
spikeDetectorNodeCount=362
# Erdos Renyi
randModel=0
randParameter=0.0
data=range(regionsCount)
for i in data:
... |
2d66eb861b7396c9064a9bd5a55bb20225ef18a6b2210f76fd4d969ecac92f78 | Python | 200,000 | 1,601 | swc_datasets = {
# '182683': [ # TODO: wrong scale
# 'https://download.brainimagelibrary.org/3a/c1/3ac1bdc022d0da78/182683-38/mouseID_182683_01.swc',
# 'https://download.brainimagelibrary.org/3a/c1/3ac1bdc022d0da78/182683-38/mouseID_182683_02.swc',
# 'https://download.brainimagelibr... |
3e17235b9640c46109e6c9d3b1c307141f66696299722b01970eed1d991a5cb0 | Python | 200,000 | 7,348 | # PyNest File -- Export from neuroVIISAS
import nest
nest.ResetKernel()
import nest.voltage_trace
import nest.raster_plot
import pylab
import sys
import random
from numpy.numarray import *
regionsCount=442
spikeDetectorNodeCount=442
# Erdos Renyi
randModel=0
randParameter=0.0
data=range(regionsCount)
for i in data:
... |
3f9d1309e7c74bd5dc8db17e85f45e19e6c6516a40edfae2f7d6f1a4414be739 | Python | 200,000 | 4,490 | """
Classes for reducing the size of a SkelGraph but keeping its Morse-Smale complex structure
# Author: Antonio Martinez-Sanchez (Max Planck Institute for Biochemistry)
# Date: 18.11.14
"""
__author__ = 'martinez'
import gc
import operator
from .core import *
import warnings
from scipy import sparse
try:
import... |
61292a870e9e986a34eafca404905c9f746baa78d5437285a572ec2786305315 | Python | 200,000 | 5,288 | #!/usr/bin/env python
# coding: utf-8
# In[1]:
from gudhi.clustering.tomato import Tomato
import scipy.io as sio
import matplotlib.pyplot as plt
import h5py
import numpy as np
from ripser import Rips, ripser
from sklearn import preprocessing
from scipy.spatial.distance import cdist, pdist, squareform
from scipy.nd... |
64bc9a2e284219e3de53a2a7c17e7d63429121fa7f87f37baa34c5d430457537 | Python | 200,000 | 4,804 | """
Classes for doing the spatial analysis of clouds of points embedded in a dense array.
This methodology allows to handle with irregular domains in 2D and 3D and multivariate cases.
# Author: Antonio Martinez-Sanchez (Max Planck Institute for Biochemistry)
# Date: 12.03.16
"""
__author__ = 'martinez'
import gc
fro... |
73b2b4d449c9dec9525998349ebe9e2f1c599e87b99a67cf9ad38b84d48d6fb0 | Python | 200,000 | 5,356 | #!/usr/bin/env python
# coding: utf-8
# In[ ]:
# In[ ]:
def glm2d(xxss, ys, num_bins, periodicprior, LAM, GoGaussian, nF):
T = len(xxss[:,0])
tmp = np.floor(T/nF)
xxss[:,0] = normit(xxss[:,0])
xxss[:,1] = normit(xxss[:,1])
xvalscores = np.zeros(nF)
P = np.zeros((num_bins**2, nF))
L... |
83afa835032836e2f2ba46eaad7e57ac9916f795470fe1cd1d5e12a6d4ee2ec5 | Python | 200,000 | 3,223 | # -*- coding: utf-8 -*-
"""
Created on Tue May 31 10:09:38 2022
@author: Dalton
"""
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from matplotlib.colors import ListedColormap
import pickle
import dill
import os
import glob
... |
8aec276ba8b9314f19b71d748448c86f6e6ec68c51b0bb482f872f0cbc62cc12 | Python | 200,000 | 7,422 | # PyNest File -- Export from neuroVIISAS
import nest
nest.ResetKernel()
import nest.voltage_trace
import nest.raster_plot
import pylab
import sys
import random
from numpy.numarray import *
regionsCount=479
spikeDetectorNodeCount=479
# Erdos Renyi
randModel=0
randParameter=0.0
data=range(regionsCount)
for i in data:
... |
96a492712eef2825777eba9d02928175298697f8e24441e1b8f8942b19487931 | Python | 200,000 | 2,802 | # The conduction system is meant to be part of a geometry, thus, it indexes the geometry with its index values,
# and it indexes itself with meta_index values.
from warnings import warn
import numpy as np
from distance_functions import djikstra
from utils import get_nan_value, find_first, index_list, get_edge_from_pa... |
a8e0caa78ef5410cb5142838b8635f96aa9590c1fe6ac182bbe97583694b4063 | Python | 200,000 | 5,446 | #
# core.py
#
from collections import deque
import os
import typing
from typing import (
Any,
Callable,
Generator,
List,
NamedTuple,
Sequence,
Set,
TextIO,
Tuple,
Union,
cast,
)
from abc import ABC, abstractmethod
from enum import Enum
import string
import copy
import warnin... |
b8c9c8ebb0fa798010713a04e651e4e835bc47088bcc0107936280a2616c4bad | Python | 200,000 | 6,345 | # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... |
bb7225ef780f45200ebf16a948f4d43ef6a417da822c771f654083150356c900 | Python | 200,000 | 2,215 | import os
os.environ['CUDA_VISIBLE_DEVICES'] ='0'
os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION']='python'
os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
import nest_asyncio
nest_asyncio.apply()
import sys
cmd = 'tt /groups/branson/bransonlab/mayank/data/ChimpAct/gt_id/gt_id_run__banka0all_20260826T033052.log \
--r... |
be0cd6eaf9cc2d9b99a23441060ec9fe548bc57d913ad113e72d6d7cf24f4efb | Python | 200,000 | 4,829 | """
Classes for doing the spatial analysis of clouds of points embedded in a dense array.
This methodology allows to handle with irregular domains in 2D and 3D and multivariate cases.
# Author: Antonio Martinez-Sanchez (Max Planck Institute for Biochemistry)
# Date: 12.03.16
"""
__author__ = 'martinez'
import gc
fro... |
c221e85d4373db3ad4bf2a6fb3112033645f986ff46e4b2ec8ae3f2d381616a5 | Python | 200,000 | 7,422 | # PyNest File -- Export from neuroVIISAS
import nest
nest.ResetKernel()
import nest.voltage_trace
import nest.raster_plot
import pylab
import sys
import random
from numpy.numarray import *
regionsCount=472
spikeDetectorNodeCount=472
# Erdos Renyi
randModel=0
randParameter=0.0
data=range(regionsCount)
for i in data:
... |
c75aa1df0e94013e7e8364544698db98947477dfe183c4b90c453531edc1eeb4 | Python | 200,000 | 4,902 | from __future__ import division, unicode_literals, absolute_import
from builtins import int, range, dict, map, zip, object
import os
import io
import re
import sys
import queue
import random
import pkg_resources
# Future warning from cython in h5py
import warnings
warnings.simplefilter(action='ignore', category=Futu... |
cc10a835d4d3ddc22239234325fef743093382cd967bbd5fa5cebe7d13833a27 | Python | 200,000 | 7,423 | # PyNest File -- Export from neuroVIISAS
import nest
nest.ResetKernel()
import nest.voltage_trace
import nest.raster_plot
import pylab
import sys
import random
from numpy.numarray import *
regionsCount=463
spikeDetectorNodeCount=463
# Erdos Renyi
randModel=0
randParameter=0.0
data=range(regionsCount)
for i in data:
... |
d12644bccb6c5a30669bb70ab56e37c92e637e3efca40c31d91edab01d9a36da | Python | 200,000 | 7,400 | # PyNest File -- Export from neuroVIISAS
import nest
nest.ResetKernel()
import nest.voltage_trace
import nest.raster_plot
import pylab
import sys
import random
from numpy.numarray import *
regionsCount=572
spikeDetectorNodeCount=572
# Erdos Renyi
randModel=0
randParameter=0.0
data=range(regionsCount)
for i in data:
... |
d1bbf5e6ff1dccf13d97957eeee6c81686cc19a84c4f714fc74c0925af05072f | Python | 200,000 | 674 | # noqa
# type: ignore
# flake8: noqa
# pylint: skip-file
# mypy: ignore-errors
# yapf: disable
# pylama:skip=1
# *** PLEASE DO NOT MODIFY DIRECTLY: Automatically generated code ***
VERSION = "2.16.3"
import re
from .fastjsonschema_exceptions import JsonSchemaValueException
REGEX_PATTERNS = {
'^.*$': re.compi... |
db3e50cd8934883e906efff1a517aba5c4217e65b427abf5e379027508194813 | Python | 200,000 | 2,256 | # Produced using OpenRAVE
# TOOL_TFORM = [[0., 0., 1., 0.18],
# [0., 1., 0., 0.],
# [-1., 0., 0., 0.],
# [0., 0., 0., 1.]]
# PR2_DISABLED_COLLISIONS = [('base_link', 'br_caster_l_wheel_link'), ('base_link', 'br_caster_r_wheel_link'),
# ('base_link', 'f... |
db8f5e6c9ddd9c40bc865ef7e36aa2e838b8fdb7a167eb003ed817d97c36cdeb | Python | 200,000 | 4,908 | #from __future__ import division
#from __future__ import print_function
PREFETCH_BATCHES = True
from concurrent.futures import ThreadPoolExecutor
import logging
from operator import truediv
#logging.basicConfig(
# format="[%(asctime)s] %(levelname)s [%(name)s.%(funcName)s:%(lineno)d] %(message)s")
#logging.warning... |
dc21df780b52ebf760d63375eb647cd61ef61087fc5ad817e3bd03bad732628c | Python | 200,000 | 4,259 | # set QT_API environment variable
import os
import sys
# qt libraries
os.environ["QT_API"] = "pyqt5"
import qtpy
import pyqtgraph as pg
from qtpy.QtCore import *
from qtpy.QtWidgets import *
from qtpy.QtGui import *
# control
from control._def import *
if DO_FLUORESCENCE_RTP:
from control.processing_handler impor... |
e0462676c2e34846bfcdf2355d559b83bab73ef8d5bc369f29d26ae3d717c2cb | Python | 200,000 | 3,523 | #!/usr/bin/env python
# EDITABLE SECTIONS ARE MARKED WITH #@#
version="2.4"
authors=["Djurre de Jong", "Jaakko J. Uusitalo", "Tsjerk A. Wassenaar"]
# Parameters are defined for the following (protein) forcefields:
forcefields = ['martini21','martini21p','martini22','martini22p','elnedyn','elnedyn22','elnedyn22p',... |
ec9ebdcbfd4434af1b3aebef8443e6d09a2f5d2b5232fad2d1eada8aa5d3f2b0 | Python | 200,000 | 4,300 | '''
Classes for representing a tomograms with particles
'''
import gc
import os
import sys
import math
import time
import copy
import random
from shutil import rmtree
from pyorg import pexceptions, sub
from pyorg import globals as gl
# from pyorg.surf import gen_tlist, Model
from .utils import *
from pyorg.spatial.sp... |
096f04e1128e29d2ca93ee7a58c056783fc01f4e81fc3d5f5b12d2356e2b02bd | Python | 200,008 | 5,337 | import numpy as np
import numpy.random as random
import scipy.optimize as opt
import TrkFile
import APT_interface as apt
import logging
import os
import scipy
import pickle
import json
# for now I'm just using loadmat and savemat here
# when/if the format of trk files changes, then this will need to get fancier
from ... |
cd9122470d799b730d009cc51884d2c66a9976b06fffe000d92cf90f57e535c7 | Python | 200,041 | 4,488 | import os
import sys
# set QT_API environment variable
os.environ["QT_API"] = "pyqt5"
# qt libraries
import qtpy
from qtpy.QtCore import *
from qtpy.QtWidgets import *
from qtpy.QtGui import *
import pyqtgraph as pg
import pandas as pd
import napari
from napari.utils.colormaps import Colormap, AVAILABLE_COLORMAPS
im... |
bc301d944570dd1d080dd947cc79d241b86d82c0f46ad11dac7179469ee83efb | Python | 200,050 | 3,913 | """Registration tab - main workflow UI for brain-to-atlas registration.
Contains:
- _load_image_volume(): helper to load TIFF/NIfTI volumes for preview
- RegistrationTab(QWidget): the primary registration workflow tab
"""
import os
import re
import sys
import time
import subprocess
import numpy as np
... |
cef8d9536e2ce7cfee012f39d0c71dd0d9c3d17eff802300323cd634879425d7 | Python | 206,539 | 8,600 | # This file is automatically generated by tools/idna-data
# vim: set fileencoding=utf-8 :
from typing import List, Tuple, Union
"""IDNA Mapping Table from UTS46."""
__version__ = '15.0.0'
def _seg_0() -> List[Union[Tuple[int, str], Tuple[int, str, str]]]:
return [
(0x0, '3'),
(0x1, '3'),
(0x2, '3')... |
cd65d2b3e068f7d79ba9f75bb31659de4b4c89b2b13a1bcbda6a5258d87f4716 | Quarto | 177 | 12 | ---
title: "Archive"
page-layout: full
listing:
contents: posts/
type: default
fields: [date, title]
sort: 'date desc'
feed:
items: 5
categories: [python]
--- |
67c8ff40c72ff254bb07fea31b297f1f6e23213337a2bbac9d5efac20ea8fdd1 | Quarto | 299 | 18 | ---
title: "Home"
listing:
contents: posts
sort: "date desc"
type: default
categories: true
sort-ui: false
filter-ui: false
fields: [date, title, description, categories, image]
# feed: true
# items: 3
# categories: [python]
page-layout: full
title-block-banner: false
---
|
64c346e5354b78e9fa79c8bf4c08923a086cecf557fe492c66e87a937327fa75 | Quarto | 321 | 22 | ---
filters:
- interlinks.lua
interlinks:
autolink: true
aliases:
quartodoc: null
#sources:
# test:
# url: https://example.com
---
* `some_func`
* `some_func()`
* `some_func(a=1)`
* `some_func()`{.qd-no-link}
* `some_func + some_func`
* `a.b.c`
* `~a.b.c`
* `a.b.c()`
* `quartodoc.Auto()`
* `Auto()`... |
8b09534724ab8870c3bc24fc80d0ec449a352a2fc5d141a422cfe2ae90c34083 | Quarto | 322 | 11 |
---
title: Tutorials
toc: true
toc-depth: 3
fig-align: center
---
There are some written tutorials for different aspects of the process.
They are _slightly_ outdated, as maintaining large amounts of documentation takes a lot of effort. Contributions on more documentation from community members are always appreciated... |
662add43f09dd2e6f1c411aa2e7d0a47621402b06fd2eac8bd51fb1493acd468 | Quarto | 337 | 21 | ---
title: Examples
listing:
id: listing-examples
contents: examples.yml
type: grid
sort: date desc
sort-ui: false
grid-columns: 2
filter-ui: true
# template: template.ejs
# css: styles.css
---
Some examples of projects that have used Molecular Nodes in their production.
::: {#listi... |
7349d2df10f19cc86a977f045149b652ebafc7099babf4abd36f76f6f250fc89 | Quarto | 491 | 15 | ---
title: Citing Molecular Nodes
listing:
id: listing-citations
contents: citations.yml
type: grid
grid-columns: 3
---
A paper for Molecular Nodes has not yet been published, but you can cite the Zenodo entry for the GitHub repo here: [](https://zenodo.org/ba... |
52ca1e90aa19c18e099b66098f465617bf5431a18945d76391db46a9a8aa154e | Quarto | 919 | 22 | ---
title: Nodes
description: The node groups that ship with Molecular Nodes
toc: false
---
Molecular Nodes is built on top of Blender's Geometry Nodes. Everything you see
in the viewport — styles, colours, selections, animations — is created by
chaining together the node groups documented in this section.
Inside the... |
3a8ec439019272f65c604392104c9974f3ec3b115dbc427a6c6f831394589ac0 | Quarto | 1,025 | 52 | ---
title: "Session Info, packages and citat"
author: "Eve Zeyl Fiskebeck"
date: "`r format(Sys.time(), '%d %B, %Y')`"
params:
source_code_dir: 'C:/Users/vi2067/OneDrive - Veterinaerinstituttet/REINSTALLED/projects_vault/Projects/2023/1_AvianInfluenza/12302_Avian_influenza_2023/code/R'
execute:
echo: false
... |
f5564c01952649b4ec046c3ca5045e865cce910ca541a204aa81f242bc657a71 | Quarto | 1,083 | 56 | ---
title: "SEP Phylog tree packages"
author: "Eve Zeyl Fiskebeck"
date: "`r format(Sys.time(), '%d %B, %Y')`"
params:
source_code: 'code/R'
execute:
echo: false
cache: false
format: html
knitr:
opts_chunk:
collapse: true
comment: "#>"
R.options:
knitr.graphics.auto_pdf: true
editor_optio... |
a6dd2f634b2f0fd1edc49e24be6b246b17674db122ac9f482d6edf8acc09b73f | Quarto | 1,112 | 53 | ---
title: "Tree Data check: included in Tree"
author: "Eve Zeyl Fiskebeck"
date: "`r format(Sys.time(), '%d %B, %Y')`"
params:
alltree_metadata_rds: "data/prepared_data/all_tree_metadata_CAB.RDS"
tree: "results/H5N1_HA/phylog/20240104_HA_H5N1_MSA_filtered_nodub.fasta.contree"
execute:
echo: false
cache: ... |
b95b230d336b97206c4d69239235e8f96bd0f14a1c2681a0756ec8d4eb07cf5c | Quarto | 1,200 | 29 | ---
title: "About"
image: profile.jpg
about:
template: jolla
links:
- icon: twitter
text: Twitter
href: https://twitter.com/mcbwrs
- icon: linkedin
text: LinkedIn
href: https://linkedin.com/in/matt-bowers/
- icon: github
text: Github
href: https://github.com/mcb00
--... |
d678a1d7bb015ae3553d60f8671d22976819a4ac11794fdfa072b0e4c1d8adaa | Quarto | 1,206 | 48 | ---
title: "Update tree metadata"
author: "Eve Zeyl Fiskebeck"
date: "`r format(Sys.time(), '%d %B, %Y')`"
params:
save_dir: "data/prepared_data/"
all_tree_metadata: "all_tree_metadata_CAB.RDS"
new_metadata: "all_tree_metadata_CAB_2023-03-07.xlsx"
execute:
echo: false
cache: false
format: html
knitr:
... |
b16426a2ce6ff247e5a8adde33f92637a5a2ace63404308c3742fc72754cf39e | Quarto | 1,356 | 30 | ---
title: "Gradient Boosting"
listing:
contents:
- posts/gradient-boosting-machine-from-scratch
- posts/get-down-with-gradient-descent
- posts/how-gradient-boosting-does-gradient-descent
- posts/gradient-boosting-machine-with-any-loss-function
- posts/consider-the-decision-tree
- posts/decisi... |
eb3d75b1f5e4b5b4c8ea8901ba08a19e635f3bf274319d69f5e9ce807f48891d | Quarto | 2,395 | 63 | ---
title: "Electrostatics"
description: "Colour a molecular surface by an electrostatic potential from APBS."
execute:
daemon: false
---
[APBS](https://www.poissonboltzmann.org/) solves the Poisson-Boltzmann equation for a molecule in solvent and writes the electrostatic potential on a grid, usually as a `.dx` file... |
98594e793a0625798c67b0d4b16034843c27b4e813cf760ee71a0045e5f707c4 | Quarto | 2,531 | 131 | ---
toc: true
toc-depth: 3
fig-align: center
---
# Data Tables
The different lookup tables that are used to conver strings to integers in Molecular Nodes.
Code for this can be found on the [GitHub Page](https://github.com/BradyAJohnston/MolecularNodes/blob/main/molecularnodes/assets/data.py)
### Residue Names
| Nam... |
73c5f770daea12793c2c259522c06671133ef8157c9db3aeedcd37ca643f41df | Quarto | 2,554 | 77 | ---
title: Small Molecules from PubChem
description: "Fetching 3D conformers of small molecules from PubChem"
jupyter: python3
execute:
daemon: false
---
Small molecules can be fetched from [PubChem](https://pubchem.ncbi.nlm.nih.gov/) with
[](`~mn.Molecule.fetch`) and `database="pubchem"`. The code is either a compo... |
b94f8950f072209561cbc659a7e2b32ec96191cbcd3aad463fae0c32d2414853 | Quarto | 2,592 | 69 | ---
title: "README_WORKFLOW"
date: "`r format(Sys.time(), '%d %B, %Y')`"
author: "Eve Zeyl Fiskebeck"
format: html
editor: visual
params:
---
# Workflow scripts
1. Metadata and aknowledgments: `PRE_collate_raw_metata_aknowlegments.qmd`
- collates metadata for isolates
- link to previously collated metadata fo... |
773de5dc9ad714225f7763ec6a06dcaa5f5f65947698c6b872c675df339bf5c3 | Quarto | 3,021 | 35 | ---
title: Random Realizations Resurrected
date: 2023-08-02
description: The world's favorite data science blog is back.
categories: blogging
image: thumbnail.jpg
image-alt: Christ the Redeemer towers into an empty blue sky
toc: false
draft: false
comments:
utterances:
repo: mcb00/blog
issue-term: title
---
... |
1355a1b24ecdf3f1beb5b4399edca39fc6c5b133506fd7d3602d53ddaa94a15c | Quarto | 3,039 | 135 | ---
title: "Collate Metadata - Aknowledgments"
date: "`r format(Sys.time(), '%d %B, %Y')`"
author: "Eve Zeyl Fiskebeck"
format: html
editor: visual
params:
metadata_files_dir: "data/raw_metadata/metadata"
metadata_rds: "data/raw_metadata/collated_metadata.RDS"
acknowledgments_files_dir: "data/raw_metadata/ac... |
ba68761ec30b8264c5b33b06d388a9e2624deb4889c5e13cf78cf9be83c2dd07 | Quarto | 3,101 | 76 | ---
title: "Installation"
aliases:
- ../installation.html
---
::: callout-warning
# Blender 4.2
For Blender 4.2, we no longer have to download anything externally. Molecular Nodes can be installed and updated from _within_ Blender, using the new [`extensions`](https://https://extensions.blender.org/) platform. The i... |
d945bcbb9722122327e09880d1d8184d3ab4d35be0b8007fcf821348ebe96d8a | Quarto | 3,492 | 80 | ---
title: Selections
author: Brady Johnston
aliases:
- "02_selections.html"
---
Selections power every part of Molecular Visualisation.
You are probably used to using selections via manually pointing and clicking with your mouse, or typing long strings of complex selection algebra.
## A Simple Example
To create a... |
7819fe4be6e8275076f77a34cc0f6256db3264348aa47c42ec67d39becbf9123 | Quarto | 3,768 | 64 | ---
title: CryoEM Maps
author: Brady Johnston
aliases:
- "04_cryoem.html"
---
We can import CryoEM maps, which are imported into Blender as a 'volume' object. As Blender is designed to work with data for movies and TV shows, this is implemented in a way to emulate smoke or gas. We ar... |
da735b8c741f6c4fb30da5ddb6866137fdd7d0ab4eec6052433185cce7580bde | Quarto | 3,871 | 115 | ---
title: "Rendering"
description: "Rendering images and animations with the Canvas."
execute:
daemon: false
---
The [](`~mn.Canvas`) is used to render images and animations of any [](`~mn.Molecule`),
whether it is a single static structure or a multi-frame trajectory. Annotations can be
attached to the molecule an... |
a61837b906efe8e4e145e041fdd31184b8dd5695bb0f309da7fb7201ed28019f | Quarto | 3,936 | 134 | ---
title: "Materials"
jupyter: python3
fig-align: center
---
MolecularNodes ships a small set of pre-built materials, available as classes in
`mn.material`. Instantiating one creates a new, independent Blender material —
starting values are set through the constructor, and every parameter remains
tweakable on the ins... |
25fda9bbbcd5043c5b59fcc94ac5a33f4cf9b827d56e7eba9f458340c417af3a | Quarto | 3,993 | 110 | ---
title: "Examples"
number-sections: true
number-depth: 3
---
## Animating ATP Synthase
To create the animation of ATP synthase you can morph between separate states of ATP synthase and export them from your favourite molecul... |
12d9ba7dc4288ea685c405ba336bc16e46511ec29845591c0fe5e16a95db2905 | Quarto | 4,216 | 62 | ---
title: Streaming Trajectories
subtitle: Load and stream a currently running simulation using MDanalysis and IMDClient.
author: Brady Johnston
fig-align: center
---
With the MDAnalysis [2.10 release](https://www.mdanalysis.org/2025/10/26/release-2.10.0/) we can now stream information (positions, velocities etc) fro... |
24e22081aec5950f95b8a86da3e7fa5f64761ea75bf44f604302584ba3dc97c3 | Quarto | 4,322 | 57 | ---
toc: false
title: Hello World! And Why I'm Inspired to Start a Blog
date: "11/22/2020"
description: A reflection on what inspired me to start a blog and three reasons I think it could be a good
idea.
categories: blogging
image: thumbnail.jpg
image-alt: Matt raises his arms in joy at the world.
---
 example from the MDAnalysis user g... |
5c636b173a20b0950d161966251365bb8a203f14c859179921ba5bb3f593c609 | Quarto | 4,586 | 173 | ---
title: "Manually assigning regulators"
format:
html:
fig-width: 7
fig-height: 4
fig-dpi: 100
---
```{r}
#| include: false
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
The purpose of this vignette is to show how to manually configure the
`is_regulator` vector, e.g. when you want t... |
3544c9aa3bbaaab4368fcb29d2f8c379d488dece13ac7fb83907b2d71cd56ae0 | Quarto | 5,329 | 240 | ---
title: Density
description: "Examples of using the Density entity API"
jupyter: python3
execute:
daemon: false
---
The Density entity can be used to load density grids into Blender. It uses MDAnalysis [`GridDataFormats`](https://www.mdanalysis.org/GridDataFormats/) library and supports a wide variety of [formats... |
2c13ba6461d2ff630137257897f5290b9ca354abbd95c57edadb83741838d762 | Quarto | 6,088 | 149 | ---
title: Python API
description: Introduction to the API and the quirks of programming in Blender
jupyter: python3
---
::: {.callout-warning}
# The API is Experimental
Molecular Nodes was first built as an add-on for Blender, intended to be used through the GUI.
A lot of improvements have been made to now allow a ... |
a3bf8f6df4dfe21c8badafbd3bc8789bf0373f79893ca37439d72191f179bb59 | Quarto | 6,377 | 249 | ---
title: "Check stats datasets"
author: "Eve Zeyl Fiskebeck"
date: "`r format(Sys.time(), '%d %B, %Y')`"
params:
prepdata_dir: "data/prepared_data"
results_dir: "results"
execute:
echo: false
cache: false
format: html
knitr:
opts_chunk:
collapse: true
comment: "#>"
R.options:
knitr.gra... |
f292b9242f7f3a3826fb326d012a2768b72c01e183aeda6faceaa2bfe738019c | Quarto | 6,380 | 236 | ---
title: Node Trees
subtitle: Using `nodebpy` to create and edit node trees
---
Molecular Nodes uses `nodebpy` for creating and changing the node trees - Geometry, Shader and Compositor.
This allows for replicating the node-based workflows from the GUI, enabling the full power of Geometry Nodes (GN) and the other n... |
ef24ee4ede39d0df67ea28666512dc2a6dd8f9ec694b1f5070b0a848c794bc75 | Quarto | 6,440 | 68 | ---
title: "About"
toc: false
page-layout: article
---
<img src="images/logo.png" align="right" style = "height:250px;"/>
[](https://github.com/BradyAJohnston/MolecularNodes/actions/workflows/lint.yml)
[{autoplay=true loop=true muted=t... |
1a65bfcfaccaac42c1b02e67b71f020d62152036940430802346e7cb757dfebd | Quarto | 6,622 | 123 | ---
title: Trajectories
subtitle: Importing Molecular Dynamics data
author: Brady Johnston
bibliography: references.bib
fig-align: center
aliases:
- "03_molecular_dynamics.html"
---
::: {#fig-md-example-render}
{autoplay="true" loop="true"}
The protein adenylate kinase (AdK) underg... |
9ade50d4debe35fa0f9f7cf0afe4aef7f8348032416bc90a395bddbe3e3681e9 | Quarto | 6,872 | 250 | ---
title: "SEP H5N1 NA Phylogenetic tree"
author: "Eve Zeyl Fiskebeck"
date: "`r format(Sys.time(), '%d %B, %Y')`"
params:
strain: "H5N1"
segment: "HA"
all_tree_metadata: "data/prepared_data/all_tree_metadata_CAB.RDS"
tree: "results/H5N1_NA/prerun/iqtree/SEP_NA_H5N1_MSA_nonamedub.fasta.contree"
sav... |
462de9287763c0e86f095d738ef8fa7ca14e46bd282abc24d669658eed75fea4 | Quarto | 7,207 | 214 | ---
title: "Demonstration of workflow"
format:
html:
fig-width: 7
fig-height: 4
fig-dpi: 100
---
```{r}
#| include: false
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
rlang::local_options(lifecycle_verbosity = "quiet")
```
The methods below are described in our article
> Larsson, Held, ... |
2d6acbc060c3f5172d29aa061252fee9ade543176a33b919459c6a82be02c361 | Quarto | 7,288 | 222 | ---
title: Trajectories
description: "Working with multi-frame molecules and molecular dynamics data"
jupyter: python3
---
A trajectory is not a special class — it is simply a [](`~mn.Molecule`) whose underlying
[](`MDAnalysis.Universe`) has more than one frame. Loading, styling and rendering all work
exactly as they ... |
50849726d2b1e9a9cefbf3863b51d24d4f235e02dd6953c0a0d355e6ad6c9bcc | Quarto | 7,521 | 243 | ---
title: Styles
description: "Adding styles and representations to molecules"
jupyter: python3
execute:
daemon: false
---
Styles (or *representations*) are how we turn the raw atomic data of a [](`~mn.Molecule`)
into something we can actually see. Every `Molecule` is styled the same way, whether it is
a single sta... |
9368139a943bcdef5b9ce5e753979b8c8806ce5cd93e3322718e2f26f474e71e | Quarto | 9,141 | 363 | ---
title: "Preparation Article Aknkowledgments"
author: "Eve Zeyl Fiskebeck"
date: "`r format(Sys.time(), '%d %B, %Y')`"
params:
aknowledgments_dir: "data/raw_metadata/acknowledge"
metadata_dir: "data/raw_metadata/metadata"
save_dir: "data/prepared_data"
execute:
echo: false
cache: false
format: html
knitr... |
ed50511273a28b69d5540af9b28724b5631c773086ffd2b36559dcd6f1f2b103 | Quarto | 9,354 | 129 | ---
title: Blender's Interface
alasses:
- "00_interface.html"
---
Blender has a _very_ overwhelming interface. There is no getting around this. Blender can do _practically anything_, and it is sometimes hard to do simple things.
### A New File
Starting a new file, you are greeted by the splash screen. You can clic... |
27546f85ff6d62104b4e0453b36ee4ea90a986a2889010d871a33d7c6ce8ddfc | Quarto | 10,063 | 144 | ---
title: Fetch from the PDB
fig-align: center
aliases:
- "01_importing.html"
---
Molecules, simulations and other file formats can all be imported via the `Molecular Nodes` subpanel. This panel can be found under the `Scene Properties` panel. This is the small cone and spheres icon on the right. This contains all ... |
104e381819f7def0816b9856085f43379cdc87bfc1a120b17427f9ce01c76880 | Quarto | 14,004 | 495 | ---
title: "Sampling distribution check"
author: "Eve Zeyl Fiskebeck"
date: "`r format(Sys.time(), '%d %B, %Y')`"
params:
sampling_data: "data/raw_metadata/HPAI_all_samples20201001_20230815_dev.xlsx"
save_dir: "results"
source_code: 'code/R'
execute:
echo: false
cache: false
format: html
knitr:
opts_... |
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