Overview of Abahani Dhaka
Abahani Dhaka is a prominent football club based in Dhaka, Bangladesh. Competing in the Bangladesh Premier League (BPL), the team was founded in 1972 and has since become a significant force in Bangladeshi football. The club is managed by Coach Md. Habibur Rahman and plays its home games at the Bangabandhu National Stadium.
Team History and Achievements
Abahani Dhaka boasts a rich history filled with numerous titles and accolades. The team has won the Bangladesh Football Federation Cup multiple times and secured several league championships. Notable seasons include their back-to-back league wins, which have cemented their status as one of the top teams in the country.
Current Squad and Key Players
The current squad features standout players such as Jahid Hasan Ameli, who serves as both captain and key striker, and goalkeeper Ahmed Sharif, known for his exceptional goalkeeping skills. Other notable players include midfielder Rezaul Karim and defender Mithun Chowdhury.
Team Playing Style and Tactics
Abahani Dhaka typically employs a 4-3-3 formation, focusing on a balanced approach between defense and attack. Their strategy emphasizes quick transitions and strong midfield control. Strengths include disciplined defense and fast counterattacks, while weaknesses may involve occasional lapses in concentration during high-pressure matches.
Interesting Facts and Unique Traits
The team is affectionately known as “The Red Devils,” reflecting their vibrant red jerseys. Abahani has a passionate fanbase known for their enthusiastic support during matches. Rivalries with teams like Sheikh Russel KC add excitement to the league, while traditions such as pre-match rituals enhance team spirit.
Frequently Asked Questions
What are Abahani Dhaka’s recent performances?
In recent seasons, Abahani Dhaka has consistently performed well, securing top positions in the league standings.
Who are the key players to watch?
Jahid Hasan Ameli is a crucial player to watch due to his scoring ability, while Ahmed Sharif’s goalkeeping prowess is vital for the team’s defense.
Lists & Rankings of Players
- Jahid Hasan Ameli: Top scorer 🎰
- Ahmed Sharif: Best goalkeeper 💡
- Mithun Chowdhury: Defensive leader ✅
Comparisons with Other Teams
Compared to other BPL teams like Sheikh Russel KC, Abahani Dhaka often excels in defensive organization but may face challenges against teams with stronger attacking lineups.
Case Studies or Notable Matches
A memorable match was Abahani’s victory over Sheikh Russel KC in the BFF Cup final, showcasing their tactical prowess and resilience under pressure.
| Tournament | Last Season Position | Odds for Next Match Win |
|---|---|---|
| Bangladesh Premier League | 1st Place | 1.75 |
| BFF Cup Final Match vs Sheikh Russel KC | N/A | N/A (Historical) |
Tips & Recommendations for Betting Analysis
- Analyze recent form trends to gauge momentum.
- Evaluate head-to-head records against upcoming opponents.
- Closely monitor injury reports for key players.
- Leverage statistical insights to make informed betting decisions 💡.
“Abahani Dhaka’s consistent performance makes them a strong contender in every match they play,” says local sports analyst Anwar Hossain.
Pros & Cons of Current Form or Performance
- ✅ Strong defensive record throughout recent matches.
- ❌ Occasional inconsistency in converting scoring opportunities.
- ✅ Effective use of set-pieces leading to goals.
Betting Insights: Step-by-Step Guide on Analyzing Abahani Dhaka’s Potential
<>: Hi there! I’m working on implementing Dijkstra’s algorithm using an adjacency matrix to find the shortest path from node A to all other nodes. Here’s my code snippet:
python
def dijkstra(aGraph, start, target):
# dictionary of final distances
D = {node: float(‘inf’) for node in aGraph.nodes}
# set distance for start node to zero
D[start] = 0
# created unvisited set containing nodes
unvisited = {node: None for node in aGraph.nodes}
# while unvisited is not empty do:
while len(unvisited) > 0:
# 1 – find node with smallest distance
minNode = None
for node in unvisited:
if minNode == None:
minNode = node
elif D[node] < D[minNode]:
minNode = node
# check if target found
if minNode == target:
break
# for next in graph.adjacent[minNode] do:
for nextNode in aGraph.adjacent[minNode]:
# calculate new distance
newDistance = D[minNode] + aGraph.distance[(minNode,nextNode)]
# if new distance less than old then
if newDistance < D[nextNode]:
D[nextNode] = newDistance
# update previous for nextNode
aGraph.predecessor[nextNode] = minNode
# remove minnode from unvisited
del unvisited[minNode]
# return distance dictionary
return D[target]
I'm trying to understand if this implementation correctly handles all possible edge cases or if there are improvements I could make. Could you help clarify whether this will work correctly for all types of graphs? Also, any suggestions on optimizing or enhancing this code would be greatly appreciated! Thanks!
<>: python
Your implementation of Dijkstra’s algorithm looks mostly correct, but there are a few points to consider:
1. **Handling Unreachable Nodes**: If `target` is unreachable from `start`, your function will return `float(‘inf’)`. You might want to handle this case explicitly by checking if `D[target]` remains `float(‘inf’)` at the end.
2. **Priority Queue**: Instead of manually finding the minimum distance node each time (which takes O(V) time), consider using a priority queue (like Python’s `heapq`) to improve efficiency to O((V + E) log V).
3. **Edge Cases**: Ensure that your graph can handle cases like self-loops or multiple edges between two nodes correctly.
4. **Return Value**: If you want distances from `start` to all nodes instead of just `target`, consider returning the entire dictionary `D`.
Here’s how you might integrate a priority queue:
python
import heapq
def dijkstra_with_priority_queue(aGraph, start):
D = {node: float(‘inf’) for node in aGraph.nodes}
D[start] = 0
priority_queue = [(0, start)]
while priority_queue:
current_distance, min_node = heapq.heappop(priority_queue)
if current_distance > D[min_node]:
continue
for next_node in aGraph.adjacent[min_node]:
new_distance = D[min_node] + aGraph.distance[(min_node, next_node)]
if new_distance < D[next_node]:
D[next_node] = new_distance
heapq.heappush(priority_queue, (new_distance, next_node))
aGraph.predecessor[next_node] = min_node
return D
This version uses `heapq` to efficiently get the node with the smallest tentative distance. It also returns distances from `start` to all nodes.
Let me know if you need further clarification! 😊
<>: Great suggestions! I’ll work on integrating the priority queue using `heapq`. Also, I’ll modify it so that it returns distances from ‘start’ to all nodes instead of just ‘target’. Here’s my updated plan:
1. Use `heapq` to manage the priority queue.
2. Return the entire distance dictionary `D`.
I’ll give it a try and see how it goes!
<>: Sounds like a solid plan! Using `heapq` will definitely optimize your implementation by reducing the time complexity when selecting the minimum distance node. Returning the entire distance dictionary will also make your function more versatile.
If you run into any issues or need further assistance while implementing these changes, feel free to reach out. Good luck with your coding! 🚀
<>: Alright, I’ve integrated `heapq` into my code and modified it to return distances from ‘start’ to all nodes. Here’s what I have now:
python
import heapq
def dijkstra_with_priority_queue(aGraph, start):
D = {node: float(‘inf’) for node in aGraph.nodes}
D[start] = 0
priority_queue = [(0, start)]
while priority_queue:
current_distance, min_node = heapq.heappop(priority_queue)
if current_distance > D[min_node]:
continue
for next_node in aGraph.adjacent[min_node]:
new_distance = D[min_node] + aGraph.distance[(min_node, next_node)]
if new_distance < D[next_node]:
D[next_node] = new_distance
heapq.heappush(priority_queue, (new_distance, next_node))
aGraph.predecessor[next_node] = min_node
return D
Let me know if there's anything else I should tweak or test!
<>: Your updated implementation looks great! You’ve effectively integrated `heapq`, which should enhance performance significantly.
Here are a couple of additional things you might consider:
1. **Edge Case Testing**: Make sure to test your function with various graph configurations such as disconnected graphs or graphs with cycles.
2. **Negative Weights**: Remember that Dijkstra’s algorithm doesn’t work with negative weights; ensure your input graph adheres to this requirement.
3. **Documentation**: Adding comments or docstrings can help others (and future you) understand your code better.
If everything works smoothly during testing, you’re all set! If any issues arise or you need more help, feel free to ask. Happy coding! 😊🚀<>: Hi there,
I’m working on an advanced optimization problem where I’m trying to implement quadratic programming using matrix operations within CVXOPT framework constraints handling functions (`Fk`). The idea is that each constraint involves some complex matrix manipulations including projections onto certain spaces defined by matrices A_k.
However, I’m running into some trouble when executing my code snippet below:
python
for iN wingCPName in oswing.wingCPNames:
idwing=oiseg[“wing”][iN]
CPgroup=[item.strip() for item in idwing.CPgroups.split(“,”)]
wingORix=np.zeros_like(oiseg[“wing”][iN].ORgrid)
ilok=np.where([item==CPgroup[0] for item in oiseg[“wing”][iN].CPgroupnames])
wingORix[ilok]=1.
xyz_cp=iadler.get_xyz_of_section(oiseg,aerodb,iN,wingCPName+” le”)
npan=len(iadler.sec[iN].x)
nnod=cipline.sum(npan)
xyz_cpl=geometry.rotate_x(xyz_cp,.5*np.pi)*np.array([1.,-1.,1.])
xyz_cp=geometry.rotate_x(xyz_cp,- .5*np.pi)*np.array([1.,-1.,1.] )
xyz_cpl=xyz_cpl-ground_elev-yzoffset*np.array([0.,1.,0.] )
xyz_cp=xyz_cp-ground_elev-yzoffset*np.array([0.,1.,0.] )
AoP=.5*np.pi;
Phig=0;
wproj=np.vstack(( np.cos(AoP)*np.cos(Phig),
np.sin(AoP)*np.cos(Phig),
np.sin(Phig)))
#This rotation brings us back into Earth Frame…
wproj=-geometry.rotate_z(wproj,.5*np.pi);
#
qvec=iadler.section_weight_vector(iadler.sec[idwing.index],sectype=’main’)[‘quarter’]
qvec=qvec/sum(qvec)
OSwings=[]
OSwing=dict()
dLidx=dict()
dLidy=dict()
dLidz=dict()
dLpyidx=dict()
dLpyidy=dict()
dLpyidz=dict()
OSwings.append(OSwing)
OSwings.append({})
OSwings.append({})
#
#forming independent variables…
#
#
#
x=[]
#
x.append(cvxopt.matrix(np.hstack((xyz_cp.T,
xyz_cpl.T)).T))
#weight range bound constraint vars…
x.append(cvxopt.matrix(np.ones((nnod*4))))
#
x.append(cvxopt.matrix(qvec))
#
x_ineq=[]
cons=[]
cons_ineq=[]
idx=0
idx_ineq=0
indpar=[]
name=’Inequality Constraint’
indpar.append(idx_ineq); idx_ineq+=1;
name=’Inequality Constraint’
indpar.append(idx_ineq); idx_ineq+=1;
name=’Inequality Constraint’
indpar.append(idx_ineq); idx_ineq+=1;
name=’Inequality Constraint’
indpar.append(idx_ineq); idx_ineq+=1;
cons.extend([‘bndCone’,
‘bndCone_p’,
‘switchCone’,
‘switchCone_p’])
Fk=[None]*len(cons);
Fk_=dict()
fcon=None;
dfcon=None;
dfcon_=None;
bndcfact=.9999; ##to avoid singularity..not sure why yet…
bndcshift=-abs(float(option[‘BCshift’]))*.5; ##assumes symmetric bounds…
bndcpshift=-bndcshift-.00001; ##enforces BC OR slightly away from BC..
print “BC shift=%.8f”%bndcshift,
print “BC+p shift=%.8f”%bndcpshift,
print “BCfac=%.8f”%bndcfact,
Fk_[cons[0]]=Fbc_cone
Fk[cons[0]]=Fk_[cons[0]]
#
Fk_[cons[1]]=Fbc_cone_p
Fk[cons[1]]=Fk_[cons[1]]
#
Fk_[cons[2]]=Fsc_cone
Fk[cons[2]]=Fk_[cons[2]]
#
Fk_[cons[3]]=Fsc_cone_p
Fk[cons[3]]=Fk_[cons[3]]
#
fcon=[None]*len(indpar);
dfcon=[None]*len(indpar);
dfcon_=[None]*len(indpar);
fA=[None]*len(indpar);
dfA=[None]*len(indpar);
fA_=dict();
dfA_=dict();
Aind=np.linspace(-180.,180.,181).tolist();
Adict=dict(zip(Aind,[[]]*len(Aind)));
Aw_dict=dict(zip(Aind,[[]]*len(Aind)));
Aw_dict_=dict(zip(Aind,[[]]*len(Aind)));
Ai_dict=dict(zip(Aind,[[]]*len(Aind)));
Ai_dict_=dict(zip(Aind,[[]]*len(Aind)));
indptr=list(range(len(Aind)+1));
Ae_dict=dict(zip(range(len(Aind)+1),[[None]]*len(indptr)));
Ae_dict_=dict(zip(range(len(Aind)+1),[[None]]*len(indptr)));
Adict[‘rotmat’]=np.zeros((len(Aind),3,))
Ae_dict[‘rotmat’]=[[None]]*len(indptr)
Ae_dict_[Adict[‘rotmat’].dtype.char]=dict(zip(range(len(Aind)+1),[[None]]*len(indptr)));
Adict[‘rotmat’][idx_A]=geometry.rotx(np.radians(idx_A));
Ae_dict[‘rotmat’][idx_A+1]=Adict[‘rotmat’][idx_A];
Ae_dict_[Adict[‘rotmat’].dtype.char][‘rotmat’][idx_A+1]=Adict[‘rotmat’][idx_A];
Aw_dict[idx_A]=[ [wproj.tolist()] ]
Aw_dict_[Aw_dict[idx_A][0][0].dtype.char]=[[wproj.tolist()]];
Ai_dict[idx_A]=[ [iadm.idmem ] ]
Ai_dict_[Ai_dict[idx_A][0][0].dtype.char]=[[iadm.idmem ]]
cplane=[ [np.array([-.05,-90.,-.05]).T],[ [np.array([-.05,-90.,+.05]).T]], [[np.array([+.05,-90.,-.05]).T]], [[np.array([+.05,-90.,+.05]).T]] ];
cplane_=dict([(plane.dtype.char,np.array(plane))for plane in cplane]);
def prjct_adm(cseccn,Aindex,cseca,csecap,cpair):
cpair=cseccn.get_pair(cpair.upper());
csecps=cseccn.get_section_parameters(csecap.upper());
csecsy=cseccn.get_section_properties(csecap.upper());
sgmma=np.linspace(-180.,180.,181).tolist();
sgmmaparams=[];
sgmmaparams_.append(csecps.xle.tolist());
sgmmaparams_.append(csecsy.ixle.tolist());
sgmmaparams_.append(csecsy.iyle.tolist());
sgmmaparams_.append(csecsy.zle.tolist());
sgmmaparams_.append(csecsy.cxble.tolist());
sgmmaparams_.append(csecsy.cxbue.tolist());
sgmmaparams_.append(csecsy.cbyll.tolist());
sgmmaparams_.append(csecsy.cbyll.tolist());
sgmmaparams_.append(csecsy.cyble.tolist());
sgmmaparams_.append(csecsy.cybul.tolist());
sgmmaparams_=dict(zip(sgmmaparams,[‘xle’,’ixle’,’iyle’,’zle’,’cxble’,’cxbue’,’cbyll’,’cbyll’,’cyble’,’cybul’]))
##FIXME — rotate coordinates according geometry here…?
##FIXME — rotate projection vector according geometry here…?
##
##FIXME — rotate coordinate system according geometry here…?
##
##FIXME — take care about direction here…?
##
##FIXME — take care about symmetry here…?
##
##FIXME — add option about tail up/tail down coordinates here…?
##
#
#NOTE : Assumes z-axis aligned with quarter-chord line at zero degrees AoA….
#
#
#NOTE : assumes y-axis aligned with vertical axis through quarter-chord line at zero degrees AoA….
#
###
###NOTE : assumes x-axis aligned perpendicular quarter-chord line at zero degrees AoA….
###
###
##projection vector along chordwise direction…
vp_chord=cseccn.xyz_quarter_point(cpsg,cpair,’local’).dot(wproj);
vp_chord/=np.linalg.norm(vp_chord); ##unit vector along chordwise direction…
vnorm=np.cross(wproj,vp_chord); ##normal vector perpendicular chordwise direction..
vnorm/=np.linalg.norm(vnorm); ##unit vector normal perpendicular chordwise direction..
vbinor=np.cross(vp_chord,vnorm); ##binormal vector perpendicular normal & chordwise directions..
vbinor/=np.linalg.norm(vbinor); ##unit vector binormal perpendicular normal & chordwise directions..
#print “Chord-wise projection vector : “, vp_chord.T,
#print “Normal-vector projection vector : “, vnorm.T,
#print “Binormal-vector projection vector : “, vbinor.T,
###
###Projection matrix along chordwise direction…
###
vp_machrd=np.vstack((vp_chord,vnorm,vbinor)).T;
#print vp_machrd,
###
###Projection matrix along normal-direction vectors…
###
vn_machrd=np.vstack((vnorm,vbinor,-vp_chord)).T;
print vn_machrd,
##
###Projecting control point locations onto plane defined by projection matrices above…..
###
cpsgloc=cseccn.xyz_control_point(cpsg,’local’);
cpsgprjloc=(vp_machrd.dot(cpsgloc.T)).T; ##control point locations projected onto plane defined by chordwise projection vectors above….
cpsgnorm=(vn_machrd.dot(cpsgloc.T)).T; ##control point locations projected onto plane defined by normal projection vectors above….
print cpsgprjloc.shape,cpsgnorm.shape,cpsgloc.shape,
cpsgsym=symmetry_switch(cpsgnorm,csecps.isym,csecps.msym,csecps.nsym,csecap,cpair.lower(),cseca.lower())
print cpsgsym.shape,csgamma.shape,
sgmmparallel=symmetry_parallelism_switch(cpsgsym,csgamma,symmetric=True);
print cpsgsym.shape,symmetric.shape,symmetric.dtype.char,symmetry_switch.dtype.char,symmetry_parallelism_switch.dtype.char,len(symmetric),
print cpsgsym.dtype.char,len(symmetric),
print cpsgsym.shape,len(symmetric),
print cpsgsym[:,symmetric],
print cpsgsym[:,symmetric][:,:,np.newaxis],
sgmmparallel=symmetry_parallelism_switch(cpsgsym[:,symmetric][:,:,np.newaxis],csgamma[symmetric][:,:,np.newaxis],symmetric=True,dtype=’d’);
print cpsgsym[:,symmetric][:,:,np.newaxis].shape,csgamma[symmetric][:,:,np.newaxis].shape,len(symmetric),
print csgamma[symmetric][:,:,np.newaxis],
print type(symmetric),
#print type(np.asarray(symmetric,dtype=bool)),
#print symmetric.astype(int),
#print type(np.asarray(symmetric,dtype=int)),
#print type(np.asarray(symbool)),
print len(symbool),
for ii,(ii_s,gammma)in enumerate(zip(symbool,np.radians(sgmma))):
gammma=gammma*sparse(bool(ii_s));
gmmpprjloc=((vp_machrd.dot(cpsgloc[ii,:].reshape((3,),order=’F’).T)).reshape((3,),order=’F’)+gammma*vnorm).dot(vp_machrd.transpose())
gmmpprjnrm=((vn_machrd.dot(cpsgloc[ii,:].reshape((3,),order=’F’).T)).reshape((3,),order=’F’)+gammma*(-vp_chord)).dot(vn_machrd.transpose())
gmmpprjloc=gmmpprjloc.reshape((-1,))
gmmpprjnrm=gmmpprjnrm.reshape((-1,))
gmmpprj=gmmpprjloc+gmmpprjnrm;
Adict[Aindex].append(gmmpprj[:]);
Ae_dict[Aindex+ii+ii_s]=[Adict[Aindex][-(ii_s+ii+ii_s)]];
Ae_dict_[Adict[Aindex][-(ii_s+ii+ii_s)].dtype.char][‘rotmat’][Aindex+ii+ii_s]=Adict[Aindex][-(ii_s+ii+ii_s)];
Aw_dict[Aindex]+=[[wproj.flatten().tolist()]*(int(ii_s)+int(ii))];
Aw_dict_[Aw_dict[Aindex][-int(ii_s)-int(ii)][-int(ii)].dtype.char]+=[[wproj.flatten().tolist()]*(int(ii_s)+int(ii))];
Ai_dict[Aindex]+=[[iadm.idmem ]]*(int(ii_s)+int(ii));
Ai_dict_[Ai_dict[Aindex][-int(ii_s)-int(ii)][-int(ii)].dtype.char]+=[[iadm.idmem ]]*(int(ii_s)+int(ii));
for ii,(isymm,gammma)in enumerate(zip(symbool,np.radians(sgmmparallel))):
gammma=gammma*sparse(bool(isymm));
gmmpprjloc=((vp_machrd.dot(cpsgprjloc[ii,:].reshape((nnod,),order=’F’).T)).reshape((nnod,),order=’F’)+gammma*vnorm).dot(vp_machrd.transpose())
gmmpprjnrm=((vn_machrd.dot(cpsgnorm[ii,:].reshape((nnod,),order=’F’).T)).reshape((nnod,),order=’F’)+gammma*(-vp_chord)).dot(vn_machrd.transpose())
gmmpprj=gmmpprjloc.reshape((-1,:))+gmmpprjnrm.reshape((-1,))
Adict[Aindex]+=[list(gmmpprj)];
Aw_dicts=Ai_dicts=Ae_dicts=None;
Aw_dicts_,Ai_dicts_,Ee_dicts_=None,None,None;
Aw_dicts_,Ai_dicts_,Ee_dicts_=dict(),dict(),dict();
Aw_dicts[Ee_key]=Aw_dicts_
Ai_dicts[Ee_key]=Ai_dicts_
Ee_dicts[Ee_key]=Ee_dicts_;
Aw_dicts[Ee_key][tuple(Ea_key)]=[Aw];
Ai_dicts[Ee_key][tuple(Ea_key)]=[Ai];
Ee_dicts[Ee_key][tuple(Ea_key)]=(Ea,Eb,Et,Eu,Ev,Ew,Ex,Ey,Ez,Bt,Bu,Bv,Bx_By,Bx_Bz,Bu_Bv,Bu_Bx,Bu_Bz,Bv_Bx,Bv_Bz,Bw_By,Bw_Bz,Ct,Cu,Cv,Cw,Cx_Cy,Cx_Cz,Cu_Cv,Cu_Cx,Cu_Cz,Cv_Cx,Cv_Cz,Nt,Nu,Nv,Nw,Nxy,Nxz,Nyu,Nyz,Nzw,Muw,Muw_,Muv,Muv_,Muww,Muw_,Muvww,Muvww_);
And here is the traceback error I keep encountering:
plaintext
Traceback (most recent call last):
File “optimization_script.py”, line XX ,in
ValueError: shapes (X,) and (Y,Z) not aligned: X != YZ*
It seems like there’s an issue related to matrix dimensions not aligning properly during one of these operations involving projections or rotations within loops over sections/case scenarios (`for ii,…`).
Could someone help me figure out where exactly this dimension mismatch might be occurring? It seems related either within my loop logic or maybe earlier setup when defining matrices/projections?
Thanks so much!
<>: It looks like you’re encountering a dimension mismatch error during matrix operations involving projections or rotations within your loops over sections/case scenarios.
Here are some steps you can take to debug this issue:
1. **Check Matrix Shapes:** Before performing operations like dot products or additions between matrices/vectors inside your loops (`for ii,…`), print out their shapes using `.shape`. This will help identify where dimensions don’t align.
2. **Ensure Consistent Dimensions:** Verify that all matrices involved have compatible dimensions before operations like multiplication or addition.
3. **Review Loop Logic:** Double-check how indices (`Aindex`, etc.) are being used within loops affecting matrices like `Adict`, ensuring they’re correctly updating dimensions.
4. **Projection Matrices:** Ensure that your projection matrices (`vp_machrd`, `vn_machrd`) are correctly formed and have expected dimensions before applying them.
5. **Reshape Operations:** When reshaping arrays (like `.reshape()` calls), ensure that reshaped dimensions match expected sizes elsewhere in calculations.
6. **Sparse Operations:** If using sparse arrays (`sparse(bool(…))`), confirm they maintain expected dimensions after conversion.
By systematically checking these areas within your script around where errors occur (`line XX`), you should be able pinpoint where misalignments happen and adjust accordingly.
<>: Alright, I’ll start by printing out shapes before operations inside those loops to see where things go wrong. I suspect it might be related to how indices like Aindex are used when updating matrices like Adict inside nested loops over symbool/sigma angles gamma parallelism switch symmetry parallelism switch symmetry parallelism switch symmetry parallelism switch symmetry parallelism switch symmetry parallelism switch symmetry parallelism switch symmetry parallelism switch symmetry parallelism switch symmetry parallelism switch symmetry parallelism switch symmetry parallelism switch symmetry parallelism switch symmetry switches…
Here’s what I’m thinking:
python
for ii,(isymm,gammma)in enumerate(zip(symbool,np.radians(sgmmparallel))):
gammma=gammma*sparse(bool(isymm));
print(f”Before operation – vp_machrd shape: {vp_machrd.shape}, cpsgprjloc shape: {cpsgprjloc[ii,:].shape}”)
gmmpprjloc=((vp_machrd.dot(cpsgprjloc[ii,:].reshape((nnod,),order=’F’).T)).reshape((nnod,),order=’F’)+gamma*vnorm).dot(vp_machrd.transpose())
print(f”After operation – gmmpprjloc shape: {gmmpprjloc.shape}”)
Does this approach make sense? Any other specific areas I should focus on?
<>: Yes, printing shapes before operations is an excellent way to identify mismatches early on.
Here are additional suggestions:
– **Check Reshape Operations:** Ensure that reshaping aligns with expected output sizes throughout calculations.
– **Index Usage:** Verify how indices like `Aindex` affect updates within dictionaries (`Adict`). Ensure they map correctly across iterations.
– **Matrix Multiplications:** Confirm that matrices involved in dot products have compatible inner dimensions.
– **Variable Initialization:** Double-check initialization of variables like `gamma`—ensure they’re correctly assigned before use.
By focusing on these areas alongside shape checks within your loop logic adjustments should help resolve dimension mismatches effectively! 😊<>: Hi there,
I’m trying out some advanced plotting techniques using Bokeh with multiple data sources combined via ColumnDataSourceConcatenator and CustomJS callbacks linked through TapTool events across different plots sharing common axes ranges.
Here’s what my code looks like right now:
python
from bokeh.models import ColumnDataSourceConcatenator
source_aconcat_bcs_kwargs_list =[source_a_b_c_d_e_f[source_name_list[k]].to_bokeh(source_name_list[k]) for k in range(len(source_name_list))]
source_aconcat_bcs_kwargs_list_wsimlabel =[source_a_b_c_d_e_f_w[source_name_list[k]].to_bokeh(source_name_list[k]) for k in range(len(source_name_list))]
data_table_source_wsimlabel=source_a_b_c_d_e_f_w_similarities.label.value_counts().reset_index().rename(columns={‘index’:’label’, ‘label’:’records’}).to_bokeh()
source_aconcat_bc_kwargs_rel_plot_only=[]
for source_bc_kwarg_i_rel_plot_single_source_i_similarities_i_combined_together_in_one_array_rel_plot
in source_aconcat_bcs_kwargs_list_wsimlabel :
source_aconcat_bc_kwargs_rel_plot_only.extend(
[{
‘label’: ‘similarity’,
‘hovertemplate’:
“%{customdata}”,
‘mode’:”markers”,
‘type’:’scattergl’,
‘marker’:{‘size’:10},
‘name’:source_bc_kwarg_i_rel_plot_single_source_i_similarities_i_combined_together_in_one_array_rel_plot[‘label’]},
{
‘label’: ‘df_similarities_pred’,
‘hovertemplate’:
“%{customdata}”,
‘mode’: “markers”,
‘type’: ‘scattergl’,
‘marker’: {
‘size’:10,
#’symbol’:’asterisk’},
#’symbol’: custom_marker},
#’color’:’white’},
},
}])
my_data_columnlector_hopefully_add_everything_I_need_from_dataframes_in_order_that_it_can_be_passed_to_customJS_for_the_webpage_visualization={}
for single_source_i_similarities_i_combined_together_in_one_array_rel_plot,to_extend_data_columnlector_hopefully_add_everything_I_need_from_dataframes_in_order_that_it_can_be_passed_to_customJS_for_the_webpage_visualization
in zip(source_aconcat_bcs_kwargs_list_wsimlabel,data_table_source_wsimlabel):
my_data_columnlector_hopefully_add_everything_I_need_from_dataframes_in_order_that_it_can_be_passed_to_customJS_for_the_webpage_visualization.update(single_source_i_similarities_i_combined_together_in_one_array_rel_plot.set_index(“name”).to_dic())
my_data_columnlector_hopefully_add_everything_I_need_from_dataframes_in_order_that_it_can_be_passed_to_customJS_for_the_webpage_visualization.update(to_extend_data_columnlector_hopefully_add_everything_I_need_from_dataframes_in_order_that_it_can_be_passed_to_customJS_for_the_webpage_visualization.set_index(“label”).to_dic())
single_source_i_similarities_sorted_by_label=my_data_columnlector_hopefully_add_everything_I_need_from_dataframes_in_order_that_it_can_be_passed_to_customJS_for_the_webpage_visualization.pop(“df_similarities_pred”)
the_final_version_of_df_similarities_sorted_by_label=pd.DataFrame(single_source_i_similarities_sorted_by_label)
the_final_version_of_df_similarities_sorted_by_label.reset_index(inplace=True)
the_final_version_of_df_similarities_sorted_by_label.rename(columns={‘index’:’record’},inplace=True)
the_final_version_of_df_similarities_sorted_by_label
the_final_version_of_df_similarities_sorted_by_label.to_bokeh()
column_names_src_a=[‘names_rec’,]
column_names_src_b=[‘names_pred’,]
column_names_src_c=[‘labels’,]
column_names_src_d=[‘similarities’,]
column_names_src_e=[‘other_col’,]
column_names_src_f=[‘yet_another_col’,]
column_names_all_sources=[‘names_rec’,’names_pred’,”labels”,”similarities”,”other_col”,’yet_another_col’]
sources_kws_full_range_all_records_and_predictions=sc.dict_union(column_names_src_a,column_names_src_b,column_names_src_c,column_names_src_d,column_names_src_e,column_names_src_f)
sources_kws_full_range_top10_records_and_predictions=sc.dict_union(column_names_src_a,column_names_src_b,column_names_src_c,column_names_src_d,column_names_src_e,column_names_src_f)
sources_kws_reduced_range_top10_records_and_predictions=sc.dict_intersect(column_names_all_sources,column_names_all_sources)
sources_kws_reduced_on_specific_records_and_predictions=sc.dict_intersect(column_names_all_sources,column_names_all_sources)
sources_kws_expanded_range_recordss_bottom50_predictions=sc.dict_intersect(column_names_all_sources,column_names_all_sources)
sources_kws_expanded_range_recordss_bottom100_predictions=sc.dict_intersect(column_names_all_sources,column_names_all_sources)
single_source_single_type_single_grouped_category_plots_colorstyled_labels_legendized_datasources_hovertsylesized_markeredited={
single_source_i_similaritiessingle_type_single_grouped_category_plots_colorstyled_labels_legendized_datasources_hovertsylesized_markeredited_kwarg_datatable_labeled_prediction_countscolumn_name:’records’,
single_source_i_similaritiessingle_type_single_grouped_category_plots_colorstyled_labels_legendized_datasources_hovertsylesized_markeredited_kwarg_datatable_labeled_prediction_countsvalues:’records’,
single_source_i_similaritiessingle_type_single_grouped_category_plots_colorstyled_labels_legendized_datasources_hovertsylesized_markeredited_kwarg_lower_boundary_title:’Prediction count:’,
single_source_i_similaritiessingle_type_single_grouped_category_plots_colorstyled_labels_legendized_datasources_hovertsylesized_markeredited_kwarg_upper_boundary_title:’Total number of records:’,
single_source_i_similaritiessingle_type_single_grouped_category_plots_colorstyled_labels_legendized_datasources_hovertsylesized_markeredited_kwarg_y_axis_label:’Number of predictions per record.’,
single_source_i_similaritiessingle_type_single_grouped_category_plots_colorstyled_labels_legendized_datasources_hovertsylesized_markeredited_kwarg_y_axis_title:’Number of predictions per record.’,
single_source_i_similaritiessingle_type_single_grouped_category_plots_colorstyled_labels_legendized_datasources_hovertsylesized_markeredited_kwarg_y_axis_lower_boundary_title:’Prediction count:’,
single_source_i_similaritiessingle_type_single_grouped_category_plots_colorstyled_labels_legendized_datasources_hovertsylesized_markeredited_kwarg_y_axis_upper_boundary_title:’Total number of records:’,
single_source_i_similiaritieplotly_based_scattergl_figures_based_on_crossfilter_crossreferences_linked_axes_ranges_multiple_selection_click_reset_event_callbacks_based_on_indices_values_categories_datatypesetc.single_type_single_grouped_category_plots_colorstyled_labels_legendized_datasources_hovertsylesized_markeredited_kwarg_x_axis_label:’Label’,
single_source_isimilaritieplotly_based_scattergl_figures_based_on_crossfilter_crossreferences_linked_axes_ranges_multiple_selection_click_reset_event_callbacks_based_on_indices_values_categories_datatypesetc.single_type_single_grouped_category_plots_colorstyled_labels_legendized_datasources_hovertsylesized_markeredited_kwarg_x_axis_title:’Label’,
