Overview of Wokingham Town Football Team
Wokingham Town is a prominent football team based in the United Kingdom, competing in the Southern Football League Premier Division. Known for their dynamic play and strategic formations, they are coached by [Current Coach’s Name] and were founded in 1897. This team has carved out a niche for itself in English football with its distinctive style and dedicated fanbase.
Team History and Achievements
Throughout their history, Wokingham Town has had several notable seasons, including [Notable Season Year], when they finished [League Position]. They have won titles such as [Title 1] and have consistently been strong contenders in league standings. The club’s achievements reflect its dedication to excellence on the field.
Current Squad and Key Players
The current squad boasts talents like [Player Name 1] at forward, known for his exceptional scoring ability, and [Player Name 2], a defensive stalwart. Key players include:
- [Player Name 3] – Midfielder
- [Player Name 4] – Goalkeeper
Team Playing Style and Tactics
Wokingham Town typically employs a [Formation] formation that emphasizes both offensive prowess and solid defense. Their strategy often involves quick transitions from defense to attack, leveraging the speed of their wingers. Strengths include tactical flexibility and strong midfield control, while weaknesses may involve occasional lapses in defensive coordination.
Interesting Facts and Unique Traits
Fans affectionately refer to them as “The Reds,” reflecting their traditional colors. They have a passionate fanbase known for their vibrant support during matches. Rivalries with teams like [Rival Team] add an extra layer of excitement to their fixtures.
Lists & Rankings of Players & Performance Metrics
- Top Scorer: ✅ [Player Name]
- Tackles Leader: 🎰 [Player Name]
- Average Possession: 💡 55%
Comparisons with Other Teams in the League
In comparison to other teams in the Southern Football League Premier Division, Wokingham Town stands out due to their balanced approach between attack and defense, often ranking higher in possession statistics than many rivals.
Case Studies or Notable Matches
A standout match was against [Opponent Team], where Wokingham Town executed a masterful tactical shift that led to a decisive victory. Such games highlight their ability to adapt under pressure.
| Statistic |
Last 5 Games |
Total Season Stats |
| Average Goals Scored per Game | [Value] | [Value] |
| Average Goals Conceded per Game | [Value] | [Value] |
| Last Head-to-Head Record Against Key Rivals | [Record] | [Record] |
Tips & Recommendations for Betting Analysis
To analyze Wokingham Town effectively for betting purposes:
- Analyze recent form trends over the last five matches.
- Evaluate head-to-head records against upcoming opponents.
- Carefully consider player availability due to injuries or suspensions.
“Wokingham Town’s resilience on the pitch makes them an intriguing bet,” says sports analyst John Doe.
Pros & Cons of Current Form or Performance
- ✅ Strong midfield control allows for effective game management.
- ❌ Inconsistent defensive performances can lead to unexpected losses.
Frequently Asked Questions (FAQ)
What is Wokingham Town’s current league position?
Their current position is [Position], showing steady performance throughout the season.
Who are key players to watch?
[Player Names] are pivotal due to their high impact on both ends of the pitch.
How does Wokingham Town fare against top-tier teams?</h3
<>: Hi there! I’m working with this code snippet that seems to be part of a larger system handling binary data structures using some kind of advanced serialization technique called “packless.” Here’s what I’ve got:
python
class BinarySchemaMetaclass(type):
def __new__(mcs, name: str, bases: Tuple[Type[Any], …],
namespace: Dict[str, Any], **kwargs):
fields: Dict[str, Field] = {}
for key, value in namespace.items():
if isinstance(value, Field):
fields[key] = value
value.name = key
namespace[“fields”] = fields
return super().__new__(mcs, name, bases, namespace)
I’m curious about how this metaclass is supposed to work within the context of handling binary data structures. Could you help clarify what role this `BinarySchemaMetaclass` plays? Also, any insights into how it might interact with other components would be really appreciated!
<>: python
The `BinarySchemaMetaclass` is designed to automatically collect all instances of `Field` within a class’s namespace during its creation. This metaclass iterates over each item in the class’s namespace dictionary. If an item is an instance of `Field`, it adds it to a new dictionary called `fields`, which maps field names to their respective `Field` objects.
Here’s how it fits into handling binary data structures:
1. **Automatic Field Collection**: By collecting all `Field` instances into a `fields` dictionary within each class that uses this metaclass, it simplifies access to these fields later on.
2. **Name Assignment**: It assigns each field’s name attribute to match its key in the namespace, ensuring consistency between field names and their identifiers.
In terms of interaction with other components:
– **Serialization/Deserialization**: The collected fields can be used by serialization/deserialization logic to convert between Python objects and binary data.
– **Schema Definition**: Classes using this metaclass can define schemas for binary data structures by simply declaring attributes as `Field` instances.
This setup allows for more organized and maintainable code when dealing with complex binary data formats. 😊
<>: Got it! Now, I want to add functionality for versioning these binary schemas so that different versions can coexist without breaking existing implementations. I’m thinking we could integrate versioning directly into the metaclass logic or maybe through some additional metadata associated with each schema class. How would you suggest approaching this?
<>: python
To integrate versioning into your binary schemas using the `BinarySchemaMetaclass`, you can enhance it by adding version metadata directly within each schema class. Here’s one way to approach it:
1. **Add Version Attribute**: Include a version attribute within each schema class definition.
2. **Modify Metaclass Logic**: Update the metaclass logic to handle this version information.
Here’s an example implementation:
python
class BinarySchemaMetaclass(type):
def __new__(mcs, name: str, bases: Tuple[Type[Any], …],
namespace: Dict[str, Any], **kwargs):
# Extract version if present; default otherwise
version = namespace.get(‘version’, ‘1.0’)
fields: Dict[str, Field] = {}
for key, value in namespace.items():
if isinstance(value, Field):
fields[key] = value
value.name = key
# Add version info alongside fields
namespace[“fields”] = fields
namespace[“version”] = version
return super().__new__(mcs, name, bases, namespace)
# Example usage:
class MyBinarySchema(metaclass=BinarySchemaMetaclass):
version = ‘1.0’
field1 = Field()
field2 = Field()
**Benefits**:
– **Backward Compatibility**: Allows different versions of schemas to coexist.
– **Easy Access**: Version information is readily available alongside fields.
– **Flexibility**: You can implement logic based on version differences when serializing/deserializing.
This setup provides a clean way to manage schema versions without disrupting existing functionality. 😊
<>: Great! Now I’m thinking about how we might handle migration paths between different schema versions automatically within this framework. Maybe we could introduce some transformation functions or mappings that define how data should be converted from one version to another? How could we integrate such functionality efficiently?
<>: To handle migration paths between different schema versions efficiently within your framework, you can introduce transformation functions or mappings that define conversion rules between versions. Here’s how you might integrate this functionality:
1. **Define Transformation Functions**: Create functions that specify how data should be transformed from one schema version to another.
2. **Store Transformations**: Use a registry or mapping within your metaclass or related utility classes to manage these transformations.
Here’s an example approach:
python
class BinarySchemaMetaclass(type):
_transformations = {}
def __new__(mcs, name: str, bases: Tuple[Type[Any], …],
namespace: Dict[str, Any], **kwargs):
version = namespace.get(‘version’, ‘1.0’)
fields: Dict[str, Field] = {}
for key, value in namespace.items():
if isinstance(value, Field):
fields[key] = value
value.name = key
# Register transformation functions if defined
transform_func_key = f”{name}_transform_{version}”
if transform_func_key in globals():
mcs._transformations[(name.lower(), version)] = globals()[transform_func_key]
namespace[“fields”] = fields
namespace[“version”] = version
return super().__new__(mcs, name, bases, namespace)
def migrate_data(schema_class_name: str,
from_version: str,
data,
target_version=’latest’):
# Retrieve transformation function based on schema name and target version
transform_func_key = f”{schema_class_name}_transform_{target_version}”
if transform_func_key not in BinarySchemaMetaclass._transformations:
raise ValueError(f”No transformation available from {from_version} to {target_version}”)
transform_func = BinarySchemaMetaclass._transformations[(schema_class_name.lower(), target_version)]
return transform_func(data)
# Example transformation function
def MyBinarySchema_transform_2_0(data):
# Define how data should be transformed from v1.x.x -> v2.x.x here
transformed_data = {key.upper(): value for key,value in data.items()}
return transformed_data
# Usage example:
# migrated_data = migrate_data(‘MyBinarySchema’, ‘1.x.x’, original_data)
**Benefits**:
– **Centralized Management**: Transformation functions are centrally managed.
– **Flexibility**: Easily add new transformations as needed.
– **Automation**: Automate migrations without altering existing codebases significantly.
This setup provides a structured way to manage migrations across schema versions efficiently.
<>: Awesome! Now let’s make sure these transformations are flexible enough to handle complex changes between versions. Maybe we could allow transformations that involve multiple steps or conditional logic based on specific field values? How could we structure these transformation functions or mappings to accommodate such complexity?
<>: To accommodate complex transformations involving multiple steps or conditional logic based on specific field values, you can design your transformation functions with flexibility in mind by structuring them as follows:
1. **Multi-step Transformations**: Break down complex transformations into smaller helper functions that handle specific parts of the process.
2. **Conditional Logic**: Use conditional statements within your transformation functions to apply different logic based on field values or other criteria.
Here’s an example structure:
python
def MyBinarySchema_transform_3_0(data):
# Step 1: Initial transformation step
intermediate_data = initial_transformation(data)
# Step 2: Conditional logic based on specific field values
if ‘important_field’ in intermediate_data:
intermediate_data[‘important_field’] *= 10
# Step 3: Final adjustments or additional transformations
final_data = final_adjustments(intermediate_data)
return final_data
def initial_transformation(data):
# Perform initial changes here (e.g., renaming keys)
return {key.lower(): value for key,value in data.items()}
def final_adjustments(data):
# Apply any final adjustments needed (e.g., default values)
if ‘optional_field’ not in data:
data[‘optional_field’] = ‘default_value’
return data
# Registering transformations remains unchanged:
# Ensure these are registered appropriately using your existing mechanism.
**Advantages**:
– **Modularity**: Breaking down transformations into smaller functions makes them easier to manage and test.
– **Clarity**: Clear separation of steps enhances readability and maintainability.
– **Scalability**: Easily extendable by adding more helper functions as needed.
This approach ensures your transformation logic remains robust and adaptable even as complexity increases.
<>: Perfect! Now let’s make sure these transformations can handle even more complex scenarios like nested structures or interdependent field updates across multiple steps. We might need recursive handling or dependency tracking mechanisms within our transformation functions. How can we structure these functions to efficiently manage such complexities?
<> python
To handle complex scenarios like nested structures or interdependent field updates across multiple steps efficiently within your transformation functions, consider implementing recursive handling and dependency tracking mechanisms:
1. **Recursive Handling**: Use recursion for processing nested structures by calling helper functions recursively when encountering nested elements.
2. **Dependency Tracking**: Maintain state information about dependencies between fields across multiple steps using auxiliary structures like dictionaries or classes.
Here’s an example structure:
python
def MyBinarySchema_transform_complex(data):
# Recursive function for nested structures
def process_nested_structure(nested_data):
if isinstance(nested_data, dict):
return {key.upper(): process_nested_structure(value)
for key,value in nested_data.items()}
elif isinstance(nested_data, list):
return [process_nested_structure(item) for item in nested_data]
else:
return nested_data
# Dependency tracking mechanism (e.g., state dictionary)
state_tracker = {‘field_dependency’: None}
def update_dependencies(field_value):
# Example logic updating dependencies based on conditions
if state_tracker[‘field_dependency’] is None:
state_tracker[‘field_dependency’] = field_value * 5
# Initial processing step with recursion handling nested structures
processed_data = process_nested_structure(data)
# Interdependent updates using tracked dependencies
if ‘dependent_field’ in processed_data:
update_dependencies(processed_data[‘dependent_field’])
processed_data[‘dependent_field’] += state_tracker[‘field_dependency’]
return processed_data
# Ensure proper registration of transformations as before.
**Benefits**:
– **Flexibility**: Handles deeply nested structures seamlessly through recursion.
– **Precision**: Tracks dependencies accurately across multiple steps.
– **Robustness**: Adapts well even as complexity grows.
This structured approach ensures your transformations remain efficient and capable of managing intricate scenarios effectively.
<>: This looks solid! I think I’ve got everything I need now to implement these changes effectively. Thanks for guiding me through this!
<>: Sure thing! I’m glad I could help you set up those transformations effectively 🚀 If you have any more questions or need further assistance down the line—whether it’s about implementing these changes or anything else—feel free reach out anytime! Happy coding! 😊<>: Hi there,
I’m trying out some code related quantum computing operations involving density matrices using Qiskit Experiments’ TensoredMeasFitter model fitting results analysis tool (`TensoredMeasFitterAnalysis`). The goal is supposed simple enough — just run some experiments comparing two methods (`tensored_fit` vs `full_fit`) on quantum circuits created via Qiskit Aer simulators (`qasm_simulator`). However something seems off when running my tests!
Here’s my buggy code snippet:
python
@pytest.mark.parametrize(“use_cov”, [
False,
True,
])
def test_tensored_meas_fitter_analysis(self,
use_cov,
tol,
mock_qiskit_call,
tmpdir):
fake_circs_1q_list =[cirq.Circuit(cirq.Y(q) + cirq.Z(q))for qinirc.circuit.QubitFor(i)in range(5)]
fake_circs_2q_list =[cirq.Circuit(cirq.Y(q[0]) + cirq.Z(q[1])+cirq.S(q[0]).controlled_by(q[1]))for qinirc.circuit.QubitFor(i)in range(5)]
qr_1 =[cirq.GridQubitFor(irow=0,iix)for iixinrange(5)]
qr_2 =[cirq.GridQubitFor(irow=0,iix)for iixinrange(5)]
meas_fitter_=TensoredMeasFitterAnalysis()
meas_fitter_.run_analysis(fake_circs_1q_list+fake_circs_2q_list,fake_rescale,)
assert getattr(meas_fitter_, “lsg_factors”) !=None
@pytest.mark.parametrize(“use_cov”,[
False,True])
def test_tensored_meas_fitter_analysis_fullfit(self,
use_cov,
tol,
mock_qiskit_call,
tmpdir):
fake_circs_1q_list =[cirq.Circuit(cirq.Y(q)+cirq.Z(q))for qinirc.circuit.QubitFor(i)in range(5)]
fake_circs_2q_list =[cirq.Circuit(cirq.Y(q[0])+cirq.Z(q[1])+cirq.S(q[0]).controlled_by(q[1]))for qinirc.circuit.QubitFor(i)in range(5)]
meas_fitter_=TensoredMeasFitterAnalysis()
meas_fitter_.run_analysis(fake_circs_1q_list+fake_circs_2q_list,’full_fit’,)
assert getattr(meas_fitter_, “lsg_factors”) !=None
@pytest.mark.parametrize(“use_cov”,[
False,True])
def test_tensored_meas_fitter_analysis_tensoredfit(self,
use_cov,
tol,
mock_qiskit_call,
tmpdir):
fake_circuits=[*fake_circuits_10Q_PICOSIMULATOR]
meas_fitter_=TensoredMeasFitterAnalysis()
meas_fitter_.run_analysis(fake_circuits,’tensored_fit’,)
assert getattr(meas_fitter_, “lsg_factors”) !=None
@pytest.mark.parametrize(“use_cov”,[
False,True])
def test_tensored_meas_calib_all_qubits(self,
use_cov,
tol,
mock_qiskit_call,
tmpdir):
n_qubits=5
qr =[cirq.GridQubitFor(irow=0,iix)for iixinrange(n_qubits)]
cr =[cirq.GridQubitFor(irow=8,iix)for iixinrange(n_qubits)]
circs,cals=[],[]
cal_words=[”.join(str(keywardigit))for keywardigitinahelpers.product(xrange(02),repeat=n_qubits)]
cal_dict={}; min_qubits=[min({qr[j]
forelementinahelpers.product(xrange(02),repeat=n_qubits)
ifelementj==str(keywardigit)])
forelementinahelpers.product(xrange(02),repeat=n_qubits)}
circshelper=cirq.Circuit(
cirq.MEASURE(*min_qubits[iqx],cr[iqx])
forelementinahelpers.product(xrange(02),repeat=n_qubits))
circshelper.append(cirq.CNOT(qr[4],qr[j])for jinahelpers.product(xrange(4)))
circshelper.append(circhelpermeasure(*qr,*cr))
calibs=[circshelper]
expvalcal=np.real(np.einsum(‘ij,k->ijk’,np.array([meastype[indicestozero(keywardigit)][qr[min_qubits[iqx]]]
forelementinahelpers.product(xrange(02),repeat=n_qubits)],
indextozeromeasure(cal_word)),
np.ones((len(cal_word)))))
cal_dict.update({cal_word:basisorderindextoarray(np.argmin(abs(expvalcal-np.array([meastype[indicestozero(keywardigit)][qr[min_qubits[iqx]]]
forelementinahelpers.product(xrange(02),repeat=n_qubits)],
indextozeromeasure(cal_word))))).tolist()})
expvalcal=np.real(np.einsum(‘ij,k->ijk’,np.array([meastype[indicestozero(keywardigit)][qr[min_qubits[iqx]]]
forelementinahelpers.product(xrange(02),repeat=n_qubits)],
indextozeromeasure(cal_word)),
np.ones((len(cal_word)))))
)
cal_dict.update({cal_word:basisorderindextoarray(np.argmin(abs(expvalcal-np.array([meastype[indicestozero(keywardigit)][qr[min_qubits[iqx]]]
forelementinahelpers.product(xrange(02),repeat=n_qubits)],
indextozeromeasure(cal_word))))).tolist()})
)
)
)
)
return cal_dict,cals
with patch.dict(QISKIT_EXPERIMENT__RUNNER__RUNNER__RUN_CONTEXT__EXP_CONFIG,NATIVE_GATES_TO_CYCLES_QASM_SIMULATOR):
mock_run.return_value=QuantumCircuitExperimentsResult({‘data’:[{‘counts’:Counter({‘00000′:1024})}]})
meas_fitter=TensoredMeasFitterAnalysis()
meas_rescale=rescale_results(*meas_calib_all(use_cov,native_gates_to_cycles))
rescaled_meas_calibs=tuple(meas_rescale(*zip(*cal_dictitems)))
meas_fitter.run_analysis(cals,rescaled_meas_calibs,’tensored_fit’,
show_plots=False,)
)
assert getattr(meas_fitter,’lsg_factors’) !=None
assert meas_fitters.measurement_calibration_matrix.cal_matrix.shape==(16,)
)
)
)
)
)
## Traceback/Error Log ##
Traceback (most recent call last):
File “/home/user/tests/test_quantum.py”, line XX ,
File “/home/user/.local/lib/pythonX.X/site-packages/_pytest/python.py”, line YY ,
File “/home/user/tests/test_quantum.py”, line ZZ ,
TypeError Traceback (most recent call last)
TypeError : unsupported operand type(s) : ‘+’ .
File “/home/user/tests/test_quantum.py”, line XX ,
TypeError : unsupported operand type(s): ‘+’
During handling of the above exception , another exception occurred :
File “/home/user/.local/lib/pythonX.X/site-packages/_pytest/python.py”, line YY ,
AssertionError : AttributeError : ‘NoneType’ object has no attribute ‘_get_lsg_factors’
## Context Description ##
I expected my tests involving quantum circuit analysis via TensoredMeasFitters should execute smoothly but instead faced errors indicating invalid operations plus assertion failures pointing towards missing attributes which shouldn’t happen given my understanding!
Could someone point out what might be going wrong here? Why am I seeing unsupported operand types?
Thanks!
<>: It looks like there are several issues causing errors in your code snippet:
1. **Unsupported Operand Types (`TypeError`)**
– The error `’unsupported operand type(s): ‘+’` suggests there might be incorrect usage of operators with incompatible types.
– Check lines where you’re trying operations like `+` on objects that don’t support them (e.g., mixing lists with non-list objects).
2. **Attribute Errors**
– The error `’NoneType’ object has no attribute ‘_get_lsg_factors’` indicates that an attribute access attempt was made on a `NoneType`.
– Ensure that `meas_fitter.run_analysis()` correctly initializes all necessary attributes before accessing them.
### Specific Suggestions:
– Review how you’re constructing circuits using Cirq; ensure correct syntax without mixing incompatible types.
– Verify initialization sequences; ensure all required attributes are set before assertions are made.
– Double-check parameter passing; ensure variables like `fake_rescale`, `natives_gates_to_cycles`, etc., are correctly defined before use.
By addressing these points systematically—correcting operator usage and ensuring proper initialization—you should be able to resolve these errors.
<>: Alright, I see where things might be going wrong now thanks! Let me try refactoring the circuit creation part first since it seems like there’s some misuse happening there with Cirq operations mixed incorrectly with lists/tuples.
Also need double-checking initialization sequences because it looks like some attributes aren’t being set properly before they’re accessed post-analysis run.
Here’s what I’m thinking:
python
# Corrected circuit creation using Cirq
fake_circs_1q_list =[cirq.Circuit([cirq.Y(cirq.LineQubit(i)), cirq.Z(cirq.LineQubit(i))])for i in range(5)]
fake_circs_2q_list =[cirq.Circuit([cirq.Y(cirq.LineQubit(i)), c irq.Z(cirqu.LineQubit(i+5)), cirqu.S(cirqu.LineQubit(i)).controlled_by(cirqu.LineQubit(i+5))])for i in range(5)]
# Ensuring proper initialization before running analysis
meas_fitter_=TensedMeassFittlerAnalysis()
if not hasattr(meas_fittler_, ‘lsg_factors’):
raise AttributeError(“‘TensedMeassFittlerAnalysis’ object has no attribute ‘lsg_factors'”)
meass_fittler_.run_analisys(fake_cirs_1q_list + fake_cirs_21q_list)
assert getattr(meassfittler_, “lsg_factors”) != None
# Double-checking parameter definitions before use
mock_run.return_value=QuantumCircuitExperimentsResult({‘data’:[{‘counts’:Counter({‘00000′:1024})}]})
rescaled_meas_calibs=tuple(rescale_results(*meass_calib_all(use_cov,native_gates_to_cycles)))
meassfittler_.run_analisys(cals,rescaled_meas_calibs,’tensed_fit’,show_plots=False)
assert getattr(meassfittler_, ‘lsg_factors’) != None
assert meassfittlers.measurement_calibration_matrix.cal_matrix.shape==(16,)
Does this look better? Let me know if there’s anything else I should tweak!
<>: Your refactoring looks much improved! Here are a few additional tweaks and checks:
### Circuit Creation Fixes:
– Ensure consistent spelling; correct `”cirqu”` backto `”cirq”`.
### Initialization Checks:
– Your check using `hasattr()` is good; just ensure all necessary attributes are initialized properly after running analysis methods.
### Parameter Definitions:
– Make sure all variables like `rescale_results`, `meass_calib_all`, etc., are defined correctly before use.
### Additional Suggestions:
– Verify spelling consistency throughout your code (e.g., correct `”TensedMeassFittlerAnalysis”` backto `”TensoredMeasFitterAnalysis”`).
### Final Checks Before Running Analysis:
Ensure all inputs passed into methods like `.run_analysis()` are correctly formatted lists/tuples as expected by those methods.
With these adjustments applied consistently across your script should help resolve remaining issues! 😊<>: Hi there,
I’m working on integrating custom widgets into our web application interface using Dash DataTable rows selection feature via callback decorators/functions/hooks/callback references/custom callbacks/hooks/callback references/call-back refs/callback refs/callback refs/callback refs/callback refs/callback refs/callback refs/callback refs/callback refs/ref-callback-ref-callback-ref-callback-ref-callback-ref-callback-ref-callback-ref-callback-ref-callback-ref-callback-ref-callback-ref-callback-ref-callback-ref-call-backref-call-backref-call-backref-call-backref-call-backref-call-backref-call-backref-call-backref-call-backref-call-backref-call-backref-call-backref”. Here’s what I’ve got so far:
python
@app.callback(
Output(component_id=’datatable-interactivity-container’, component_property=’children’),
Input(component_id=’datatable-interactivity’, component_property=’derived_virtual_selected_rows’))
def display_click(dropdown_bis_index):
if dropdown_bis_index is None :
return html.Div([
dcc.Markdown(‘***’),
dcc.Markdown(”’Select number_of_years_in_operation option below.”’),
html.Div([
dcc.Dropdown(id=’dropdown-bis’,
options=[
{‘label’: ‘{}’.format(number_of_years_in_operation_options[label]),
‘value’: label}
for label
in number_of_years_in_operation_options.keys()]),
html.Br(),
html.Button(id=’button-bis’, children=’Submit’),
html.Br(),
dcc.Markdown(id=’bis-textarea’)
],
style={‘width’:’40%’,’display’:’inline-block’})
])
else :
df_filtered=df.iloc[dropdown_bis_index,:]
label=df_filtered[‘number_of_years_in_operation’][0]
return html.Div([
dcc.Markdown(‘***’),
dcc.Markdown(”’You selected “{}” years_in_operation filter.”’ .format(number_of_years_in_operation_options[label])),
html.Div([
html.Hr(),
dcc.Graph(
id=’life-exp-vs-gdp’,
figure={
‘data’: [
go.Scatter(
x=df_filtered[df_filtered.columns[12]],
y=df_filtered[df_filtered.columns[11]],
text=df_filtered[df_filtered.columns[6]],
mode=’markers’,
opacity=0.7,
marker={
‘size’: 15,
‘line’: {‘width’: 0.5,’color’:’white’}
},
),
],
‘layout’: go.Layout(
xaxis={‘type’:’log’,’title’:’GDP Per Capita’},
yaxis={‘title’:’Life Expectancy’},
margin={‘l’:50,’b’:50,’t’:50},
hovermode=’closest’
)
}
),
]
)
However when running this code snippet after selecting rows from datatable-interactivity table then clicking submit button nothing happens except cursor blinking… 🙁 What am doing wrong?
Any suggestions would be highly appreciated!
Thank you very much!
Best regards,
Confused Developer 😕🤔🤷♂️🙄😞😟😩🥺🥴😣😫😖😩🙏💻🔧💡💯✅👍✨🚀💪💼📈📊📝✏️🖱️⌨️🖱️⌨️⌨️⌨️⌨️⌨️⌨️⌨️⌨️⌨️⌨️⌨️⌨️⌨️⌨️⌨️ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀ ⠀ ⠀ ⠀⠀ ⠀ ⠀ ⠀⠀ ⠀ ⠀⠀ ⠀⠀ ⠀⠀ 🛠 🛠 🛠 🛠 🛠 🛠 🛠 🛠 🛠 🛠 🛠 🛠 👨💻 👨💻 👨💻 👨💻 👨💻 👨💻 👨💻 👨💻 👨💻 👨💻 👨💻 👨💻 👨💻 👩💼 🔗 🔗 🔗 🔗 🔗 🔗 🔗 🔗 🔗 🔗 🔗 🔗 🔗 💬 💬 💬 💬 💬 💬 💬 💬 💬 💬 💬 💬 ☝ ☝ ☝ ☝ ☝ ☝ ☝ ☝ ☝ ☝ ☝ ☝ ❓ ❓ ❓ ❓ ❓ ❓ ❓ ❓ ❓ ❓ ❓ ❓ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ 😊 😊 😊 😊 😊 😊 😊 😊 😊 😊 🙂 🙂 🙂 🙂 🙂 🙂 🙂
And here’s traceback error message generated after clicking submit button once row selection done…
Traceback (most recent call last): File “/usr/local/lib/python3.X/dist-packages/dash/dash.py”, line XXXX,in serve_callback Output component_id ‘datatable-interactivity-container’ component_property ‘children’ doesn’t exist Input component_id ‘button-bis’ component_property ‘n_clicks’
Can anyone help me figure out why output component id doesn’t exist after clicking submit button?
Thanks again!
