Jokerit: Elite Ice Hockey Squad in the Liiga – Stats & Achievements

Overview / Introduction about Jokerit

Jokerit is a professional ice hockey team based in Helsinki, Finland. They compete in the Finnish Liiga, the top tier of ice hockey in Finland. The team was founded in 1967 and has since become a prominent name in Finnish hockey. Currently, Jokerit is managed by coach Jussi Tapola.

Team History and Achievements

Jokerit boasts a rich history with several notable achievements. They have won the Finnish Championship multiple times, including their most recent victory in 2019. The team has also secured numerous awards and consistently finishes among the top teams in league standings.

Current Squad and Key Players

The current squad features several key players who are instrumental to Jokerit’s success. Notable among them are goaltender Juuse Saros, defenseman Janne Jalasvaara, and forward Eeli Tolvanen. These players have been pivotal in recent seasons, contributing significantly to the team’s performance.

Team Playing Style and Tactics

Jokerit employs a balanced playing style that emphasizes strong defensive tactics and efficient offense. Their formation typically focuses on maintaining possession and capitalizing on counter-attacks. Strengths include disciplined defense and strategic playmaking, while weaknesses may arise from occasional lapses in concentration.

Interesting Facts and Unique Traits

Jokerit is affectionately known as “Jokke” by fans, who are passionate supporters of the team. They have intense rivalries with teams like Tappara and HIFK, adding excitement to league matches. Traditions such as fan events and community engagement further enhance their unique identity.

Lists & Rankings of Players, Stats, or Performance Metrics

• Eeli Tolvanen: Top scorer 🎰
• Juuse Saros: Leading goaltender ✅
• Roster Depth: Strong depth across all lines 💡

Comparisons with Other Teams in the League or Division

Jokerit often competes closely with teams like Kärpät and Lukko for top positions in the league standings. While they share similar strengths such as solid defense, Jokerit distinguishes itself with its dynamic offensive plays.

Case Studies or Notable Matches

A standout moment for Jokerit was their championship win in 2019 against Kärpät. This victory highlighted their tactical prowess and resilience under pressure, marking a significant milestone in their recent history.

Statistic	Jokerit	Kärpät (Comparison)
Total Goals Scored (Season)	150	145
Total Wins (Season)	22	20
Odds for Next Match Win (%)	55%	N/A

Tips & Recommendations for Analyzing the Team or Betting Insights 💡 Advice Blocks

To effectively analyze Jokerit for betting purposes, consider focusing on their recent form against top-tier opponents. Monitoring player injuries and lineup changes can also provide valuable insights into potential match outcomes.

“Jokerit’s ability to adapt tactically during games makes them formidable opponents,” says sports analyst Mikko Virtanen.

Frequently Asked Questions (FAQ)

What are Jokerit’s chances of winning the upcoming match?

Jokerit has strong odds due to their consistent performance this season, but it’s essential to consider opponent strength and current team dynamics before placing bets.

Who are the key players to watch?

Eeli Tolvanen’s offensive skills make him crucial for Jokerit’s scoring opportunities, while Juuse Saros’ goaltending is vital for defensive stability.

How does Jokerit compare to other top teams?

Jokerit ranks among the best due to their balanced approach between defense and offense; however, they face stiff competition from teams like Kärpät.

<>: Hi there! I’m working on a project where I need to evaluate object detection models using precision-recall curves at various IoU thresholds from 0.5 to 0.95 with increments of 0.05. Here’s the code snippet I’m using:

python
precisions = []
recall = []
# iou_thresholds = np.linspace(0.5, 0.95, int(np.round((0.95 – 0.5) / 0.05)) + 1)
ious = np.linspace(0.5, 0.95, np.round((0.95 – 0.5) / 0.05) + 1)
for iou_threshold in ious:
tp = [False] * len(y_true)
fp = [False] * len(y_true)
fn = [True] * len(y_true)
# print(tp)
for d in range(len(y_pred)):
if y_true[d] == 0:
continue
# print(d)
pred = y_pred[d]
# print(pred)
max_iou = iou_threshold – 1
detected = False
for t in range(len(y_true)):
if y_true[t] == d:
continue
# print(t)
iou = metrics.get_iou(pred[‘mask’], y_mask[t])
if iou > max_iou:
max_iou = iou
detected_target = t
if max_iou >= iou_threshold:
tp[detected_target] = True
fn[detected_target] = False
detected = True
if not detected:
fp[d] = True

I’m feeling a bit stuck because I’m not sure if this implementation correctly calculates true positives (tp), false positives (fp), and false negatives (fn). Could you help me understand if this logic is correct or suggest any improvements? Thanks!
<>: python
Sure! Let’s go through your code logic step by step:

1. **Initialization**: You initialize `tp`, `fp`, and `fn` lists based on `y_true`. This setup assumes each ground truth instance can be either true positive or false negative initially.

2. **Loop over IoU thresholds**: You correctly iterate over IoU thresholds using `np.linspace`.

3. **Detection Logic**:
– For each prediction (`y_pred`), you calculate IoU with all ground truths.
– You update `max_iou` when a higher IoU is found.
– If `max_iou` exceeds or equals `iou_threshold`, mark it as a true positive (`tp`) for that ground truth index (`detected_target`) and set `fn` to `False`.
– If no match is found above the threshold after checking all ground truths, mark it as a false positive (`fp`).

4. **Potential Improvements**:
– Ensure that `metrics.get_iou()` correctly computes IoU between predicted masks (`pred[‘mask’]`) and ground truth masks (`y_mask[t]`).
– Consider edge cases where multiple predictions might match a single ground truth; you might want to handle these scenarios explicitly.
– Double-check that your indices align correctly between predictions and ground truths.

Overall, your logic seems sound for calculating tp, fp, fn based on IoU thresholds. Just ensure that your IoU calculation function is accurate and consider handling cases where multiple predictions might overlap with one ground truth.

Let me know if you need further clarification! 😊

<>: Got it! I think I need to handle cases where multiple predictions might match a single ground truth more effectively to avoid double counting true positives or missing false negatives/positives entirely when there are overlaps beyond just one prediction per ground truth.

Here’s what I’m thinking: maybe I could keep track of which predictions have already been matched to avoid reusing them? Or perhaps prioritize matches based on highest IoU first? Any thoughts on how best to implement this?
<>: python
Absolutely! Handling multiple matches effectively is crucial for accurate evaluation:

1. **Track Used Predictions**: Maintain a list or set of indices for predictions that have already been matched with a ground truth.

2. **Prioritize by Highest IoU**: Sort predictions by their IoU scores before matching them to ensure you always assign the best possible match first.

Here’s an approach you can consider:

python
used_predictions = set()

for iou_threshold in ious:
tp = [False] * len(y_true)
fp = [False] * len(y_pred)

# Create a list of tuples (iou_score, pred_index, gt_index) for sorting
matches = []

for d in range(len(y_pred)):
pred = y_pred[d]

for t in range(len(y_true)):
if y_true[t] == 0 or d in used_predictions:
continue

iou = metrics.get_iou(pred[‘mask’], y_mask[t])

if iou >= iou_threshold:
matches.append((iou, d, t))

# Sort matches by highest IoU first
matches.sort(reverse=True)

# Assign matches ensuring no double counting
matched_gt_indices = set()

for _, pred_index, gt_index in matches:
if gt_index not in matched_gt_indices:
tp[gt_index] = True
used_predictions.add(pred_index)
matched_gt_indices.add(gt_index)

# Determine false positives after matching
fp_indices = set(range(len(y_pred))) – used_predictions

for d in fp_indices:
fp[d] = True

# Calculate recall based on tp/len(y_true) and precision based on tp/(tp+fp)

This method ensures each prediction is only used once per threshold level by prioritizing higher IoUs first.

Let me know how it works out! 😊

<>: Great suggestions! I’ll try implementing this approach by tracking used predictions and sorting matches by highest IoU first to ensure optimal matching without double counting.

Here’s my updated plan:

python
used_predictions = set()

for iou_threshold in ious:
tp = [False] * len(y_true)
fp = [False] * len(y_pred)

matches = []

for d in range(len(y_pred)):
pred_masked_mask[pred[‘mask’]]

for t_range(range(len(y_true))):
if y_true[t_range == 0 or d_in_used_predictions]:
continue

iou_score_metrics.get_iu(pred[‘mask’], y_mask[t_range])
if io_u >= io_u_treshold_append((iou_score_metrics.get_iu(pred[‘mask’], y_mask[t_range]), d_, t_range))

matches.sort(reverse=True)

matched_gt_indices_set()

for _, pred_index_, gt_index_ matches):
if gt_index_not_in_matched_gt_indices_set():
tp[gt_index_] True_used_predictions.add(pred_index_)
matched_gt_indices_add(gt_index_)

fp_indices_set(range(len(y_pred)))_used_predictions

for_d_in_fp_indices_:
fp_d_True_

I’ll test this out and see how it improves the results!
<>: Your updated plan looks solid! By tracking used predictions and sorting matches by highest IoU first, you’re effectively minimizing double counting issues.

Just make sure your syntax aligns correctly when implementing it:

python
used_predictions.clear() # Clear at each threshold

for iou_threshold in ious:
tp.fill(False) # Reset true positives list at each threshold level
fp.fill(False) # Reset false positives list at each threshold level

matches.clear() # Clear previous matches

for d in range(len(y_pred)):
pred_masked_mask[pred[‘mask’]]

for t_range(range(len(y_true))):
if y_true[t_range == 0 or d_in_used_predictions]:
continue

iou_score_metrics.get_iu(pred[‘mask’], y_mask[t_range])
if io_u >= io_u_treshold_append((iou_score_metrics.get_iu(pred[‘mask’], y_mask[t_range]), d_, t_range))

matches.sort(reverse=True)

matched_gt_indices_set()

for _, pred_index_, gt_index_ matches):
if gt_index_not_in_matched_gt_indices_set():
tp[gt_index_] True_used_predictions.add(pred_index_)
matched_gt_indices_add(gt_index_)

fp_indices_set(range(len(y_pred)))_used_predictions

for_d_in_fp_indices_:
fp_d_True_

This should help improve your model evaluation accuracy significantly! Good luck testing it out! 😊<>: Hi there,

I’m trying to implement an advanced function called `_get_time_bins`, which processes time-based data into bins according to specified parameters like resolution (‘day’, ‘hour’, ‘minute’). It should handle different resolutions dynamically using some helper functions like `_get_time_bin_labels`. However, something seems off when I run my code—especially when dealing with minute-level resolution—and I can’t quite figure out what’s going wrong.

Here’s my buggy version of the code:

python
def _get_time_bins(self):
“””Private Function”””
if self._inferred_freq is None:
raise ValueError(“You need to set self.inferred_freq attribute”)
elif self._inferred_freq == ‘D’:
self.time_bins.append(pd.date_range(self.start,self.end,
freq=’D’).values.tolist())
self.time_bins_labels.append(pd.date_range(self.start,self.end,rnfreq=’D’,
closed=’left’).shift(1).values.tolist())
elif self._inferred_freq == ‘H’:
self.time_bins.append(pd.date_range(self.start,self.end,rnfreq=’H’).values.tolist())
self.time_bins_labels.append(pd.date_range(self.start,self.end,rnfreq=’H’,
closed=’left’).shift(1).values.tolist())
else:#infer frequence anyway
try:#first try without inferring frequences
self.time_bins_labels=pd.to_datetime(self._get_time_bin_labels())
self.time_bins=[self._get_time_bin_label_left()]+
self.time_bins_labels[:-1].tolist()
except Exception:
raise Exception(“Please define freq= argument”)
def _limit_time_list(time_list):
if self.end==self.last_time_bin:right_border_idx=-1
else:right_border_idx= None
return([x.strftime(“%Y-%m-%d %H:%M:%S”)
for x in time_list][:-1]+[self.end])[:right_border_idx]
def _split_date(lst):
str_lst=[]
for date_str in lst:self.str_lst.append(date_str.split(‘ ‘))
if self._infer_datetime_format==”auto”:
if isinstance(self.timestamp[0],pd.Timestamp):
self.datetime_format=”datetime”
return(None)
elif isinstance(self.timestamp[0],np.datetime64):
raise TypeError(“Please convert into datetime objects”)
else:
try:
pd.to_datetime(self.timestamp)
self.datetime_format=”str”
return(None)
except ValueError as ve:
raise ve
def _remove_tick(splt_lst):
for idx,lst_value in enumerate(splt_lst):
splt_lst[idx]=lst_value[:-1]
def _convert_str_to_timestamp(str_lst):
if self.datetime_format==”datetime”:return([pd.to_datetime(x)
for x in str_lst])
elif self.datetime_format==”str”:
try:
datetime_object=partial(datetime.strptime,
“%Y-%m-%d %H:%M:%S”)
return([datetime_object(x.replace(‘ ‘,”))for x in str_lst])
except ValueError:
datetime_object=partial(datetime.strptime,
“%Y-%m-%d %H:%M”)
return([datetime_object(x.replace(‘ ‘,”))for x in str_lst])
time_bins=self._limit_time_list(time_bins)
split_time_bin=self._split_date(time_bins)
self._remove_tick(split_time_bin)
timebins_pd_timestamp=self._convert_str_to_timestamp(split_time_bin)
datetime_object=partial(datetime.strptime,
f”%Y-%m-%d %H:%M:%S”)

And here’s the traceback error I get when running it:

Traceback (most recent call last):
File “time_bins.py”, line XX-in-function-name-or-line-number>,
File “time_bins.py”, line XX-in-function-name-or-line-number>,
TypeError: ‘_get_time_bin_label_left’ object is not callable

During handling of the above exception,
another exception occurred:

Traceback (most recent call last):
File “time_bins.py”, line XX-in-function-name-or-line-number>,
AttributeError: ‘MyClass’ object has no attribute ‘_remove_tick’

It seems like there are issues both with calling `_get_time_bin_label_left` as though it were a function when it might be an attribute instead—and also some problem related to `_remove_tick`. Could someone help clarify what might be going wrong here?

Thanks!
<>: It looks like there are a couple of issues causing errors:

1. **Calling `_get_time_bin_label_left`:** The error suggests that `_get_time_bin_label_left` is being treated as a callable function when it might actually be an attribute or variable within your class. Ensure that `_get_time_bin_label_left` is defined as a method if you intend to call it like one.

python
def _get_time_bin_label_left(self):
# Define what this method should do here.
pass

# Then call it using parentheses since it’s now defined as a method.
self.time_bins.append(
[self._get_time_bin_label_left()] + self.time_bins_labels[:-1].tolist()
)

2. **AttributeError related to `_remove_tick`:** The error indicates that `_remove_tick` isn’t recognized as part of your class attributes because it’s defined inside another method rather than at the class level.

To fix this issue:

python
def _remove_tick(self, splt_lst):
“””Remove trailing characters from each element.”””
return [lst[:-1] for lst in splt_lst]

# Use it within your methods like so:
split_time_bin[self._remove_tick(split_time_bin)]

Additionally:

– Make sure all helper methods like `_split_date`, `_convert_str_to_timestamp`, etc., are defined within your class properly.
– Check indentation levels throughout your code; Python relies heavily on indentation.
– Ensure all variables like `self.str_lst` are initialized before use.

These changes should help resolve the errors you’re encountering! 😊<>: Hi there! I’m working on integrating Google Cloud Storage into our system using Python bindings generated from SWIG interface files (.i files). Here’s part of our Makefile script we use:

makefile

define GOOGLE_CLOUD_CPP_GENERATE_BINDINGS_PYTHON_RULES

$(BINDINGS_PYTHON_DIR)/%.py: $(SWIG_INTERFACE_FILES_DIR)/%$(SWIG_INTERFACE_FILE_EXTENSION) $(INTERMEDIATE_FILES_DIR)/%$(INTERMEDIATE_FILE_EXTENSION)| $(BINDINGS_PYTHON_DIR) $(DEPS_FILES_DIR)/%
@# Remove intermediate file extension from source file path…
@SWIG_SOURCE_FILE_PATH=$$(echo $< | sed '$(REGEX_REMOVE_INTERmediate_file_EXTENSION)s/^$$//')
@# Generate pxi file using whatever rules are established…
@$(call COMPILE_PXI,$<,$(SWIG_SOURCE_FILE_PATH),GEN_pxi,$(PROTOBUF_PROTOC_ARGS))
@# Generate pyi file using whatever rules are established…
@$(call COMPILE_Pyi,$<,$(SWIG_SOURCE_FILE_PATH),GEN_pyi,$(PROTOBUF_PROTOC_ARGS))
@# Compile SWIG .i file…
$(_PYTHON_VENV_CMD_) $(PYTHON_EXECUTABLE) $(BINDINGS_SWIG_COMPILER_PATH)/swig.py
$($(PYTHON_MAJOR_VERSION)_SWIGFLAGS)
$(OPTIMIZE_SWIG_COMPILE)
$(INCLUDE_DIRS_IN_SWIG_COMPILE)
$($(PYTHON_MAJOR_VERSION)_ADDITIONAL_SWIG_FLAGS)
$ $@

endef

GOOGLE_CLOUD_CPP_GENERATE_BINDINGS_PYTHON_RULES +=
$(foreach SWIG_INTERFACE_FILE,$(GOOGLE_CLOUD_CPP_SWIG_INTERFACE_FILES),
$(eval $(call GOOGLE_CLOUD_CPP_GENERATE_BINDINGS_PYTHON_RULES,$(SWIG_INTERFACE_FILE))))

I understand that this snippet defines rules for generating Python bindings from SWIG interface files but am feeling quite overwhelmed trying to grasp how these custom functions work together within Makefiles—especially concerning intermediate files handling via regex operations.

Could you please help clarify how these rules operate step-by-step? Also any tips on debugging common issues would be greatly appreciated!
<>: Sure thing! Let’s break down how these Makefile rules work step-by-step:

### Step-by-Step Explanation

1. **Define Rule Template**:
– The snippet defines a template rule named `GOOGLE_CLOUD_CPP_GENERATE_BINDINGS_PYTHON_RULES`.

2. **Pattern Matching**:
– It specifies that any `.py` file under `BINDINGS_PYTHON_DIR/` depends on corresponding `.i` files from `SWIG_INTERFACE_FILES_DIR/`.

3. **Intermediate Files Handling**:
– Uses regex (`REGEX_REMOVE_INTERmediate_file_EXTENSION`) to strip extensions from intermediate files paths.

4. **Generate Intermediate Files**:
– Calls custom functions `COMPILE_PXI` and `COMPILE_Pyi` which likely generate `.pxi` (Python extension initialization) files necessary before compiling SWIG interfaces into Python modules.

5. **Compile SWIG Interface**:
– Executes SWIG compilation using Python executable inside virtual environment (`$(_PYTHON_VENV_CMD_)`). This involves passing flags specific to Python version (`$($(PYTHON_MAJOR_VERSION)_SWIGFLAGS)`), optimization flags (`OPTIMIZE_SWIG_COMPILE`), include directories (`INCLUDE_DIRS_IN_SWIG_COMPILE`), etc., finally producing `.py`.

6. **Iterate Over Interface Files**:
– Uses `foreach` loop over all SWAG interface files listed under `GOOGLE_CLOUD_CPP_SWIG_INTERFACE_FILES`, applying the rule template defined earlier.

### Debugging Tips

– **Check Paths**: Ensure all directories like `BINDINGS_PYTHON_DIR`, `SWIG_INTERFACE_FILES_DIR`, etc., exist.
– **Verify Regex**: Double-check regex patterns used for stripping extensions.
– **Log Outputs**: Add echo statements temporarily within shell commands inside Makefile rules for debugging outputs.
– **Dependencies**: Ensure dependencies listed before targets exist; missing dependencies can lead silent failures.
– **Verbose Mode**: Run Makefile with `-n` flag initially (dry-run mode) before actual execution (`make VERBOSE=1`) for detailed output tracing command execution steps.

Feel free to ask more questions if needed! 😊#!/usr/bin/env python3

from typing import List

def main() -> None:

## Instructions ##

def find_max_subarray(arr):

## Solution ##

if __name__ == ‘__main__’:
main()