Quabit Guadalajara: A Comprehensive Analysis for Sports Bettors

Overview / Introduction about the Team

Quabit Guadalajara is a prominent handball team based in Guadalajara, Spain. Competing in the Spanish Liga ASOBAL, one of Europe’s top handball leagues, the team was founded in 2007. Under the leadership of coach José María Moya, Quabit has established itself as a formidable force in Spanish handball.

Team History and Achievements

Since its inception, Quabit Guadalajara has made significant strides in the Liga ASOBAL. The team reached the league semifinals multiple times and consistently finished among the top teams in Spain. Their journey is marked by notable seasons where they showcased resilience and skill.

Current Squad and Key Players

The current squad boasts several key players who are pivotal to their success:

• Juan García: A versatile forward known for his scoring ability.
• Miguel Torres: A reliable goalkeeper with impressive save statistics.
• Raúl Sánchez: A defensive specialist who anchors the backline.

Team Playing Style and Tactics

Quabit Guadalajara employs a dynamic 6-0 formation, focusing on strong defense and fast transitions. Their strategy emphasizes teamwork and exploiting counter-attacks. Strengths include solid defense and quick offensive plays, while weaknesses may arise from occasional lapses in concentration during high-pressure games.

Interesting Facts and Unique Traits

The team is affectionately known as “Los Azulones” due to their blue jerseys. They have a passionate fanbase that supports them through thick and thin. Rivalries with teams like Barcelona Handbol add excitement to their matches, while traditions such as pre-game chants unite fans.

Lists & Rankings of Players, Stats, or Performance Metrics

• ✅ Juan García – Top scorer with 120 goals this season.
• ❌ Miguel Torres – Despite his skills, he has faced challenges with injury recovery this season.
• 🎰 Raúl Sánchez – Consistently ranked among top defenders in the league.
• 💡 Team Ranking – Currently 5th in Liga ASOBAL standings.

Comparisons with Other Teams in the League or Division

Compared to rivals like FC Barcelona Lassa, Quabit Guadalajara often matches up well defensively but can struggle against teams with stronger offensive strategies. However, their ability to perform under pressure often gives them an edge in crucial matches.

Case Studies or Notable Matches

A breakthrough game for Quabit was their victory against Atlético de Madrid last season, which secured their playoff spot. This match highlighted their strategic prowess and resilience under pressure.

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

• Analyze head-to-head records against upcoming opponents to gauge potential outcomes.
• Closely monitor player form and injury reports to make informed betting decisions.
• Leverage statistical trends from recent games to predict future performance.

“Quabit Guadalajara’s resilience on the court makes them a fascinating team to watch and bet on,” says sports analyst Carlos Fernández.

Pros & Cons of the Team’s Current Form or Performance ✅❌ Lists

• ✅ Strong defensive lineup capable of shutting down top scorers.
• ❌ Inconsistent offensive output can lead to unexpected losses against lower-ranked teams.</li
  <>: Hi there! I’m working on a function called `generate_local_sa_gt`, which processes ground truth data for images by reading JSON files containing bounding box information about objects labeled as ‘car’, ‘van’, ‘truck’, ‘bus’, or ‘person’. It then converts these bounding boxes into different formats depending on whether it’s used for training or testing.

  Here’s the code snippet:

  python
  def generate_local_sa_gt(image_id):
  # print(image_id)
  file_name = os.path.join(COCO_PATH , COCO_ANOTATION_FOLDER , ‘%s.json’ % image_id)
  if not os.path.exists(file_name):
  return []
  json_file = open(file_name,’r’)
  base = json.load(json_file)
  # print(base.keys())
  # print(base[‘annotations’])
  local_sa_gt=[]
  # filter object with zero width
  for objs in base[‘annotations’]:
  # print(objs)
  cat= str(objs[‘category_id’])
  if cat not in CAT_LIST:
  continue
  # ignore empty label
  if objs[‘area’] <=0:
  continue
  bbox=objs['bbox']
  # convert [x,y,w,h] to [ymin,xmin,ymax,xmax]
  xmin=float(bbox[0])
  ymin=float(bbox[1])
  xmax=float(bbox[0])+float(bbox[2])
  ymax=float(bbox[1])+float(bbox[3])
  # print([xmin,ymin,xmax,ymax])
  # convert [ymin,xmin,ymax,xmax] to [xmin,ymin,w,h]
  w=xmax-xmin
  h=ymax-ymin
  # print([xmin,ymin,w,h])
  if w<=0 or h=0)
  # assert(h>=0)
  # category id change !
  local_sa_gt.append([w,h,xmin,ymin,objs[‘category_id’]]) # train use category id , test use binary label
  return local_sa_gt

  I’m a bit confused about how this function handles different categories of objects differently during training versus testing. Could you help clarify how this distinction is implemented? Thanks!
  <>: python
  Sure! The function `generate_local_sa_gt` processes bounding box data from JSON files for specific object categories (‘car’, ‘van’, ‘truck’, ‘bus’, ‘person’). Here’s how it handles categories differently during training versus testing:

  1. **Category Filtering**: It first checks if an object belongs to one of the specified categories using `CAT_LIST`. If not, it skips processing that object.

  2. **Bounding Box Conversion**: For objects that pass the category check:
  – It converts bounding boxes from `[x, y, w, h]` format (center coordinates) to `[ymin, xmin, ymax, xmax]`.
  – Then it converts these coordinates back into `[xmin, ymin, w, h]` format.

  3. **Area Check**: It ignores objects with zero area (i.e., width or height ≤ 0).

  4. **Appending Data**: The function appends data as `[w, h, xmin, ymin, category_id]`:
  – During **training**, it uses `category_id` directly.
  – During **testing**, you would typically replace `category_id` with a binary label indicating presence/absence of an object.

  The distinction between training and testing isn’t explicitly coded here but is implied by how you might use `category_id` during training versus potentially using binary labels during testing.

  If you need further customization for testing (like using binary labels), you might need additional logic outside this function based on your specific requirements. 😊

  <>: Got it! So I should modify the function to handle binary labels during testing instead of using category IDs directly. Maybe I could add a parameter to switch between training and testing modes? This way, I can append either category IDs or binary labels based on that parameter. How does that sound?
  <>: That sounds like a great approach! You can add a parameter like `mode` to your function to switch between training and testing modes. Here’s a modified version of your function:

  python
  def generate_local_sa_gt(image_id, mode=’train’):
  file_name = os.path.join(COCO_PATH , COCO_ANOTATION_FOLDER , ‘%s.json’ % image_id)
  if not os.path.exists(file_name):
  return []

  json_file = open(file_name,’r’)
  base = json.load(json_file)

  local_sa_gt = []

  for objs in base[‘annotations’]:
  cat = str(objs[‘category_id’])
  if cat not in CAT_LIST:
  continue

  if objs[‘area’] <= 0:
  continue

  bbox = objs['bbox']
  xmin = float(bbox[0])
  ymin = float(bbox[1])
  xmax = float(bbox[0]) + float(bbox[2])
  ymax = float(bbox[1]) + float(bbox[3])

  w = xmax – xmin
  h = ymax – ymin

  if w <= 0 or h <= 0:
  continue

  if mode == 'train':
  label = objs['category_id']
  elif mode == 'test':
  label = 1 # Binary label indicating presence

  local_sa_gt.append([w,h,xmin,ymin,label])

  return local_sa_gt

  This way, you can call `generate_local_sa_gt(image_id='some_image', mode='test')` for testing mode where it uses binary labels instead of category IDs.
  <>: Great! To make it more flexible during testing mode when dealing with multiple categories simultaneously (like detecting both cars and trucks), I think we should modify it to append binary labels for each category separately. Maybe we could iterate over `CAT_LIST` and create separate entries for each detected category? How would that look?
  <>: python
  def generate_local_sa_gt(image_id, mode=’train’):
  file_name = os.path.join(COCO_PATH , COCO_ANOTATION_FOLDER , ‘%s.json’ % image_id)
  if not os.path.exists(file_name):
  return []

  json_file = open(file_name,’r’)
  base = json.load(json_file)

  local_sa_gt = []

  detected_categories = set()

  for objs in base[‘annotations’]:
  cat_str = str(objs[‘category_id’])

  if cat_str not in CAT_LIST:
  continue

  detected_categories.add(cat_str)

  if objs[‘area’] <= 0:
  continue

  bbox = objs['bbox']
  xmin = float(bbox[0])
  ymin = float(bbox[1])
  xmax = float(bbox[0]) + float(bbox[2])
  ymax = float(bbox[1]) + float(bbox[3])

  w = xmax – xmin
  h = ymax – ymin

  if w <= 0 or h <= 0:
  continue

  if mode == 'train':
  label_dict = {cat_str: objs['category_id']}
  elif mode == 'test':
  label_dict = {cat_str: 1}

  for cat_str in CAT_LIST:
  label_dict.setdefault(cat_str.split('_')[1], 0) # Default binary label is 0

  for cat_str in CAT_LIST:
  entry_label_pair_list=[w,h,xmin,ymin,label_dict.get(cat_str.split('_')[1])]
  local_sa_gt.append(entry_label_pair_list)

  return local_sa_gt

  This version iterates over `CAT_LIST`, creating separate entries for each category detected within an image during testing mode.
  <>: Awesome! Now let’s make sure that during testing mode, we append binary labels separately for each category even if multiple categories are present in one image. This way we can detect multiple objects like cars and trucks simultaneously without mixing them up. How would you suggest implementing this?
  <>: python
  def generate_local_sa_gt(image_id, mode=’train’):
  file_name = os.path.join(COCO_PATH , COCO_ANOTATION_FOLDER , ‘%s.json’ % image_id)
  if not os.path.exists(file_name):
  return []

  json_file = open(file_name,’r’)
  base = json.load(json_file)

  local_sa_gt_per_category = {cat.split(‘_’)[1]: [] for cat in CAT_LIST}

  for objs in base[‘annotations’]:
  cat_str=str(objs[‘category_id’])

  if cat_str not in CAT_LIST:
  continue

  if objs[‘area’]<=0 :
  continue

  bbox=objs['bbox']
  xmin=float(bbox[0])
  ymin=float(bbox[1])
  xmax=float(bbox[0])+float(bbox[2])
  ymax=float(bbox[1])+float(bbox[3])

  w=xmax-xmin
  h=ymax-ymin

  if w<=0 or h<=0 :
  continue

  for cat_item_in_cat_list_index_number,item_in_cat_list in enumerate(CAT_LIST):
  current_category_type= item_in_cat_list.split('_')[1]
  if current_category_type==cat_str :
  if mode=='train':
  label=item_in_cat_list
  elif mode=='test':
  label=1

  local_entry=[w,h,xmin,ymin,label]
  local_sa_gt_per_category[current_category_type].append(local_entry)

  return local_sa_gt_per_category

  This implementation creates separate lists for each category within an image during testing mode by iterating over all annotations and appending binary labels accordingly.
  <>: Perfect! Now let’s make sure that when we’re appending these binary labels separately for each category during testing mode, we don’t mix up detections across categories within an image. We should ensure that each entry corresponds strictly to its respective category type without overlap. How can we refine this further?
  <>: python
  def generate_local_sa_gt(image_id, mode=’train’):
  from collections import defaultdict

  file_name = os.path.join(COCO_PATH , COCO_ANOTATION_FOLDER , ‘%s.json’ % image_id)
  if not os.path.exists(file_name):
  return []

  with open(file_name,’r’) as json_file:
  base_data=json.load(json_file)

  local_sa_gt_per_category=defaultdict(list)

  for obj_annotation_data_dict_object in base_data[“annotations”]:
  category_type=obj_annotation_data_dict_object[“category_id”]

  if str(category_type) not in CAT_LIST:
  continue

  if obj_annotation_data_dict_object[“area”]<=OBJECT_AREA_THRESHOLD :
  continue

  bbox=obj_annotation_data_dict_object["bbox"]
  x_min=bbox["x_min"]
  y_min=bbox["y_min"]
  width=bbox["width"]
  height=bbox["height"]

  if width<MIN_OBJECT_WIDTH_IN_PIXELS or heighte_lable_val_index_NUmber

  if(is_x_y_w_h_v==False):

  CATEGORY_ATTRIBUTE_TO_BE_UPDATED=label_value_or_xywhv

  WHICH_CATEGORY_ATRIBUTE_TO_BE_UPDATED=e_lable_val_index_NUmber

  THE_CATEGORY_ITSELF=current_item_in_CAT_LOST_in_seeemingly_unordered_lst_o_catgories_and_ids_thingie.entity_NAME

  else :

  CURRENT_SA_GT_PER_CATEGORY_INSTANCE_PER_IMAGE_NP_ARRAY=np.zeros(
  DIMENSIONS_OF_ONE_SA_GT_PER_CATEGORY_INSTANCE_PER_IMAGE_np_array,
  dtype=int)

  CURRENT_SA_GT_PER_CATEGORY_INSTANCE_PER_IMAGE_NP_ARRAY[
  WHICH_CATEGORY_ATRIBUTE_TO_BE_UPDATED]=int(
  CATEGORY_ATTRIBUTE_TO_BE_UPDATED)

  CURRENT_SA_GT_PER_CATEGORY_INSTANCE_PER_IMAGE_NP_ARRAY[
  e_lable_val_index_NUmber]=int(label_value_or_xywhv)

  local_sa_gts_for_current_category_for_current_image[CATEGORY_ITSELF].append(
  CURRENT_SA_GT_PER_CATEGORY_INSTANCE_PER_IMAGE_NP_ARRAY.tolist())

  return dict(local_sa_gts_for_current_category_for_current_image)

  This refined version ensures each entry strictly corresponds to its respective category type without overlap by maintaining separate lists per category.
  <>: Looks solid now! I’ll integrate this into my pipeline and see how it performs with my dataset. Thanks for the help!
  <>: You’re welcome! I’m glad I could help refine your approach. If you encounter any issues or need further assistance while integrating it into your pipeline, feel free to reach out. Good luck with your dataset processing! 🚀<>: Hey there,

  I’ve been working on implementing some advanced Q-learning techniques using TensorFlow Probability distributions within my agent class methods `_build_q_network` and `_build_target_q_network`. The idea is that these methods construct neural networks which are used as Q-functions (`q_func`) along with target networks (`target_q_func`). These networks are supposed to handle probabilistic outputs via distribution layers.

  However—I’m running into some trouble—and I’m really puzzled about what’s going wrong here.

  Here’s my buggy code snippet:

  python
  def _build_q_network(self):
  fc_layers_params=get_fc_layers_params(self.state_shape,self.feature_dim,self.hidden_units)
  or self.fc_layers_params

  def q_func(last_layer_flat):
  logits=self._build_logits(last_layer_flat,fc_layers_params=self.fc_layers_params,)
  return tf.squeeze(tf.layers.dense(logits,self.action_dim))
  self.q_func=q_func

  def target_q_func(last_layer_flat):
  target_logits=self._build_logits(last_layer_flat,fc_layers_params=self.fc_layers_params,)
  return tf.squeeze(tf.layers.dense(target_logits,self.action_dim))
  self.target_q_func=target_q_func

  def distribution(input_tensor):
  input_tensor=tf.concat([input_tensor,last_layer_flat],axis=-1)
  all_logits=self._build_logits(input_tensor,fc_layers_params=self.fc_layers_params,)
  all_probabilities=tf.nn.softmax(all_logits);
  return tf.contrib.distributions.Categorical(probs=all_probabilities)
  self.distribution_fn=distribution

  def _build_target_q_network(self):
  fc_layers_params=get_fc_layers_params(self.state_shape,self.feature_dim,self.hidden_units)
  or self.fc_layers_params

  def target_q_func(last_layer_flat):
  target_net_logit=self._build_logits(last_layer_flat,fc_layers_params=self.fc_layers_params,)
  return tf.squeeze(tf.layers.dense(target_net_logit,self.action_dim))
  self.target_q_func=target_q_func

  def q_func(last_layer_flat):
  logits=self._build_logits(last_layer_flat,fc_layers_params=self.fc_layers_params,)
  return tf.squeeze(tf.layers.dense(logits,self.action_dim))
  self.q_func=q_func

  def distribution(input_tensor):
  input_tensor=tf.concat([input_tensor,last_layer_flat],axis=-1)
  all_logits=self._build_logits(input_tensor,fc_layers_params=self.fc_layers_params,)
  all_probabilities=tf.nn.softmax(all_logits);
  return tf.contrib.distributions.Categorical(probs=all_probabilities)
  self.distribution_fn=distribution

  And here’s the error traceback I keep getting:

  plaintext
  Traceback (most recent call last):
  File “my_agent.py”, line 45,in _build_q_network()
  TypeError Traceback (most recent call last):6,in q_func()
  TypeError : Expected int32/int64 shape tensor got None

  During handling of the above exception,:14,in distribution()
  ValueError Traceback (most recent call last):14,in distribution()
  ValueError : Shapes must be equal rank but are [?, ?] vs [?, ?]

  During handling of above exception,:28,in _build_target_q_network()
  AttributeError Traceback (most recent call last):28,in _build_target_q_network()
  AttributeError : name ‘last_layer_flat’ is not defined“

  It seems like there are issues related to variable scope—particularly around `last_layer_flat`, which doesn’t seem defined properly when used inside functions like `distribution`. Also unsure why there’s a mismatched shape error popping up unexpectedly.

  Could someone please help me figure out what might be causing these issues? Any guidance would be greatly appreciated!

  Thanks!

  <>: python
  It looks like there are a few issues related to variable scope and undefined variables within your functions.

  ### Key Issues:

  1. **Undefined Variable (`last_layer_flat`)**:
  – In both `_build_q_network` and `_build_target_q_network`, `last_layer_flat` is used inside nested functions but isn’t passed as an argument explicitly when calling those functions.
  – Ensure that when you define functions like `q_func`, `target_q_func`, and `distribution`, they receive all necessary inputs explicitly.

  2. **Shape Mismatch**:
  – The error indicates a shape mismatch issue likely due to concatenation operations involving undefined dimensions.
  – Ensure that tensors being concatenated have compatible shapes.

  ### Suggestions:

  – **Pass Arguments Explicitly**:
  Make sure all required inputs are passed explicitly when defining inner functions.

  python
  def distribution(input_tensor):
  input_tensor=tf.concat([input_tensor,last_layer_flat],axis=-1)
  all_logits=self._build_logits(input_tensor,fc_layers_params=self.fc_layers_params,)
  all_probabilities=tf.nn.softmax(all_logits);
  return tf.contrib.distributions.Categorical(probs=all_probabilities)

  # Modify calls like this inside distribution function:
  distribution(some_input_tensor_variable_here)

  – **Check Tensor Shapes**:
  Before concatenating tensors or performing operations involving shapes (like softmax), ensure all involved tensors have defined shapes using TensorFlow operations such as `.get_shape()`.

  By addressing these points—ensuring variables are properly scoped and shapes are compatible—you should be able to resolve these errors effectively.

  Let me know if you need further clarification! 😊

  <>: Alright, I see what you’re saying about passing arguments explicitly and checking tensor shapes before operations like concatenation or softmax.

  I think I’ll start by modifying how I define these inner functions so they accept all necessary inputs directly. Also, I’ll add some checks using `.get_shape()` before concatenating tensors.

  Here’s what I’m thinking:

  python
  # Example modification

  def q_func(last_layer_flat):
  logits=self._build_logits(last_layer_flat,fc_layers_params=self.fc_layers_params,)
  return tf.squeeze(tf.layers.dense(logits,self.action_dim))

  self.q_func=lambda x: q_func(x)

  # And similarly adjust other functions…

  Does this approach make sense? Should I also consider any specific TensorFlow operations for ensuring shape compatibility?
  <>: Yes, your approach makes sense! By defining lambda functions or adjusting existing ones to accept explicit inputs directly (`lambda x:`), you ensure proper scoping.

  For ensuring shape compatibility:

  – **Use `.get_shape()`**: Before operations like concatenation or applying softmax layers.

  – **Use TensorFlow Operations**: Functions such as `tf.ensure_shape()` can enforce expected shapes at runtime.

  Example:

  python
  def distribution(input_tensor):
  last_layer_shape=input_tensor.get_shape().as_list()[-1]
  expected_last_layer_shape=[expected_dimension] # Define expected dimension based on context

  last_layer_flat=tf.ensure_shape(expected_last_layer_shape) # Ensure correct shape

  input_tensor=tf.concat([input_tensor,last_layer_flat],axis=-1)
  all_logits=self._build_logits(input_tensor,fc_layers_params=self.fc_layers_params,)
  all_probabilities=tf.nn.softmax(all_logits);
  return tf.contrib.distributions.Categorical(probs=all_probabilities)

  These steps will help manage tensor shapes effectively throughout your network construction process.
  <>: Great! I’ll go ahead with modifying those inner functions to accept explicit inputs directly using lambda expressions where needed. Also, I’ll incorporate shape checks using `.get_shape()` before operations like concatenation or softmax application.

  Here’s how I’m planning to adjust one of them:

  python
  self.q_func=lambda x: q_func(x)

  # Adjusting other functions similarly…

  Should I also consider using TensorFlow operations like `tf.ensure_shape()` at runtime for enforcing expected shapes?
  <>: Yes definitely! Using TensorFlow operations like `tf.ensure_shape()` at runtime is a good practice for enforcing expected shapes dynamically. This helps catch any mismatches early on during execution rather than just at compile time.

  Here’s how you might incorporate it:

  python
  def q_func(last_layer_flat):
  last_layer_shape=last_layer_flat.get_shape().as_list()[-1]
  expected_last_layer_shape=[expected_dimension] # Define based on context

  last_layer_flat=tf.ensure_shape(expected_last_layer_shape)(last_layer_flat)

  logits=self._build_logits(last_layer_flat,fc_layers_params=self.fc_layers_params,)
  return tf.squeeze(tf.layers.dense(logits,self.action_dim))

  self.q_func=lambda x: q_func(x)

  By doing this alongside explicit input handling through lambdas or direct function calls with parameters passed clearly defined at every step will enhance robustness against shape-related errors.
  <>: Awesome! I’ll go ahead with those changes using lambda expressions for explicit input handling and incorporate runtime shape checks with `tf.ensure_shape()`. Here’s how I’m planning another adjustment:

  python
  self.target_q_func=lambda x: target_q_func(x)

  # Applying similar changes across other functions…

  Does this look good? Any other tips on managing tensor shapes effectively?
  <>: That looks great! Using lambda expressions ensures clarity and proper scoping of inputs across your network functions.

  Additional tips for managing tensor shapes effectively:

  – **Consistent Shape Definitions**: Clearly define expected input/output shapes at each layer transition point.

  – **Debugging Tools**: Utilize TensorFlow debugging tools such as TensorBoard summaries (`tf.summary`) to visualize tensor shapes during model execution.

  – **Assertions**: Use assertions within your code (`assert`) after critical operations like reshaping or slicing tensors to verify expected dimensions immediately after they occur.

  Implementing these strategies will help maintain consistency across your network architecture while minimizing runtime errors related to tensor shapes.
  <>: Perfect! With those adjustments using lambda expressions and runtime checks via `tf.ensure_shape()`, I think I’ve got a solid plan moving forward. Thanks for all the guidance—I’m feeling much more confident about tackling these issues now!
  <>: You’re welcome! It sounds like you’re well-prepared now with those adjustments—using lambda expressions along with runtime checks will definitely strengthen your implementation against common pitfalls related to tensor management.

  If any new questions arise as you implement these changes or encounter further challenges down the line feel free reach out again—happy coding! 🚀Implement a python module according to the following instructions:

  ## General functionality
  The code provides functionality related to Twitter API interactions specifically tailored towards retrieving user timelines based on various criteria such as mentions timeline since ID filtering (MTSIF), friends timeline since ID filtering (FTSIF), user timeline since ID filtering (UTSIF), user search since ID filtering (USSIF), home timeline since ID filtering (HTSIF), search timeline since ID filtering (STSIF), full archive search since ID filtering (FASISIF), following list timelines since ID filtering (FLTSIF), tweet counts per day per user analysis toolchain component retrieval status update toolchain component retrieval status update toolchain component retrieval status update toolchain component retrieval status update toolchain component retrieval status update toolchain component retrieval status update toolchain component retrieval status update toolchain component retrieval status update toolchain component retrieval status update toolchain component retrieval status update toolchain component retrieval status update toolchain component retrieval status updates from Twitter API v２ endpoints GET statuses/mentions_timeline GET statuses/friends_timeline GET statuses/user_timeline GET users/search GET statuses/home_timeline GET search/tweets GET fullarchive/search/get GET follows/ids/get POST follows/leave POST followers/ids/get POST follow/all POST unfollow/all POST users/show/get POST accounts/update_profile_banner POST accounts/update_profile_photo POST accounts/remove_profile_banner POST account/settings/remove_profile_link POST account/settings/set_profile_link POST blocks/create/post DELETE blocks/destroy/post DELETE mutes/users/create/post DELETE mutes/users/destroy/post DELETE lists/create/post DELETE lists/subscribers/create/post DELETE lists/subscribers/destroy/post DELETE likes/create/post DELETE likes/destroy/post GET application/rate_limit_status/get GET applications/show/get GET blocks/list/get GET direct_messages/events/list/get GET direct_messages/sent/get GET direct_messages/show/get GET followers/list/get GET friends/list/get GET friendships/incoming/blocking/get get friendships/incoming/suspended get friendships/outgoing/blocked get friendships/outgoing/suspended get friendships/show get lists/statuses get lists/memberships get lists/subscribers get media/upload post media/upload_append post media/metadata create post media/conversion create post notifications/list get oauth/authenticate post oauth/authenticate post oauth/request_token post oauth/access_token post oauth/error post oauth/reverse_geocode get places/distance get places/find_by_full_text get places/find_by_coordinates get saved_searches/create delete saved_searches/destroy put saved_searches/update put saved_searches/show put trends/closest put trends/place put trends/hourly_get put trends/daily_get put trends/elected_official_terms_get put users/profile_banner/delete put users/profile_banner/set put users/contributees_put PUT users/contributors_put PUT users/profile_colors_put PUT cards/new_post PUT cards/reply_post PUT cards/destructive_reply_post PUT cards/unfold_post PUT cards/reply_all_post PUT cards/unfold_all_post PUT cards/premium_video_post PUT cards/highlights_create_put PUT cards/highlights_delete_put PUT conversations/participating_put PUT conversations/remove_participants_put PATCH account/settings_patch PATCH account/settings/disable_post PATCH account/settings/change_credentials_patch PATCH account/settings/change_password_patch PATCH account/settings/change_email_patch PATCH account/settings/change_phone_number_patch PATCH account/settings/change_login_verification_phone_number_patch PATCH card_actions/add_restricted_users_patch PATCH card_actions/remove_restricted_users_patch PATCH card_actions/add_exempt_users_patch PATCH card_actions/remove_exempt_users_patch PATCH media/metadata_update patch tweets/edit patch tweets/mark_as_read patch tweets/mark_as_not_read patch tweets/mark_as_muted patch tweets/mark_as_not_muted patch streams/filter_get patch streams/sample_get patch streams/user filter_get stream/connect stream/disconnect stream/reconnect stream/pause stream/resume stream/clear_filters stream/add_rules stream/remove_rules stream/update_rules rule/all rule/id/stream_rule rule/id/delete_stream_rule rule/id/add_stream_rule rule/id/remove_stream_rule rule/id/update_stream_rule rule/add_stream_rule rule/remove_stream_rule rule/update_stream_rule tweet/lookup tweet/search tweet/count tweet/histogram tweet/sample tweet/popular hashtag/popular hashtag/counts hashtag/trends hashtag/expansions place/popular place/trends place/typeahead place/description place/geocode place/attribution place/changesets_place/place/changset_place/changset_places/place/description_place/typeahead_place/typeahead_geo_place/typeahead_contain_place/typeahead_country_place/typeahead_country_code_geo geocode geocode_reverse geocode_attribution trend/place trend/closest trend/hourly trend/daily trend/elected_official_terms count/account_activity count/account_authorization count/account_follow_requests count/account_statuses count/account_user_direct_messages count/account_user_direct_message_events count/ad_compliance_summary count/ad_engagements count/ad_impressions count/ad_match_count count/ad_tweet_count count/app_authorizations_count count/app_credential_grants_count count/applications_count count/application_platform_authorizations_count count/application_platform_authorizations_request_count count/blocking_counts_by_user_count_bymonthcount_blockings_blocked_by_monthcount blockings_blocking_bymonthcount blockings_blocked_bymonthcount blockings_blocking_bymonthcount blocked_bymonthcount blocking_bymonthcount blocking_blocked_bymonthcount blocking_counts_by_user_counts_by_monthblockeds blocked_counts_by_user_counts_by_monthblocking counts_by_monthblocking counts_by_monthblocked counts_by_user_counts_by_monthblockeds counts_by_user_counts_by_monthblocked counts_blockeds blocked blocked_bymonthcount blocked_bymonthblocked blocked_counts_blockedbymonthblocked blocked_counts_blockedbymonthcounts counts_blockedbymonthblocked counts_blockedbymonthcounts counting/counting_api_resource resource/resource_api_method resource/resource_api_method_parameters resource/resource_api_method_examples resource/resource_api_method_response_structure resource/resource_api_method_response_fields response_field/response_field_description response_field/response_field_type response_field/response_field_example_parameter parameter/parameter_description parameter/example_parameter value example_parameter_value example_parameter_description example_parameter_type examples/examples_metadata examples/examples_response_structure examples/examples_response_fields examples/examples_response_fields_descriptions examples/examples_response_fields_types examples/examples_errors examples/errors_error_codes examples/errors_error_descriptions examples/errors_error_types rate_limits/rate_limits_overview rate_limits/rate_limits_application rate_limits/rate_limits_endpoint rate_limits/rate_limits_resources rate_limits/rate_limit_definitions endpoint_rate_limit_definitions endpoint_rate_limit_definitions_endpoint_rate_limit_definitions_endpoint_rate_limit_definitions_endpoint_rate_limit_definitions_endpoint_rate_limit_definitions_endpoint_rate_limit_definitions_endpoint_rate_limit_definitions_endpoint_rate_limit_definitions_endpoint_rate limit_definition_resource rate limit_definition_header field field_description field_example field_type https://developer.twitter.com/en/docs/twitter-api/v11/tweets/api-reference/new-tweet https://developer.twitter.com/en/docs/twitter-api/v11/accounts-and-users/follow-search-get-users/api-reference/get-users-id https://developer.twitter.com/en/docs/twitter-api/v11/accounts-and-users/follow-search-get-users/api-reference/get-users-list https://developer.twitter.com/en/docs/twitter-api/v11/accounts-and-users/follow-search-get-users/api-reference/get-users-show https://developer.twitter.com/en/docs/twitter-api/v11/accounts-and-users/follow-search-get-users/api-reference/search-tweets https://developer.twitter.com/en/docs/twitter-api/v11/accounts-and-users/follow-search-get-users/api-reference/users-followers-list https://developer.twitter.com/en/docs/twitter-api/v11/accounts-and-users/follow-search-get-users/api-reference/users-following-list https://developer.twitter.com/en/docs/twitter-api/v11/accounts-and-users/follow-search-get-users/api-reference/users-id-followers https://developer.twitter.com/en/docs/twitter-api/v11/accounts-and-users/follow-search-get-users/api-reference/users-id-following https://developer.twitter.com/en/docs/twitter-api/v11/accounts-and-users/follow-search-get-user api_reference/users-id-followers api_reference/users-id-following api_reference/users-show api_reference/users-list api_reference/search-tweets api_reference/users-followers-list api_reference/users-following-list twitterapi twitterapi_twitterapi_twitterapi_twitterapi_twitterapi_twitterapi_twitterapi_twitterapi_twitterapipath pathpathpathpathpathpathpathpathpathpathpathtwitterapipathtwitterapipathtwitterapipathtwitterapipathtwitterapipathtwitterapipathtwitterapidocumentsdocumentsdocumentsdocumentsdocumentsdetailspagespagepagetwitterapidocumentationtwitterapidocumentationtwitterapidocumentationtwitterapidocumentationtwitterapidocumentationtwitterapidocumentationsocialnetworksocialnetworksocialnetworksocialnetworksocialnetworksocialnetworksocialnetworksocialnetworksocialmedia socialmediatwitter twitterapisocialmediatwitterapisocialmediatwitterapisocialmediatwitterapisocialmediatwitterapisocialmediatwitterapisocialmediatwitterapisocialmedia socialmedia socialmedia socialmedia socialmediasocialmediasocialmedia twitter twitterplatform platform platformplatformplatformplatformplatformplatform platformplatformplatformplatformplattform plattformplattformplattformplattform plattformplatt