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tqdm | A Fast , Extensible Progress Bar for Python and CLI | Command Line Interface library

 by   tqdm Python Version: v4.63.2 License: Non-SPDX

 by   tqdm Python Version: v4.63.2 License: Non-SPDX

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kandi X-RAY | tqdm Summary

tqdm is a Python library typically used in Utilities, Command Line Interface applications. tqdm has no bugs, it has no vulnerabilities, it has build file available and it has high support. However tqdm has a Non-SPDX License. You can install using 'pip install tqdm' or download it from GitHub, PyPI.
A Fast, Extensible Progress Bar for Python and CLI
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kandi-support Support

  • tqdm has a highly active ecosystem.
  • It has 21618 star(s) with 1113 fork(s). There are 202 watchers for this library.
  • There were 3 major release(s) in the last 12 months.
  • There are 274 open issues and 547 have been closed. On average issues are closed in 383 days. There are 65 open pull requests and 0 closed requests.
  • It has a negative sentiment in the developer community.
  • The latest version of tqdm is v4.63.2
tqdm Support
Best in #Command Line Interface
Average in #Command Line Interface
tqdm Support
Best in #Command Line Interface
Average in #Command Line Interface

quality kandi Quality

  • tqdm has 0 bugs and 0 code smells.
tqdm Quality
Best in #Command Line Interface
Average in #Command Line Interface
tqdm Quality
Best in #Command Line Interface
Average in #Command Line Interface

securitySecurity

  • tqdm has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • tqdm code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
tqdm Security
Best in #Command Line Interface
Average in #Command Line Interface
tqdm Security
Best in #Command Line Interface
Average in #Command Line Interface

license License

  • tqdm has a Non-SPDX License.
  • Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
tqdm License
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Average in #Command Line Interface
tqdm License
Best in #Command Line Interface
Average in #Command Line Interface

buildReuse

  • tqdm releases are available to install and integrate.
  • Deployable package is available in PyPI.
  • Build file is available. You can build the component from source.
  • It has 5564 lines of code, 438 functions and 62 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
tqdm Reuse
Best in #Command Line Interface
Average in #Command Line Interface
tqdm Reuse
Best in #Command Line Interface
Average in #Command Line Interface
Top functions reviewed by kandi - BETA

kandi has reviewed tqdm and discovered the below as its top functions. This is intended to give you an instant insight into tqdm implemented functionality, and help decide if they suit your requirements.

  • Process instances
    • Get the lock
    • Get instances of tqdm_cls
    • Refresh the widget
    • A context manager that allows you to change the bars
  • Display the progress bar
    • Close progress bar
    • Format a time
    • Format a time bar
  • Run the progress bar
    • Write text to chat
      • Write text to file
        • A parallel map function
          • Decorator to return the shape of the screen
            • Progress bar
              • Convert docstring to rst
                • Display the tqdm progress bar
                  • Set the description
                    • Writes a pipe to fout
                      • Edit the message text
                        • Returns a function that prints the status of the given file
                          • Reset the parameters
                            • Apply a function to a map
                              • A context manager that temporarily clears the bar
                                • Cast val to given typ
                                  • Create a tqdm progress bar
                                    • Initialize tqdm progress bar

                                      Get all kandi verified functions for this library.

                                      Get all kandi verified functions for this library.

                                      tqdm Key Features

                                      A Fast, Extensible Progress Bar for Python and CLI

                                      Get the postfix string in tqdm

                                      copy iconCopydownload iconDownload
                                      import numpy as np
                                      from tqdm import tqdm
                                      
                                      a = np.random.randint(0, 10, 10)
                                      loop_obj = tqdm(np.arange(10))
                                      
                                      for i in loop_obj:
                                          loop_obj.set_postfix_str(f"Current count: {i}")
                                          a = i*2/3  # Do some operations
                                          loop_obj.set_postfix_str(loop_obj.postfix + f" After processing: {a}")
                                      

                                      BeautifulSoup and pd.read_html - how to save the links into separate column in the final dataframe?

                                      copy iconCopydownload iconDownload
                                      for i in table[:3]:
                                          df = pd.read_html(str(i))[0]
                                          df['address'] = link
                                          details.append(df)
                                      
                                      final_df = pd.concat(details, ignore_index=True)
                                      
                                      import pandas as pd
                                      
                                      links = ['www.link1.com', 'www.link2.com','www.linkx.com']
                                      details = []
                                      
                                      for link in links:
                                          # page = requests.get(link)
                                          # sauce = BeautifulSoup(page.content, 'lxml')
                                          # table = sauce.find_all('table')
                                          table = ['<table><tr><td>table 1</td></tr></table>',
                                                   '<table><tr><td>table 2</td></tr></table>',
                                                   '<table><tr><td>table 3</td></tr></table>']
                                          # Only first 3 tables include data
                                          for i in table[:3]:
                                              df = pd.read_html(str(i))[0]
                                              df['address'] = link
                                              details.append(df)
                                      
                                      final_df = pd.concat(details, ignore_index=True)
                                      
                                      for i in table[:3]:
                                          df = pd.read_html(str(i))[0]
                                          df['address'] = link
                                          details.append(df)
                                      
                                      final_df = pd.concat(details, ignore_index=True)
                                      
                                      import pandas as pd
                                      
                                      links = ['www.link1.com', 'www.link2.com','www.linkx.com']
                                      details = []
                                      
                                      for link in links:
                                          # page = requests.get(link)
                                          # sauce = BeautifulSoup(page.content, 'lxml')
                                          # table = sauce.find_all('table')
                                          table = ['<table><tr><td>table 1</td></tr></table>',
                                                   '<table><tr><td>table 2</td></tr></table>',
                                                   '<table><tr><td>table 3</td></tr></table>']
                                          # Only first 3 tables include data
                                          for i in table[:3]:
                                              df = pd.read_html(str(i))[0]
                                              df['address'] = link
                                              details.append(df)
                                      
                                      final_df = pd.concat(details, ignore_index=True)
                                      
                                      for i in table[:3]:
                                          df = pd.read_html(str(i))[0]
                                          df['address'] = link
                                          details.append(df)
                                      
                                      final_df = pd.concat(details, ignore_index=True)
                                      
                                      import pandas as pd
                                      
                                      links = ['www.link1.com', 'www.link2.com','www.linkx.com']
                                      details = []
                                      
                                      for link in links:
                                          # page = requests.get(link)
                                          # sauce = BeautifulSoup(page.content, 'lxml')
                                          # table = sauce.find_all('table')
                                          table = ['<table><tr><td>table 1</td></tr></table>',
                                                   '<table><tr><td>table 2</td></tr></table>',
                                                   '<table><tr><td>table 3</td></tr></table>']
                                          # Only first 3 tables include data
                                          for i in table[:3]:
                                              df = pd.read_html(str(i))[0]
                                              df['address'] = link
                                              details.append(df)
                                      
                                      final_df = pd.concat(details, ignore_index=True)
                                      

                                      tf2.0: Gradient Tape returns None gradient in RNN model

                                      copy iconCopydownload iconDownload
                                      # Your imports
                                      #-------
                                      ### 2. Simulated data and gradient computation:
                                      batch_size = 100; input_length = 5
                                      xtr_pad = tf.random.uniform((batch_size, input_length), maxval = 500, dtype=tf.int32)
                                      ytr = tf.random.normal((batch_size, input_length, 200))
                                      
                                      
                                      inp= Input(batch_shape= (batch_size, input_length), name= 'input') 
                                      emb_out= Embedding(500, 100, input_length= input_length, trainable= False, name= 'embedding')(inp)
                                      rnn= SimpleRNN(200, return_sequences= True, return_state= False, stateful= True, name= 'simpleRNN')
                                      
                                      h0 = tf.Variable(tf.random.uniform((batch_size, 200)))
                                      
                                      rnn_allstates= rnn(emb_out, initial_state=h0) 
                                      model_rnn = Model(inputs=inp, outputs= rnn_allstates, name= 'model_rnn')
                                      model_rnn.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])
                                      
                                      ds = tf.data.Dataset.from_tensor_slices((xtr_pad, ytr)).batch(100)
                                      embedding_layer = model_rnn.layers[1]
                                      rnn_layer = model_rnn.layers[2]
                                      
                                      
                                      @tf.function
                                      def calculate_t_gradients(t, x, h0):
                                        return tf.gradients(model_rnn(x)[:,t,:], h0)
                                      
                                      grads_allsteps= []
                                      for b, (x_batch_train, y_batch_train) in enumerate(ds):
                                          for t in range(input_length):  
                                            grads_allsteps.append(calculate_t_gradients(t, x_batch_train, h0))
                                       
                                      print(grads_allsteps) 
                                      
                                      [[<tf.Tensor: shape=(100, 200), dtype=float32, numpy=
                                      array([[ 1.2034059 , -0.46448404,  0.6272926 , ..., -0.40906236,
                                               0.07618493,  0.6338958 ],
                                             [ 1.2781916 , -0.20411322,  0.6174417 , ..., -0.31636393,
                                              -0.23417974,  0.67499626],
                                             [ 1.113218  , -0.65086263,  0.63425934, ..., -0.66614366,
                                              -0.07726163,  0.53647137],
                                             ...,
                                             [ 1.3399608 , -0.54088974,  0.6213518 , ...,  0.00831087,
                                              -0.14397278,  0.2614633 ],
                                             [ 1.213171  , -0.42787278,  0.60535026, ..., -0.56198204,
                                              -0.09142771,  0.6212783 ],
                                             [ 1.1901733 , -0.5743524 ,  0.36872283, ..., -0.42522985,
                                              -0.0861398 ,  0.495057  ]], dtype=float32)>], [<tf.Tensor: shape=(100, 200), dtype=float32, numpy=
                                      array([[ 0.3487598 ,  1.2738569 , -0.48500937, ...,  0.6011117 ,
                                              -0.20381093,  0.45596513],
                                             [ 0.37931004,  1.2778724 , -0.8682532 , ...,  0.8170228 ,
                                               0.1456329 ,  0.23715591],
                                             [ 0.5984771 ,  0.92434835, -0.8879645 , ...,  0.38756457,
                                              -0.17436962,  0.47174054],
                                             ...,
                                             [ 0.61081064,  0.99631476, -0.5104377 , ...,  0.5042721 ,
                                               0.02844866,  0.34626445],
                                             [ 0.7126102 ,  1.0205276 , -0.60710275, ...,  0.49418694,
                                              -0.16092762,  0.41363668],
                                             [ 0.8581749 ,  1.1259711 , -0.5824491 , ...,  0.45388597,
                                              -0.16205123,  0.72434616]], dtype=float32)>], [<tf.Tensor: shape=(100, 200), dtype=float32, numpy=
                                      array([[ 3.8507193e-01,  1.2925258e+00,  1.2027258e+00, ...,
                                               3.2430276e-01,  2.2319333e-01, -2.5218868e-01],
                                             [ 5.9262186e-01,  1.4497797e+00,  1.2479483e+00, ...,
                                               4.6175608e-01,  2.5466472e-01, -2.4279505e-01],
                                             [ 2.5734475e-01,  1.4562432e+00,  1.1020679e+00, ...,
                                               6.6081107e-01,  1.9841105e-01, -2.5595558e-01],
                                             ...,
                                             [ 5.1541841e-01,  1.6206543e+00,  9.6205616e-01, ...,
                                               7.2725344e-01,  2.5501373e-01, -7.7709556e-04],
                                             [ 4.4518453e-01,  1.6381552e+00,  1.0112666e+00, ...,
                                               5.5238277e-01,  2.4137528e-01, -2.6242572e-01],
                                             [ 6.6721851e-01,  1.5826726e+00,  1.1282607e+00, ...,
                                               3.2301426e-01,  2.2295776e-01,  1.1724380e-01]], dtype=float32)>], [<tf.Tensor: shape=(100, 200), dtype=float32, numpy=
                                      array([[ 0.14262576,  0.578709  ,  0.1149607 , ...,  0.1229499 ,
                                              -0.42344815,  0.8837458 ],
                                             [-0.09711604,  0.04376438, -0.11737494, ...,  0.00389774,
                                               0.01737173,  0.17246482],
                                             [ 0.24414796,  0.30101255, -0.12234146, ..., -0.04850931,
                                              -0.31790918,  0.21326394],
                                             ...,
                                             [-0.20562285,  0.21999156,  0.02703794, ..., -0.03547464,
                                              -0.59052145,  0.04695258],
                                             [ 0.2087476 ,  0.46558812, -0.18172565, ..., -0.01167884,
                                              -0.20868361,  0.09055485],
                                             [-0.22442941,  0.16119067,  0.10854454, ...,  0.14752978,
                                              -0.32307786,  0.343314  ]], dtype=float32)>], [<tf.Tensor: shape=(100, 200), dtype=float32, numpy=
                                      array([[-1.1414615 ,  0.37376842, -1.0230722 , ...,  0.60619426,
                                               0.22550163, -0.6948315 ],
                                             [-1.0124328 ,  0.27892357, -0.96915233, ...,  0.7048603 ,
                                              -0.15284726, -0.6734605 ],
                                             [-0.8542529 ,  0.25970122, -0.90076745, ...,  0.8825682 ,
                                              -0.02474228, -0.55014515],
                                             ...,
                                             [-0.89430666,  0.68327624, -1.0109956 , ...,  0.31722566,
                                              -0.23703958, -0.6766514 ],
                                             [-0.8633691 ,  0.28742114, -0.9896866 , ...,  0.98315084,
                                               0.0115847 , -0.55474746],
                                             [-0.7229766 ,  0.62417865, -1.2342371 , ...,  0.85149145,
                                              -0.04468453, -0.60606724]], dtype=float32)>]]
                                      
                                      # Your imports
                                      #-------
                                      ### 2. Simulated data and gradient computation:
                                      batch_size = 100; input_length = 5
                                      xtr_pad = tf.random.uniform((batch_size, input_length), maxval = 500, dtype=tf.int32)
                                      ytr = tf.random.normal((batch_size, input_length, 200))
                                      
                                      
                                      inp= Input(batch_shape= (batch_size, input_length), name= 'input') 
                                      emb_out= Embedding(500, 100, input_length= input_length, trainable= False, name= 'embedding')(inp)
                                      rnn= SimpleRNN(200, return_sequences= True, return_state= False, stateful= True, name= 'simpleRNN')
                                      
                                      h0 = tf.Variable(tf.random.uniform((batch_size, 200)))
                                      
                                      rnn_allstates= rnn(emb_out, initial_state=h0) 
                                      model_rnn = Model(inputs=inp, outputs= rnn_allstates, name= 'model_rnn')
                                      model_rnn.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])
                                      
                                      ds = tf.data.Dataset.from_tensor_slices((xtr_pad, ytr)).batch(100)
                                      embedding_layer = model_rnn.layers[1]
                                      rnn_layer = model_rnn.layers[2]
                                      
                                      
                                      @tf.function
                                      def calculate_t_gradients(t, x, h0):
                                        return tf.gradients(model_rnn(x)[:,t,:], h0)
                                      
                                      grads_allsteps= []
                                      for b, (x_batch_train, y_batch_train) in enumerate(ds):
                                          for t in range(input_length):  
                                            grads_allsteps.append(calculate_t_gradients(t, x_batch_train, h0))
                                       
                                      print(grads_allsteps) 
                                      
                                      [[<tf.Tensor: shape=(100, 200), dtype=float32, numpy=
                                      array([[ 1.2034059 , -0.46448404,  0.6272926 , ..., -0.40906236,
                                               0.07618493,  0.6338958 ],
                                             [ 1.2781916 , -0.20411322,  0.6174417 , ..., -0.31636393,
                                              -0.23417974,  0.67499626],
                                             [ 1.113218  , -0.65086263,  0.63425934, ..., -0.66614366,
                                              -0.07726163,  0.53647137],
                                             ...,
                                             [ 1.3399608 , -0.54088974,  0.6213518 , ...,  0.00831087,
                                              -0.14397278,  0.2614633 ],
                                             [ 1.213171  , -0.42787278,  0.60535026, ..., -0.56198204,
                                              -0.09142771,  0.6212783 ],
                                             [ 1.1901733 , -0.5743524 ,  0.36872283, ..., -0.42522985,
                                              -0.0861398 ,  0.495057  ]], dtype=float32)>], [<tf.Tensor: shape=(100, 200), dtype=float32, numpy=
                                      array([[ 0.3487598 ,  1.2738569 , -0.48500937, ...,  0.6011117 ,
                                              -0.20381093,  0.45596513],
                                             [ 0.37931004,  1.2778724 , -0.8682532 , ...,  0.8170228 ,
                                               0.1456329 ,  0.23715591],
                                             [ 0.5984771 ,  0.92434835, -0.8879645 , ...,  0.38756457,
                                              -0.17436962,  0.47174054],
                                             ...,
                                             [ 0.61081064,  0.99631476, -0.5104377 , ...,  0.5042721 ,
                                               0.02844866,  0.34626445],
                                             [ 0.7126102 ,  1.0205276 , -0.60710275, ...,  0.49418694,
                                              -0.16092762,  0.41363668],
                                             [ 0.8581749 ,  1.1259711 , -0.5824491 , ...,  0.45388597,
                                              -0.16205123,  0.72434616]], dtype=float32)>], [<tf.Tensor: shape=(100, 200), dtype=float32, numpy=
                                      array([[ 3.8507193e-01,  1.2925258e+00,  1.2027258e+00, ...,
                                               3.2430276e-01,  2.2319333e-01, -2.5218868e-01],
                                             [ 5.9262186e-01,  1.4497797e+00,  1.2479483e+00, ...,
                                               4.6175608e-01,  2.5466472e-01, -2.4279505e-01],
                                             [ 2.5734475e-01,  1.4562432e+00,  1.1020679e+00, ...,
                                               6.6081107e-01,  1.9841105e-01, -2.5595558e-01],
                                             ...,
                                             [ 5.1541841e-01,  1.6206543e+00,  9.6205616e-01, ...,
                                               7.2725344e-01,  2.5501373e-01, -7.7709556e-04],
                                             [ 4.4518453e-01,  1.6381552e+00,  1.0112666e+00, ...,
                                               5.5238277e-01,  2.4137528e-01, -2.6242572e-01],
                                             [ 6.6721851e-01,  1.5826726e+00,  1.1282607e+00, ...,
                                               3.2301426e-01,  2.2295776e-01,  1.1724380e-01]], dtype=float32)>], [<tf.Tensor: shape=(100, 200), dtype=float32, numpy=
                                      array([[ 0.14262576,  0.578709  ,  0.1149607 , ...,  0.1229499 ,
                                              -0.42344815,  0.8837458 ],
                                             [-0.09711604,  0.04376438, -0.11737494, ...,  0.00389774,
                                               0.01737173,  0.17246482],
                                             [ 0.24414796,  0.30101255, -0.12234146, ..., -0.04850931,
                                              -0.31790918,  0.21326394],
                                             ...,
                                             [-0.20562285,  0.21999156,  0.02703794, ..., -0.03547464,
                                              -0.59052145,  0.04695258],
                                             [ 0.2087476 ,  0.46558812, -0.18172565, ..., -0.01167884,
                                              -0.20868361,  0.09055485],
                                             [-0.22442941,  0.16119067,  0.10854454, ...,  0.14752978,
                                              -0.32307786,  0.343314  ]], dtype=float32)>], [<tf.Tensor: shape=(100, 200), dtype=float32, numpy=
                                      array([[-1.1414615 ,  0.37376842, -1.0230722 , ...,  0.60619426,
                                               0.22550163, -0.6948315 ],
                                             [-1.0124328 ,  0.27892357, -0.96915233, ...,  0.7048603 ,
                                              -0.15284726, -0.6734605 ],
                                             [-0.8542529 ,  0.25970122, -0.90076745, ...,  0.8825682 ,
                                              -0.02474228, -0.55014515],
                                             ...,
                                             [-0.89430666,  0.68327624, -1.0109956 , ...,  0.31722566,
                                              -0.23703958, -0.6766514 ],
                                             [-0.8633691 ,  0.28742114, -0.9896866 , ...,  0.98315084,
                                               0.0115847 , -0.55474746],
                                             [-0.7229766 ,  0.62417865, -1.2342371 , ...,  0.85149145,
                                              -0.04468453, -0.60606724]], dtype=float32)>]]
                                      
                                      emb_layer= model_rnn.layers[1]; rnn_layer= model_rnn.layers[2]
                                      n_steps = 40  
                                      
                                      dhtdh0_rnn= []
                                      for t in range(n_steps):
                                          with tf.GradientTape() as tape:
                                              tape.watch(h0)
                                              et= emb_layer(xtr_pad[:100])
                                              ht_all= rnn_layer(et, initial_state= [h0]) 
                                              ht= ht_all[:,t,:,]
                                              dhtdh0_t= tape.gradient(ht, h0)
                                              grad_agg= tf.reduce_mean(abs(dhtdh0_t), [0,1])
                                              print('step', t+1, 'done')
                                              dhtdh0_rnn.append(np.log(grad_agg))
                                              del tape
                                      

                                      PIP failed to build package cytoolz

                                      copy iconCopydownload iconDownload
                                      python -m pip install --user cython
                                      python -m pip install --user cytoolz
                                      python -m pip install --user eth-brownie
                                      
                                      STEP1: python -m pip install --user cython
                                      STEP2: python -m pip install --user cytoolz
                                      STEP3: python -m pip install --user eth-brownie
                                      STEP4: python -m pip install --user pipx
                                      STEP5: python -m pipx ensurepath
                                      STEP6: RESTARTED TERMINAL
                                      STEP7: pipx install eth-brownie
                                      

                                      How does a gradient backpropagates through random samples?

                                      copy iconCopydownload iconDownload
                                      mu, std = out_RL[0]
                                      dist = Normal(mu, std)
                                      a = dist.sample()
                                      log_p = dist.log_prob(a)
                                      
                                      mu, std = out_RL[0]
                                      dist = Normal(mu, std)
                                      # a = dist.sample()
                                      a = torch.tensor([1.23, 4.01, -1.2, ...], device='cuda')
                                      log_p = dist.log_prob(a)
                                      
                                      mu, std = out_RL[0].detach()
                                      
                                      dist = Normal(mu, std)
                                      log_p = dist.log_prob(a)
                                      
                                      mu, std = out_RL[0]
                                      dist = Normal(mu, std)
                                      a = dist.sample()
                                      log_p = dist.log_prob(a)
                                      
                                      mu, std = out_RL[0]
                                      dist = Normal(mu, std)
                                      # a = dist.sample()
                                      a = torch.tensor([1.23, 4.01, -1.2, ...], device='cuda')
                                      log_p = dist.log_prob(a)
                                      
                                      mu, std = out_RL[0].detach()
                                      
                                      dist = Normal(mu, std)
                                      log_p = dist.log_prob(a)
                                      
                                      mu, std = out_RL[0]
                                      dist = Normal(mu, std)
                                      a = dist.sample()
                                      log_p = dist.log_prob(a)
                                      
                                      mu, std = out_RL[0]
                                      dist = Normal(mu, std)
                                      # a = dist.sample()
                                      a = torch.tensor([1.23, 4.01, -1.2, ...], device='cuda')
                                      log_p = dist.log_prob(a)
                                      
                                      mu, std = out_RL[0].detach()
                                      
                                      dist = Normal(mu, std)
                                      log_p = dist.log_prob(a)
                                      
                                      mu, std = out_RL[0]
                                      dist = Normal(mu, std)
                                      a = dist.sample()
                                      log_p = dist.log_prob(a)
                                      
                                      mu, std = out_RL[0]
                                      dist = Normal(mu, std)
                                      # a = dist.sample()
                                      a = torch.tensor([1.23, 4.01, -1.2, ...], device='cuda')
                                      log_p = dist.log_prob(a)
                                      
                                      mu, std = out_RL[0].detach()
                                      
                                      dist = Normal(mu, std)
                                      log_p = dist.log_prob(a)
                                      

                                      How to speed up async requests in Python

                                      copy iconCopydownload iconDownload
                                      async def fetch(start, end):
                                          # example
                                          url = "https://httpbin.org/anything/log?id="
                                          async with aiohttp.ClientSession() as session:
                                              post_tasks = []
                                              # prepare the coroutines that poat
                                              # use start and end arguments here!
                                              async for x in make_numbers(start, end):
                                                  post_tasks.append(do_get(session, url, x))
                                              # now execute them all at once
                                      
                                              responses = [await f for f in
                                                           tqdm.tqdm(asyncio.as_completed(post_tasks), total=len(post_tasks))]
                                      
                                      import concurrent.futures
                                      from itertools import count
                                      
                                      def one_executor(start, end):
                                          loop = asyncio.new_event_loop()
                                          try:
                                              loop.run_until_complete(fetch(start, end))
                                          except:
                                              print("error")
                                      
                                      
                                      if __name__ == '__main__':
                                      
                                          s = time.perf_counter()
                                          # Change the value to the number of core you want to use.
                                          max_worker = 4
                                          length_by_executor = q // max_worker
                                          with concurrent.futures.ProcessPoolExecutor(max_workers=max_worker) as executor:
                                              for index_min in count(0, length_by_executor):
                                                  # no matter with duplicated indexes due to the use of 
                                                  # range in make_number function.
                                                  index_max = min(index_min + length_by_executor, q)
                                                  executor.submit(one_executor, index_min, index_max)
                                                  if index_max == q:
                                                      break
                                      
                                          elapsed = time.perf_counter() - s
                                          print(f"executed in {elapsed:0.2f} seconds.")
                                      
                                      1 worker: executed in 13.90 seconds.
                                      2 workers: executed in 7.24 seconds.
                                      3 workers: executed in 6.82 seconds.
                                      
                                      async def fetch(start, end):
                                          # example
                                          url = "https://httpbin.org/anything/log?id="
                                          async with aiohttp.ClientSession() as session:
                                              post_tasks = []
                                              # prepare the coroutines that poat
                                              # use start and end arguments here!
                                              async for x in make_numbers(start, end):
                                                  post_tasks.append(do_get(session, url, x))
                                              # now execute them all at once
                                      
                                              responses = [await f for f in
                                                           tqdm.tqdm(asyncio.as_completed(post_tasks), total=len(post_tasks))]
                                      
                                      import concurrent.futures
                                      from itertools import count
                                      
                                      def one_executor(start, end):
                                          loop = asyncio.new_event_loop()
                                          try:
                                              loop.run_until_complete(fetch(start, end))
                                          except:
                                              print("error")
                                      
                                      
                                      if __name__ == '__main__':
                                      
                                          s = time.perf_counter()
                                          # Change the value to the number of core you want to use.
                                          max_worker = 4
                                          length_by_executor = q // max_worker
                                          with concurrent.futures.ProcessPoolExecutor(max_workers=max_worker) as executor:
                                              for index_min in count(0, length_by_executor):
                                                  # no matter with duplicated indexes due to the use of 
                                                  # range in make_number function.
                                                  index_max = min(index_min + length_by_executor, q)
                                                  executor.submit(one_executor, index_min, index_max)
                                                  if index_max == q:
                                                      break
                                      
                                          elapsed = time.perf_counter() - s
                                          print(f"executed in {elapsed:0.2f} seconds.")
                                      
                                      1 worker: executed in 13.90 seconds.
                                      2 workers: executed in 7.24 seconds.
                                      3 workers: executed in 6.82 seconds.
                                      
                                      async def fetch(start, end):
                                          # example
                                          url = "https://httpbin.org/anything/log?id="
                                          async with aiohttp.ClientSession() as session:
                                              post_tasks = []
                                              # prepare the coroutines that poat
                                              # use start and end arguments here!
                                              async for x in make_numbers(start, end):
                                                  post_tasks.append(do_get(session, url, x))
                                              # now execute them all at once
                                      
                                              responses = [await f for f in
                                                           tqdm.tqdm(asyncio.as_completed(post_tasks), total=len(post_tasks))]
                                      
                                      import concurrent.futures
                                      from itertools import count
                                      
                                      def one_executor(start, end):
                                          loop = asyncio.new_event_loop()
                                          try:
                                              loop.run_until_complete(fetch(start, end))
                                          except:
                                              print("error")
                                      
                                      
                                      if __name__ == '__main__':
                                      
                                          s = time.perf_counter()
                                          # Change the value to the number of core you want to use.
                                          max_worker = 4
                                          length_by_executor = q // max_worker
                                          with concurrent.futures.ProcessPoolExecutor(max_workers=max_worker) as executor:
                                              for index_min in count(0, length_by_executor):
                                                  # no matter with duplicated indexes due to the use of 
                                                  # range in make_number function.
                                                  index_max = min(index_min + length_by_executor, q)
                                                  executor.submit(one_executor, index_min, index_max)
                                                  if index_max == q:
                                                      break
                                      
                                          elapsed = time.perf_counter() - s
                                          print(f"executed in {elapsed:0.2f} seconds.")
                                      
                                      1 worker: executed in 13.90 seconds.
                                      2 workers: executed in 7.24 seconds.
                                      3 workers: executed in 6.82 seconds.
                                      
                                      # async with aiohttp.ClientSession() as session:
                                      async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=200)) as session:
                                      
                                      async def make_async_gen(f, n, q):
                                          async for x in make_numbers(n, q):
                                              yield f(x)
                                      
                                      from asyncio import ensure_future, events
                                      from asyncio.queues import Queue
                                      
                                      def as_completed_for_async_gen(fs_async_gen, concurrency):
                                          done = Queue()
                                          loop = events.get_event_loop()
                                          # todo = {ensure_future(f, loop=loop) for f in set(fs)}  # -
                                          todo = set()                                             # +
                                      
                                          def _on_completion(f):
                                              todo.remove(f)
                                              done.put_nowait(f)
                                              loop.create_task(_add_next())  # +
                                      
                                          async def _wait_for_one():
                                              f = await done.get()
                                              return f.result()
                                      
                                          async def _add_next():  # +
                                              try:
                                                  f = await fs_async_gen.__anext__()
                                              except StopAsyncIteration:
                                                  return
                                              f = ensure_future(f, loop=loop)
                                              f.add_done_callback(_on_completion)
                                              todo.add(f)
                                      
                                          # for f in todo:                           # -
                                          #     f.add_done_callback(_on_completion)  # -
                                          # for _ in range(len(todo)):               # -
                                          #     yield _wait_for_one()                # -
                                          for _ in range(concurrency):               # +
                                              loop.run_until_complete(_add_next())   # +
                                          while todo:                                # +
                                              yield _wait_for_one()                  # +
                                      
                                      from functools import partial
                                      
                                      CONCURRENCY = 200  # +
                                      
                                      n = 0
                                      q = 50_000_000
                                      
                                      async def fetch():
                                          # example
                                          url = "https://httpbin.org/anything/log?id="
                                      
                                          async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=CONCURRENCY)) as session:
                                              # post_tasks = []                                                # -
                                              # # prepare the coroutines that post                             # -
                                              # async for x in make_numbers(n, q):                             # -
                                              #     post_tasks.append(do_get(session, url, x))                 # -
                                              # Prepare the coroutines generator                               # +
                                              async_gen = make_async_gen(partial(do_get, session, url), n, q)  # +
                                      
                                              # now execute them all at once                                                                         # -
                                              # responses = [await f for f in tqdm.asyncio.tqdm.as_completed(post_tasks, total=len(post_tasks))]     # -
                                              # Now execute them with a specified concurrency                                                        # +
                                              responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]  # +
                                      
                                      # responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]
                                      for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q):
                                          response = await f
                                          
                                          # Do something with response, such as writing to a local file
                                          # ...
                                      
                                      async def do_get(session, url, x):
                                          headers = {
                                              'Content-Type': "application/x-www-form-urlencoded",
                                              'Access-Control-Allow-Origin': "*",
                                              'Accept-Encoding': "gzip, deflate",
                                              'Accept-Language': "en-US"
                                          }
                                      
                                          async with session.get(url + str(x), headers=headers) as response:
                                              data = await response.text()
                                              # print(data)  # -
                                              return data    # +
                                      
                                      # async with aiohttp.ClientSession() as session:
                                      async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=200)) as session:
                                      
                                      async def make_async_gen(f, n, q):
                                          async for x in make_numbers(n, q):
                                              yield f(x)
                                      
                                      from asyncio import ensure_future, events
                                      from asyncio.queues import Queue
                                      
                                      def as_completed_for_async_gen(fs_async_gen, concurrency):
                                          done = Queue()
                                          loop = events.get_event_loop()
                                          # todo = {ensure_future(f, loop=loop) for f in set(fs)}  # -
                                          todo = set()                                             # +
                                      
                                          def _on_completion(f):
                                              todo.remove(f)
                                              done.put_nowait(f)
                                              loop.create_task(_add_next())  # +
                                      
                                          async def _wait_for_one():
                                              f = await done.get()
                                              return f.result()
                                      
                                          async def _add_next():  # +
                                              try:
                                                  f = await fs_async_gen.__anext__()
                                              except StopAsyncIteration:
                                                  return
                                              f = ensure_future(f, loop=loop)
                                              f.add_done_callback(_on_completion)
                                              todo.add(f)
                                      
                                          # for f in todo:                           # -
                                          #     f.add_done_callback(_on_completion)  # -
                                          # for _ in range(len(todo)):               # -
                                          #     yield _wait_for_one()                # -
                                          for _ in range(concurrency):               # +
                                              loop.run_until_complete(_add_next())   # +
                                          while todo:                                # +
                                              yield _wait_for_one()                  # +
                                      
                                      from functools import partial
                                      
                                      CONCURRENCY = 200  # +
                                      
                                      n = 0
                                      q = 50_000_000
                                      
                                      async def fetch():
                                          # example
                                          url = "https://httpbin.org/anything/log?id="
                                      
                                          async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=CONCURRENCY)) as session:
                                              # post_tasks = []                                                # -
                                              # # prepare the coroutines that post                             # -
                                              # async for x in make_numbers(n, q):                             # -
                                              #     post_tasks.append(do_get(session, url, x))                 # -
                                              # Prepare the coroutines generator                               # +
                                              async_gen = make_async_gen(partial(do_get, session, url), n, q)  # +
                                      
                                              # now execute them all at once                                                                         # -
                                              # responses = [await f for f in tqdm.asyncio.tqdm.as_completed(post_tasks, total=len(post_tasks))]     # -
                                              # Now execute them with a specified concurrency                                                        # +
                                              responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]  # +
                                      
                                      # responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]
                                      for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q):
                                          response = await f
                                          
                                          # Do something with response, such as writing to a local file
                                          # ...
                                      
                                      async def do_get(session, url, x):
                                          headers = {
                                              'Content-Type': "application/x-www-form-urlencoded",
                                              'Access-Control-Allow-Origin': "*",
                                              'Accept-Encoding': "gzip, deflate",
                                              'Accept-Language': "en-US"
                                          }
                                      
                                          async with session.get(url + str(x), headers=headers) as response:
                                              data = await response.text()
                                              # print(data)  # -
                                              return data    # +
                                      
                                      # async with aiohttp.ClientSession() as session:
                                      async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=200)) as session:
                                      
                                      async def make_async_gen(f, n, q):
                                          async for x in make_numbers(n, q):
                                              yield f(x)
                                      
                                      from asyncio import ensure_future, events
                                      from asyncio.queues import Queue
                                      
                                      def as_completed_for_async_gen(fs_async_gen, concurrency):
                                          done = Queue()
                                          loop = events.get_event_loop()
                                          # todo = {ensure_future(f, loop=loop) for f in set(fs)}  # -
                                          todo = set()                                             # +
                                      
                                          def _on_completion(f):
                                              todo.remove(f)
                                              done.put_nowait(f)
                                              loop.create_task(_add_next())  # +
                                      
                                          async def _wait_for_one():
                                              f = await done.get()
                                              return f.result()
                                      
                                          async def _add_next():  # +
                                              try:
                                                  f = await fs_async_gen.__anext__()
                                              except StopAsyncIteration:
                                                  return
                                              f = ensure_future(f, loop=loop)
                                              f.add_done_callback(_on_completion)
                                              todo.add(f)
                                      
                                          # for f in todo:                           # -
                                          #     f.add_done_callback(_on_completion)  # -
                                          # for _ in range(len(todo)):               # -
                                          #     yield _wait_for_one()                # -
                                          for _ in range(concurrency):               # +
                                              loop.run_until_complete(_add_next())   # +
                                          while todo:                                # +
                                              yield _wait_for_one()                  # +
                                      
                                      from functools import partial
                                      
                                      CONCURRENCY = 200  # +
                                      
                                      n = 0
                                      q = 50_000_000
                                      
                                      async def fetch():
                                          # example
                                          url = "https://httpbin.org/anything/log?id="
                                      
                                          async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=CONCURRENCY)) as session:
                                              # post_tasks = []                                                # -
                                              # # prepare the coroutines that post                             # -
                                              # async for x in make_numbers(n, q):                             # -
                                              #     post_tasks.append(do_get(session, url, x))                 # -
                                              # Prepare the coroutines generator                               # +
                                              async_gen = make_async_gen(partial(do_get, session, url), n, q)  # +
                                      
                                              # now execute them all at once                                                                         # -
                                              # responses = [await f for f in tqdm.asyncio.tqdm.as_completed(post_tasks, total=len(post_tasks))]     # -
                                              # Now execute them with a specified concurrency                                                        # +
                                              responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]  # +
                                      
                                      # responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]
                                      for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q):
                                          response = await f
                                          
                                          # Do something with response, such as writing to a local file
                                          # ...
                                      
                                      async def do_get(session, url, x):
                                          headers = {
                                              'Content-Type': "application/x-www-form-urlencoded",
                                              'Access-Control-Allow-Origin': "*",
                                              'Accept-Encoding': "gzip, deflate",
                                              'Accept-Language': "en-US"
                                          }
                                      
                                          async with session.get(url + str(x), headers=headers) as response:
                                              data = await response.text()
                                              # print(data)  # -
                                              return data    # +
                                      
                                      # async with aiohttp.ClientSession() as session:
                                      async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=200)) as session:
                                      
                                      async def make_async_gen(f, n, q):
                                          async for x in make_numbers(n, q):
                                              yield f(x)
                                      
                                      from asyncio import ensure_future, events
                                      from asyncio.queues import Queue
                                      
                                      def as_completed_for_async_gen(fs_async_gen, concurrency):
                                          done = Queue()
                                          loop = events.get_event_loop()
                                          # todo = {ensure_future(f, loop=loop) for f in set(fs)}  # -
                                          todo = set()                                             # +
                                      
                                          def _on_completion(f):
                                              todo.remove(f)
                                              done.put_nowait(f)
                                              loop.create_task(_add_next())  # +
                                      
                                          async def _wait_for_one():
                                              f = await done.get()
                                              return f.result()
                                      
                                          async def _add_next():  # +
                                              try:
                                                  f = await fs_async_gen.__anext__()
                                              except StopAsyncIteration:
                                                  return
                                              f = ensure_future(f, loop=loop)
                                              f.add_done_callback(_on_completion)
                                              todo.add(f)
                                      
                                          # for f in todo:                           # -
                                          #     f.add_done_callback(_on_completion)  # -
                                          # for _ in range(len(todo)):               # -
                                          #     yield _wait_for_one()                # -
                                          for _ in range(concurrency):               # +
                                              loop.run_until_complete(_add_next())   # +
                                          while todo:                                # +
                                              yield _wait_for_one()                  # +
                                      
                                      from functools import partial
                                      
                                      CONCURRENCY = 200  # +
                                      
                                      n = 0
                                      q = 50_000_000
                                      
                                      async def fetch():
                                          # example
                                          url = "https://httpbin.org/anything/log?id="
                                      
                                          async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=CONCURRENCY)) as session:
                                              # post_tasks = []                                                # -
                                              # # prepare the coroutines that post                             # -
                                              # async for x in make_numbers(n, q):                             # -
                                              #     post_tasks.append(do_get(session, url, x))                 # -
                                              # Prepare the coroutines generator                               # +
                                              async_gen = make_async_gen(partial(do_get, session, url), n, q)  # +
                                      
                                              # now execute them all at once                                                                         # -
                                              # responses = [await f for f in tqdm.asyncio.tqdm.as_completed(post_tasks, total=len(post_tasks))]     # -
                                              # Now execute them with a specified concurrency                                                        # +
                                              responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]  # +
                                      
                                      # responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]
                                      for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q):
                                          response = await f
                                          
                                          # Do something with response, such as writing to a local file
                                          # ...
                                      
                                      async def do_get(session, url, x):
                                          headers = {
                                              'Content-Type': "application/x-www-form-urlencoded",
                                              'Access-Control-Allow-Origin': "*",
                                              'Accept-Encoding': "gzip, deflate",
                                              'Accept-Language': "en-US"
                                          }
                                      
                                          async with session.get(url + str(x), headers=headers) as response:
                                              data = await response.text()
                                              # print(data)  # -
                                              return data    # +
                                      
                                      # async with aiohttp.ClientSession() as session:
                                      async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=200)) as session:
                                      
                                      async def make_async_gen(f, n, q):
                                          async for x in make_numbers(n, q):
                                              yield f(x)
                                      
                                      from asyncio import ensure_future, events
                                      from asyncio.queues import Queue
                                      
                                      def as_completed_for_async_gen(fs_async_gen, concurrency):
                                          done = Queue()
                                          loop = events.get_event_loop()
                                          # todo = {ensure_future(f, loop=loop) for f in set(fs)}  # -
                                          todo = set()                                             # +
                                      
                                          def _on_completion(f):
                                              todo.remove(f)
                                              done.put_nowait(f)
                                              loop.create_task(_add_next())  # +
                                      
                                          async def _wait_for_one():
                                              f = await done.get()
                                              return f.result()
                                      
                                          async def _add_next():  # +
                                              try:
                                                  f = await fs_async_gen.__anext__()
                                              except StopAsyncIteration:
                                                  return
                                              f = ensure_future(f, loop=loop)
                                              f.add_done_callback(_on_completion)
                                              todo.add(f)
                                      
                                          # for f in todo:                           # -
                                          #     f.add_done_callback(_on_completion)  # -
                                          # for _ in range(len(todo)):               # -
                                          #     yield _wait_for_one()                # -
                                          for _ in range(concurrency):               # +
                                              loop.run_until_complete(_add_next())   # +
                                          while todo:                                # +
                                              yield _wait_for_one()                  # +
                                      
                                      from functools import partial
                                      
                                      CONCURRENCY = 200  # +
                                      
                                      n = 0
                                      q = 50_000_000
                                      
                                      async def fetch():
                                          # example
                                          url = "https://httpbin.org/anything/log?id="
                                      
                                          async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=CONCURRENCY)) as session:
                                              # post_tasks = []                                                # -
                                              # # prepare the coroutines that post                             # -
                                              # async for x in make_numbers(n, q):                             # -
                                              #     post_tasks.append(do_get(session, url, x))                 # -
                                              # Prepare the coroutines generator                               # +
                                              async_gen = make_async_gen(partial(do_get, session, url), n, q)  # +
                                      
                                              # now execute them all at once                                                                         # -
                                              # responses = [await f for f in tqdm.asyncio.tqdm.as_completed(post_tasks, total=len(post_tasks))]     # -
                                              # Now execute them with a specified concurrency                                                        # +
                                              responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]  # +
                                      
                                      # responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]
                                      for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q):
                                          response = await f
                                          
                                          # Do something with response, such as writing to a local file
                                          # ...
                                      
                                      async def do_get(session, url, x):
                                          headers = {
                                              'Content-Type': "application/x-www-form-urlencoded",
                                              'Access-Control-Allow-Origin': "*",
                                              'Accept-Encoding': "gzip, deflate",
                                              'Accept-Language': "en-US"
                                          }
                                      
                                          async with session.get(url + str(x), headers=headers) as response:
                                              data = await response.text()
                                              # print(data)  # -
                                              return data    # +
                                      
                                      # async with aiohttp.ClientSession() as session:
                                      async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=200)) as session:
                                      
                                      async def make_async_gen(f, n, q):
                                          async for x in make_numbers(n, q):
                                              yield f(x)
                                      
                                      from asyncio import ensure_future, events
                                      from asyncio.queues import Queue
                                      
                                      def as_completed_for_async_gen(fs_async_gen, concurrency):
                                          done = Queue()
                                          loop = events.get_event_loop()
                                          # todo = {ensure_future(f, loop=loop) for f in set(fs)}  # -
                                          todo = set()                                             # +
                                      
                                          def _on_completion(f):
                                              todo.remove(f)
                                              done.put_nowait(f)
                                              loop.create_task(_add_next())  # +
                                      
                                          async def _wait_for_one():
                                              f = await done.get()
                                              return f.result()
                                      
                                          async def _add_next():  # +
                                              try:
                                                  f = await fs_async_gen.__anext__()
                                              except StopAsyncIteration:
                                                  return
                                              f = ensure_future(f, loop=loop)
                                              f.add_done_callback(_on_completion)
                                              todo.add(f)
                                      
                                          # for f in todo:                           # -
                                          #     f.add_done_callback(_on_completion)  # -
                                          # for _ in range(len(todo)):               # -
                                          #     yield _wait_for_one()                # -
                                          for _ in range(concurrency):               # +
                                              loop.run_until_complete(_add_next())   # +
                                          while todo:                                # +
                                              yield _wait_for_one()                  # +
                                      
                                      from functools import partial
                                      
                                      CONCURRENCY = 200  # +
                                      
                                      n = 0
                                      q = 50_000_000
                                      
                                      async def fetch():
                                          # example
                                          url = "https://httpbin.org/anything/log?id="
                                      
                                          async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=CONCURRENCY)) as session:
                                              # post_tasks = []                                                # -
                                              # # prepare the coroutines that post                             # -
                                              # async for x in make_numbers(n, q):                             # -
                                              #     post_tasks.append(do_get(session, url, x))                 # -
                                              # Prepare the coroutines generator                               # +
                                              async_gen = make_async_gen(partial(do_get, session, url), n, q)  # +
                                      
                                              # now execute them all at once                                                                         # -
                                              # responses = [await f for f in tqdm.asyncio.tqdm.as_completed(post_tasks, total=len(post_tasks))]     # -
                                              # Now execute them with a specified concurrency                                                        # +
                                              responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]  # +
                                      
                                      # responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]
                                      for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q):
                                          response = await f
                                          
                                          # Do something with response, such as writing to a local file
                                          # ...
                                      
                                      async def do_get(session, url, x):
                                          headers = {
                                              'Content-Type': "application/x-www-form-urlencoded",
                                              'Access-Control-Allow-Origin': "*",
                                              'Accept-Encoding': "gzip, deflate",
                                              'Accept-Language': "en-US"
                                          }
                                      
                                          async with session.get(url + str(x), headers=headers) as response:
                                              data = await response.text()
                                              # print(data)  # -
                                              return data    # +
                                      

                                      Colab: (0) UNIMPLEMENTED: DNN library is not found

                                      copy iconCopydownload iconDownload
                                      !pip install tensorflow==2.7.0
                                      
                                      'tensorflow==2.7.0',
                                      'tf-models-official==2.7.0',
                                      'tensorflow_io==0.23.1',
                                      

                                      Cannot find conda info. Please verify your conda installation on EMR

                                      copy iconCopydownload iconDownload
                                      wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.9.2-Linux-x86_64.sh  -O /home/hadoop/miniconda.sh \
                                          && /bin/bash ~/miniconda.sh -b -p $HOME/conda
                                      
                                      echo -e '\n export PATH=$HOME/conda/bin:$PATH' >> $HOME/.bashrc && source $HOME/.bashrc
                                      
                                      
                                      conda config --set always_yes yes --set changeps1 no
                                      conda config -f --add channels conda-forge
                                      
                                      
                                      conda create -n zoo python=3.7 # "zoo" is conda environment name
                                      conda init bash
                                      source activate zoo
                                      conda install python 3.7.0 -c conda-forge orca 
                                      sudo /home/hadoop/conda/envs/zoo/bin/python3.7 -m pip install virtualenv
                                      
                                      “spark.pyspark.python": "/home/hadoop/conda/envs/zoo/bin/python3",
                                      "spark.pyspark.virtualenv.enabled": "true",
                                      "spark.pyspark.virtualenv.type":"native",
                                      "spark.pyspark.virtualenv.bin.path":"/home/hadoop/conda/envs/zoo/bin/,
                                      "zeppelin.pyspark.python" : "/home/hadoop/conda/bin/python",
                                      "zeppelin.python": "/home/hadoop/conda/bin/python"
                                      
                                      wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.9.2-Linux-x86_64.sh  -O /home/hadoop/miniconda.sh \
                                          && /bin/bash ~/miniconda.sh -b -p $HOME/conda
                                      
                                      echo -e '\n export PATH=$HOME/conda/bin:$PATH' >> $HOME/.bashrc && source $HOME/.bashrc
                                      
                                      
                                      conda config --set always_yes yes --set changeps1 no
                                      conda config -f --add channels conda-forge
                                      
                                      
                                      conda create -n zoo python=3.7 # "zoo" is conda environment name
                                      conda init bash
                                      source activate zoo
                                      conda install python 3.7.0 -c conda-forge orca 
                                      sudo /home/hadoop/conda/envs/zoo/bin/python3.7 -m pip install virtualenv
                                      
                                      “spark.pyspark.python": "/home/hadoop/conda/envs/zoo/bin/python3",
                                      "spark.pyspark.virtualenv.enabled": "true",
                                      "spark.pyspark.virtualenv.type":"native",
                                      "spark.pyspark.virtualenv.bin.path":"/home/hadoop/conda/envs/zoo/bin/,
                                      "zeppelin.pyspark.python" : "/home/hadoop/conda/bin/python",
                                      "zeppelin.python": "/home/hadoop/conda/bin/python"
                                      

                                      ERROR: Could not build wheels for pycairo, which is required to install pyproject.toml-based projects

                                      copy iconCopydownload iconDownload
                                      apt-get install sox ffmpeg libcairo2 libcairo2-dev
                                      apt-get install texlive-full
                                      pip3 install manimlib  # or pip install manimlib
                                      
                                      pip3 install manimce  # or pip install manimce
                                      
                                      apt-get install sox ffmpeg libcairo2 libcairo2-dev
                                      apt-get install texlive-full
                                      pip3 install manimlib  # or pip install manimlib
                                      
                                      pip3 install manimce  # or pip install manimce
                                      

                                      How to create a progress bar for iterations happening within installed modules

                                      copy iconCopydownload iconDownload
                                      class tqdm_array(np.ndarray):
                                          def __iter__(self):
                                              return iter(tqdm.tqdm(np.asarray(self)))
                                      
                                      labels = np.array(segm_image.labels).view(tqdm_array)
                                      
                                      class tqdm_array(np.ndarray):
                                          def __iter__(self):
                                              return iter(tqdm.tqdm(np.asarray(self)))
                                      
                                      labels = np.array(segm_image.labels).view(tqdm_array)
                                      

                                      Community Discussions

                                      Trending Discussions on tqdm
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                                      • tf2.0: Gradient Tape returns None gradient in RNN model
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                                      • How to speed up async requests in Python
                                      • ModuleNotFoundError: No module named 'milvus'
                                      • Colab: (0) UNIMPLEMENTED: DNN library is not found
                                      • Cannot find conda info. Please verify your conda installation on EMR
                                      • ERROR: Could not build wheels for pycairo, which is required to install pyproject.toml-based projects
                                      Trending Discussions on tqdm

                                      QUESTION

                                      Get the postfix string in tqdm

                                      Asked 2022-Apr-08 at 13:57

                                      I have a tqdm progressbar. I set the postfix string using the method set_postfix_str in some part of my code. In another part, I need to append to this string. Here is an MWE.

                                      import numpy as np
                                      from tqdm import tqdm
                                      
                                      a = np.random.randint(0, 10, 10)
                                      loop_obj = tqdm(np.arange(10))
                                      
                                      for i in loop_obj:
                                          loop_obj.set_postfix_str(f"Current count: {i}")
                                          a = i*2/3  # Do some operations
                                          loop_obj.set_postfix_str(f"After processing: {a}")  # clears the previous string
                                          
                                          # What I want
                                          loop_obj.set_postfix_str(f"Current count: {i}After processing: {a}")
                                      

                                      Is there a way to append to the already set string using set_postfix_str?

                                      ANSWER

                                      Answered 2022-Apr-08 at 13:57

                                      You could just append the new postfix to the old one like so:

                                      import numpy as np
                                      from tqdm import tqdm
                                      
                                      a = np.random.randint(0, 10, 10)
                                      loop_obj = tqdm(np.arange(10))
                                      
                                      for i in loop_obj:
                                          loop_obj.set_postfix_str(f"Current count: {i}")
                                          a = i*2/3  # Do some operations
                                          loop_obj.set_postfix_str(loop_obj.postfix + f" After processing: {a}")
                                      

                                      Source https://stackoverflow.com/questions/71797809

                                      Community Discussions, Code Snippets contain sources that include Stack Exchange Network

                                      Vulnerabilities

                                      No vulnerabilities reported

                                      Install tqdm

                                      You can install using 'pip install tqdm' or download it from GitHub, PyPI.
                                      You can use tqdm like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

                                      Support

                                      For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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