updating latest

markov/markov.py

@@ -18,14 +18,14 @@ simNum = 15000
 states = np.array(['L', 'w', 'W'])
 
 # Possible sequences of events
-transitionName = np.array([['LL', 'Lw', 'LW'],
+transitionName = np.array(
-                           ['wL', 'ww', 'wW'],
+    [['LL', 'Lw', 'LW'], ['wL', 'ww', 'wW'], ['WL', 'Ww', 'WW']]
-                           ['WL', 'Ww', 'WW']])
+)
 
 # Probabilities Matrix (transition matrix)
-transitionMatrix = np.array([[0.6, 0.1, 0.3],
+transitionMatrix = np.array(
-                             [0.1, 0.7, 0.2],
+    [[0.6, 0.1, 0.3], [0.1, 0.7, 0.2], [0.2, 0.2, 0.6]]
-                             [0.2, 0.2, 0.6]])
+)
 
 # Starting state - Given as a list of probabilities of starting
 # in each state. To always start at the same state set one of them
@@ -69,9 +69,14 @@ class markov(object):
     currentState: a string indicating the starting state
     steps: an integer determining how many steps (or times) to simulate"""
 
-    def __init__(self, states: np.array, transitionName: np.array,
+    def __init__(
-                 transitionMatrix: np.array, currentState: str,
+        self,
-                 steps: int):
+        states: np.array,
+        transitionName: np.array,
+        transitionMatrix: np.array,
+        currentState: str,
+        steps: int,
+    ):
         super(markov, self).__init__()
         self.states = states
         self.list = list
@@ -110,10 +115,11 @@ class markov(object):
         # To calculate the probability of the stateList
         self.prob = 1
         while i != self.steps:
+            import pdb; pdb.set_trace()  # breakpoint 24e62119 //
             if self.currentState == self.states[0]:
-                self.change = np.random.choice(self.transitionName[0],
+                self.change = np.random.choice(
-                                               replace=True,
+                    self.transitionName[0], replace=True, p=transitionMatrix[0]
-                                               p=transitionMatrix[0])
+                )
                 if self.change == self.transitionName[0][0]:
                     self.prob = self.prob * self.transitionMatrix[0][0]
                     self.stateList.append(self.states[0])
@@ -127,9 +133,9 @@ class markov(object):
                     self.currentState = self.states[2]
                     self.stateList.append(self.states[2])
             elif self.currentState == self.states[1]:
-                self.change = np.random.choice(self.transitionName[1],
+                self.change = np.random.choice(
-                                               replace=True,
+                    self.transitionName[1], replace=True, p=transitionMatrix[1]
-                                               p=transitionMatrix[1])
+                )
                 if self.change == self.transitionName[1][0]:
                     self.prob = self.prob * self.transitionMatrix[1][0]
                     self.currentState = self.states[0]
@@ -143,9 +149,9 @@ class markov(object):
                     self.currentState = self.states[2]
                     self.stateList.append(self.states[2])
             elif self.currentState == self.states[2]:
-                self.change = np.random.choice(self.transitionName[2],
+                self.change = np.random.choice(
-                                               replace=True,
+                    self.transitionName[2], replace=True, p=transitionMatrix[2]
-                                               p=transitionMatrix[2])
+                )
                 if self.change == self.transitionName[2][0]:
                     self.prob = self.prob * self.transitionMatrix[2][0]
                     self.currentState = self.states[0]
@@ -161,8 +167,10 @@ class markov(object):
             i += 1
         print(f'Path Markov Chain took in this iteration: {self.stateList}')
         print(f'End state after {self.steps} steps: {self.currentState}')
-        print(f'Probability of taking these exact steps in this order is:'
+        print(
-              f' {self.prob:.2f} or {self.prob:.2%}\n')
+            f'Probability of taking these exact steps in this order is:'
+            f' {self.prob:.2f} or {self.prob:.2%}\n'
+        )
         return self.stateList
 
 
@@ -196,43 +204,69 @@ def main(*args, **kwargs):
         if initial_dist is not None:
             startingState = np.random.choice(states, p=initial_dist)
             for _ in itertools.repeat(None, simNum):
-                markovChain = markov(states, transitionName,
+                markovChain = markov(
-                                     transitionMatrix, startingState,
+                    states,
-                                     stepTime)
+                    transitionName,
+                    transitionMatrix,
+                    startingState,
+                    stepTime,
+                )
                 list_state.append(markovChain.forecast())
                 startingState = np.random.choice(states, p=initial_dist)
         else:
             for _ in itertools.repeat(None, simNum):
-                markovChain = markov(states, transitionName,
+                markovChain = markov(
-                                     transitionMatrix, startingState,
+                    states,
-                                     stepTime)
+                    transitionName,
+                    transitionMatrix,
+                    startingState,
+                    stepTime,
+                )
                 list_state.append(markovChain.forecast())
     else:
         for _ in range(1, 2):
-            list_state.append(markov(states, transitionName,
+            list_state.append(
-                                     transitionMatrix, startingState,
+                markov(
-                                     stepTime).forecast())
+                    states,
+                    transitionName,
+                    transitionMatrix,
+                    startingState,
+                    stepTime,
+                ).forecast()
+            )
     for list in list_state:
-        if(list[-1] == f'{endState!s}'):
+        if list[-1] == f'{endState!s}':
-            print(f'SUCCESS - path ended in the requested state {endState!s}'
+            print(
-                  f':', list)
+                f'SUCCESS - path ended in the requested state {endState!s}'
+                f':',
+                list,
+            )
             count += 1
         else:
-            print(f'FAILURE - path did not end in the requested state'
+            print(
-                  f' {endState!s}:', list)
+                f'FAILURE - path did not end in the requested state'
+                f' {endState!s}:',
+                list,
+            )
     if setSim is False:
         simNum = 1
-    print(f'\nTherefore the estimated probability of starting in'
+    print(
-          f' {startingState} and finishing in {endState} after '
+        f'\nTherefore the estimated probability of starting in'
-          f'{stepTime} steps is '
+        f' {startingState} and finishing in {endState} after '
-          f'{(count / simNum):.2%}.\n'
+        f'{stepTime} steps is '
-          f'This is calculated by number of success/total steps\n')
+        f'{(count / simNum):.2%}.\n'
+        f'This is calculated by number of success/total steps\n'
+    )
     if transition_matrix_step:
-        print(f'P_{stepTime} is: \n'
+        print(
-              f'{markov.transition_matrix_step(transitionMatrix, stepTime)}\n')
+            f'P_{stepTime} is: \n'
+            f'{markov.transition_matrix_step(transitionMatrix, stepTime)}\n'
+        )
     if stat_dist:
         checker(initial_dist, 'initial distribution')
-        print(f'Stat dist is {markov.stationary_dist(transitionMatrix,initial_dist, stepTime)}')
+        print(
+            f'Stat dist is {markov.stationary_dist(transitionMatrix,initial_dist, stepTime)}'
+        )
 
 
 if __name__ == '__main__':

markov/requirements.txt

@@ -0,0 +1 @@
+numpy==1.17.4

slack-bot/traffic-scraper/prd/16-11-2019_19:57:07.json

@@ -0,0 +1 @@
+[" Let's check the latest updates from Highways England as of 19:57:07! \ud83d\ude93\ud83d\udea8", "There are currently no reported congestion incidents on the M62 \ud83e\udd14", "There are currently no reported accident incidents on the M62 \ud83e\udd14", "Hey Andy, maybe flying would be quicker? \ud83d\ude81"]

slack-bot/traffic-scraper/prd/pullTrafficInfo.py

@@ -6,6 +6,7 @@ import emoji
 import random
 import json
 import os
+from chromedriver_py import binary_path
 
 
 class getTrafficInfo(object):
@@ -69,7 +70,10 @@ class getTrafficInfo(object):
             options = webdriver.ChromeOptions()
             options.add_argument('headless')
             # Start the Chromedriver
-            browser = webdriver.Chrome(options=options)
+            browser = webdriver.Chrome(
+                executable_path=binary_path,
+                options=options,
+            )
             browser.get(url)
             return browser
         else:

slack-bot/traffic-scraper/prd/requirements.txt

@@ -0,0 +1,14 @@
+aiohttp==3.6.2
+async-timeout==3.0.1
+attrs==19.3.0
+certifi==2019.9.11
+chardet==3.0.4
+chromedriver-py==79.0.3945.16
+emoji==0.5.4
+idna==2.8
+multidict==4.5.2
+requests==2.22.0
+selenium==3.141.0
+slackclient==2.3.1
+urllib3==1.25.7
+yarl==1.3.0

slack-bot/traffic-scraper/prd/slackBot1.py

@@ -32,6 +32,7 @@ accidentMessage = None
 congestionMessageStart = None
 congestionMessageEnd = None
 accidentMessageStart = None
+
 accidentMessageEnd = None
 
 messageTitle = data[0]

slack-bot/traffic-scraper/prd/slackBot2.py

@@ -0,0 +1,26 @@
+import slack
+
+
+@slack.RTMClient.run_on(event='message')
+def say_hello(**payload):
+    data = payload['data']
+    web_client = payload['web_client']
+    rtm_client = payload['rtm_client']
+    if 'Hello' in data.get('text', []):
+        channel_id = data['channel']
+        thread_ts = data['ts']
+        user = data['user']
+
+        web_client.chat_postMessage(
+            channel=channel_id, text=f"Hi <@{user}>!", thread_ts=thread_ts
+        )
+
+
+slack_token = (
+    'xoxp-2443045155-231150911092-792886570180-0cf573954c0eeb7f40c7'
+    '8bc6f5c959db'
+)
+
+# slack_token = os.environ["SLACK_API_TOKEN"]
+rtm_client = slack.RTMClient(token=slack_token)
+rtm_client.start()