cleaned test files; added argv and stdin to initial prompt, if anythi…

…ngs is there

src/chatdbg/chatdbg_pdb.py

@@ -8,14 +8,16 @@
 import sys
 import textwrap
 import traceback
-from io import StringIO
+from io import StringIO, TextIOWrapper
 from pathlib import Path
 from pprint import pprint
 
 import IPython
 import llm_utils
 from traitlets import TraitError
 
+from chatdbg.ipdb_util.capture import CaptureInput
+
 from .assistant.assistant import Assistant
 from .ipdb_util.config import Chat
 from .ipdb_util.logging import ChatDBGLog, CopyingTextIOWrapper
@@ -84,11 +86,13 @@ def __init__(self, *args, **kwargs):
         self._assistant = None
         self._history = []
         self._error_specific_prompt = ""
-
+    
         global chatdbg_config
         if chatdbg_config == None:
             chatdbg_config = Chat()
 
+        sys.stdin = CaptureInput(sys.stdin)
+
         # Only use flow when we are in jupyter or using stdin in ipython.   In both
         # cases, there will be no python file at the start of argv after the
         # ipython commands.
@@ -526,6 +530,11 @@ def _build_prompt(self, arg, conversing):
         if not conversing:
             stack_dump = f"The program has this stack trace:\n```\n{self.format_stack_trace()}\n```\n\n"
             prompt = "\n" + stack_dump + self._error_specific_prompt
+            if len(sys.argv) > 1:
+                prompt += f"\nThese were the command line options:\n```\n{' '.join(sys.argv)}\n```\n"
+            input = sys.stdin.get_captured_input()
+            if len(input) > 0:
+                prompt += f"\nThis was the program's input :\n```\n{input}```\n"
 
         if len(self._history) > 0:
             hist = textwrap.indent(self._capture_onecmd("hist"), "")
@@ -578,6 +587,8 @@ def do_mark(self, arg):
         else:
             self._log.add_mark(arg)
 
+
+
     def do_config(self, arg):
         args = arg.split()
         if len(args) == 0:

src/chatdbg/ipdb_util/capture.py

@@ -0,0 +1,31 @@
+from io import StringIO, TextIOWrapper
+
+class CaptureInput:
+    def __init__(self, input_stream):
+        input_stream = TextIOWrapper(input_stream.buffer, encoding='utf-8', newline='')
+
+        self.original_input = input_stream
+        self.capture_buffer = StringIO()
+        self.original_readline = input_stream.buffer.raw.readline
+
+        def custom_readline(*args, **kwargs):
+            input_data = self.original_readline(*args, **kwargs)
+            self.capture_buffer.write(input_data.decode())
+            return input_data
+
+        input_stream.buffer.raw.readline = custom_readline
+
+    def readline(self, *args, **kwargs):
+        input_data = self.original_input.readline(*args, **kwargs)
+        self.capture_buffer.write(input_data)
+        self.capture_buffer.flush()
+        return input_data
+
+    def read(self, *args, **kwargs):
+        input_data = self.original_input.read(*args, **kwargs)
+        self.capture_buffer.write(input_data)
+        self.capture_buffer.flush()
+        return input_data
+
+    def get_captured_input(self):
+        return self.capture_buffer.getvalue()

test/python/bootstrap2.py

@@ -0,0 +1,21 @@
+from datascience import *
+from ds101 import *
+
+def make_marble_sample():
+    table = Table().read_table('marble-sample.csv')
+    return table.column('color')
+
+def proportion_blue(sample):
+    return np.count_nonzero(sample == 'B') / len(sample)
+
+def resampled_stats(observed_marbles, num_trials):
+    stats = bootstrap_statistic(observed_marbles,
+                                proportion_blue,
+                                num_trials)
+    assert len(stats) == num_trials
+    return stats
+
+observed_marbles = make_marble_sample()
+stats = resampled_stats(observed_marbles, 5)
+
+assert np.isclose(np.mean(stats), 0.7)

test/python/ds101.py

@@ -0,0 +1,34 @@
+import numpy as np
+from datascience import *
+
+# fake library to hide identities...
+
+def bootstrap_statistic(observed_sample, compute_statistic, num_trials): 
+    """
+    Creates num_trials resamples of the initial sample.
+    Returns an array of the provided statistic for those samples.
+
+    * observed_sample: the initial sample, as an array.
+    
+    * compute_statistic: a function that takes a sample as 
+                         an array and returns the statistic for that
+                         sample. 
+    
+    * num_trials: the number of bootstrap samples to create.
+
+    """
+
+    # Check that observed_sample is an array!
+    if not isinstance(observed_sample, np.ndarray):
+        raise ValueError('The first parameter to bootstrap_statistic must be a sample represented as an array, not a value of type ' + str(type(observed_sample).__name__))
+
+    statistics = make_array()
+
+    for i in np.arange(0, num_trials): 
+        #Key: in bootstrapping we must always sample with replacement 
+        simulated_resample = np.random.choice(observed_sample, len(observed_sample))
+
+        resample_statistic = compute_statistic(simulated_resample)
+        statistics = np.append(statistics, resample_statistic)
+
+    return statistics