You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Q:

Search through directories for specific Excel files and compare data from these files with inputvalues

The task:
The firm I have gotten a summer-job for has an expanding test-database that consists of an increasing number of subfolders for each project, that includes everything from .jpeg files to the .xlsx's I am interested in. As I am a bit used to Python from earlier, I decided to give it a go at this task. I want to search for exceldocuments that has "test spreadsheet" as a part of its title(for example "test spreadsheet model259"). All the docs I am interested in are built the same way(weight is always "A3" etc), looking somewhat like this: 
Model:             259 
Lenght:   meters    27
Weight:   kg      2500
Speed:    m/s       25

I want the user of the finished program to be able to compare results from different tests with each other using my script. This means that the script must see if there is an x-value that fits both criteria at once:
inputlength = x*length of model 259
inputweight = x*weight of model 259

The program should loop through all the files in the main folder. If such an X exists for a model, I want the program to return it to a list of fitting models. The x-value will be a variable, different for each model. 
As the result I want a list of all files that fits the input, their scale(x-value) and possibly a link to the file.
For example:
Model     scale   Link
ModelA    21.1    link_to_fileA
ModelB    0.78    link_to_fileB

The script
The script I have tried to get to work so far is below, but if you have other suggestions of how to deal with the task I'll happily accept them. Don't be afraid to ask if I have not explained the task well enough. XLRD is already installed, and I use Eclipse as my IDE. I've been trying to get it to work in many ways now, so most of my script is purely for testing. 
Edited: 
#-*- coding: utf-8 -*-
#Accepts norwegian letters

import xlrd, os, fnmatch

folder = 'C:\eclipse\TST-folder'

  def excelfiles(pattern):
    file_list = []
    for root, dirs, files in os.walk(start_dir):
        for filename in files:
            if fnmatch.fnmatch(filename.lower(), pattern):
                if filename.endswith(".xls") or filename.endswith(".xlsx") or filename.endswith(".xlsm"):
                    file_list.append(os.path.join(root, filename))
    return file_list

file_list = excelfiles('*tst*')     # only accept docs hwom title includes tst

print excelfiles() 

How come I only get one result when I am printing excelfiles() after returning the values, but when I exchange "return os.path.join(filename)" with "print os.path.join(filename)"  it shows all .xls files? Does this mean that the results from the excelfiles-function is not passed on? Answered in comments
''' Inputvals '''
inputweight = int(raw_input('legg inn vekt')) #inputbox for weight
inputlength = int(raw_input('legg inn lengd')) #inputbox for lenght
inputspeed = int(raw_input('legg inn hastighet')) #inputbox for speed

    '''Location of each val from the excel spreadsheet'''
    def locate_vals():
    val_dict = {}
    for filename in file_list:
        wb = xlrd.open_workbook(os.path.join(start_dir, filename))
        sheet = wb.sheet_by_index(0)

        weightvalue = sheet.cell_value(1, 1)
        lenghtvalue = sheet.cell_value(1, 1)
        speedvalue = sheet.cell_value(1, 1)

        val_dict[filename] = [weightvalue, lenghtvalue, speedvalue]

    return val_dict
val_dict = locate_vals()
print locate_vals()

count = 0

Any ideas of how I can read from each of the documents found by the excelfiles-function? "funcdox" does not seem  to work. When I insert a print-test, for example print weightvalue after the weightvalue = sheet.cell(3,3).value function, I get no feedback at all.  Errormessages without the mentioned print-test:Edited to the script above, which creates a list of the different values + minor changes that removed the errormessages
Script works well until this point 
Made some minor changes to the next part. It is supposed to scale an value from the spreadsheet by multiplying it with a constant (x1). Then I want the user to be able to define another inputvalue, which in turn defines another constant(x2) to make the spreadsheetvalue fit. Eventually, these constants will be compared to find which models will actually fit for the test. 
    '''Calculates vals from excel from the given dimensions'''

     def dimension():   # Maybe exchange exec-statement with the function itself.
    if count == 0:
        if inputweight != 0:
            exec scale_weight()
        elif inputlenght != 0:
            exec scale_lenght()
        elif inputspeed != 0:
            exec scale_speed()

def scale_weight(x1, x2):        # Repeat for each value.
    for weightvalue in locate_vals():
        if count == 0:
            x1 * weightvalue == inputweight
            count += 1
            exec criteria2
            return weightvalue, x1
        elif count == 2:
            inputweight2 = int(raw_input('Insert weight')) #inputbox for weight
            x2 * weightvalue == inputweight2
            return weightvalue, x2

The x1 and x2 are what I want to find with this function, so I want them to be totally "free". Is there any way I can test this function without having to insert values for x1 and x2 ? 
def scale_lenght():  # Almost identical to scale_weight
    return

def scale_speed():  # Almost identical to scale_weight
    return

def criteria2(weight, lenght, speed):
    if count == 1:
        k2 = raw_input('Criteria two, write weight, length or speed.')
        if k2 == weight:
            count += 1
            exec scale_weight
        elif k2 == lenght:
            count += 1
            exec scale_lenght
        elif k2 == speed:
            count += 1
            exec scale_speed
        else:
            return

Do you see any easier way to deal with this problem?(Hope I managed to explain it well enough. The way I have written the code so far is quite messy, but since I'm not that experienced I'll just have to make it work first, and then clean it up if I have the time.
Since probably none of the values will exactly fit for both x-constants, I thought I'd use approx_Equal to deal with it:
def approx_Equal(x1, x2, tolerance=int(raw_input('Insert tolerance for scaling difference')),err_msg='Unacceptable tolerance', verbose = True ):  # Gives the approximation for how close the two values of x must be for 
    if x1 == x2:
         x = x1+ (x2-x1)/2
         return x

Eventually, I'd like a diagram of all the variables used + a link-to-file and name for each document.
No sure how I will do this, so any tips are greatly appreciated.
Thanks!

A:

In answer to the first question "How come I only get one result when I am printing excelfiles()" this is because your return statement is within the nested loop, so the function will stop on the first iteration. I would try building up a list instead and then return this list, you could also combine this with the issue of checking the name e.g. :
import os, fnmatch

#globals
start_dir = os.getenv('md')

def excelfiles(pattern):
    file_list = []
    for root, dirs, files in os.walk(start_dir):
        for filename in files:
            if fnmatch.fnmatch(filename.lower(), pattern):
                if filename.endswith(".xls") or filename.endswith(".xlsx") or filename.endswith(".xlsm"):
                    file_list.append(os.path.join(root, filename))
    return file_list

file_list = excelfiles('*cd*')
for i in file_list: print i

Obviously, you'll need to replace the cd with your own search text, but keep the * either side and replace the start_dir with your own. I have done the match on filename.lower() and entered the search text in lower case to make the matching case in-sensitive, just remove the .lower() if you don't want this. I have also allowed for other types of Excel files.
Regarding reading data from Excel files I have done this before to create an automated way of converting basic Excel files into csv format. You are welcome to have a look at the code below and see if there is anything you can use from this. The xl_to_csv function is where the data is read from the Excel file:
import os, csv, sys, Tkinter, tkFileDialog as fd, xlrd

# stop tinker shell from opening as only needed for file dialog
root = Tkinter.Tk()
root.withdraw()

def format_date(dt):
    yyyy, mm, dd = str(dt[0]), str(dt[1]), str(dt[2])
    hh, mi, ss = str(dt[3]), str(dt[4]), str(dt[5])

    if len(mm) == 1:
        mm = '0'+mm
    if len(dd) == 1:
        dd = '0'+dd

    if hh == '0' and mi == '0' and ss == '0':
        datetime_str = dd+'/'+mm+'/'+yyyy
    else:
        if len(hh) == 1:
            hh = '0'+hh
        if len(mi) == 1:
            mi = '0'+mi
        if len(ss) == 1:
            ss = '0'+ss
        datetime_str = dd+'/'+mm+'/'+yyyy+' '+hh+':'+mi+':'+ss

    return datetime_str

def xl_to_csv(in_path, out_path):
    # set up vars to read file
    wb = xlrd.open_workbook(in_path)
    sh1 = wb.sheet_by_index(0)
    row_cnt, col_cnt = sh1.nrows, sh1.ncols

    # set up vars to write file
    fileout = open(out_path, 'wb')
    writer = csv.writer(fileout)

    # iterate through rows and cols
    for r in range(row_cnt):

        # make list from row data
        row = []
        for c in range(col_cnt):
            #print "...debug - sh1.cell(",r,c,").value set to:", sh1.cell(r,c).value
            #print "...debug - sh1.cell(",r,c,").ctype set to:", sh1.cell(r,c).ctype

            # check data type and make conversions
            val = sh1.cell(r,c).value
            if sh1.cell(r,c).ctype == 2: # number data type
                if val == int(val):
                    val = int(val) # convert to int if only no decimal other than .0
                #print "...debug - res 1 (float to str), val set to:", val
            elif sh1.cell(r,c).ctype == 3: # date fields
                dt = xlrd.xldate_as_tuple(val, 0) # date no from excel to dat obj
                val = format_date(dt)
                #print "...debug - res 2 (date to str), val set to:", val
            elif sh1.cell(r,c).ctype == 4: # boolean data types
                val = str(bool(val)) # convert 1 or 0 to bool true / false, then string
                #print "...debug - res 3 (bool to str), val set to:", val
            else:
                val = str(val)
                #print "...debug - else, val set to:", val

            row.append(val)
            #print ""

        # write row to csv file
        try:
            writer.writerow(row)
        except:
            print '...row failed in write to file:', row
            exc_type, exc_value, exc_traceback = sys.exc_info()
            lines = traceback.format_exception(exc_type, exc_value, exc_traceback)
            for line in lines:
                print '!!', line

    print 'Data written to:', out_path, '\n'

def main():
    in_path, out_path = None, None

    # set current working directory to user's my documents folder
    os.chdir(os.path.join(os.getenv('userprofile'),'documents'))

    # ask user for path to Excel file...
    while not in_path:
        print "Please select the excel file to read data from ..."
        try:
            in_path = fd.askopenfilename()
        except:
            print 'Error selecting file, please try again.\n'

    # get dir for output...
    same = raw_input("Do you want to write the output to the same directory? (Y/N): ")
    if same.upper() == 'Y':
        out_path = os.path.dirname(in_path)
    else:
        while not out_path:
            print "Please select a directory to write the csv file to ..."
            try:
                out_path = fd.askdirectory()
            except:
                print 'Error selecting file, please try again.\n'

    # get file name and join to dir
    f_name = os.path.basename(in_path)
    f_name = f_name[:f_name.find('.')]+'.csv'
    out_path = os.path.join(out_path,f_name)

    # get data from file and write to csv...
    print 'Attempting read data from', in_path
    print ' and write csv data to', out_path, '...\n'
    xl_to_csv(in_path, out_path)

    v_open = raw_input("Open file (Y/N):").upper()
    if v_open == 'Y':
        os.startfile(out_path)
    sys.exit()

if __name__ == '__main__':
    main()

Let me know if you have any questions on this.
Finally, regarding the output I would consider writing this out to a html file in a table format. Let me know if you want any help with this, I will have some more sample code that you could use part of.
UPDATE
Here is some further advice on writing your output to a html file. Here is a function that I have written and used previously for this purpose. Let me know if you need any guidance on what you would need to change for your implementation (if anything). The function expects a nested object in the data argument e.g. a list of lists or list of tuples etc. but should work for any number of rows / columns:
def write_html_file(path, data, heads):
    html = []
    tab_attr = ' border="1" cellpadding="3" style="background-color:#FAFCFF; text-align:right"'
    head_attr = ' style="background-color:#C0CFE2"'

    # opening lines needed for html table
    try:
        html.append('<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" ')
        html.append('"http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"> ')
        html.append('<html xmlns="http://www.w3.org/1999/xhtml">')
        html.append('<body>')
        html.append('  <table'+tab_attr+'>')
    except:
        print 'Error setting up html heading data'

    # html table headings (if required)
    if headings_on:
        try:
            html.append('    <tr'+head_attr+'>')
            for item in heads:
                html.append(' '*6+'<th>'+str(item)+'</th>')
            html.append('    </tr>')
        except:
            exc_type, exc_value, exc_traceback = sys.exc_info()
            lines = traceback.format_exception(exc_type, exc_value, exc_traceback)
            print 'Error writing html table headings:'
            print ''.join('!! ' + line for line in lines)

    # html table content
    try:
        for row in data:
            html.append('    <tr>')
            for item in row:
                html.append(' '*6+'<td>'+str(item)+'</td>')
            html.append('    </tr>')
    except:
        print 'Error writing body of html data'

    # closing lines needed
    try:
        html.append('  </table>')
        html.append('</body>')
        html.append('</html>')
    except:
        print 'Error closing html data'

    # write html data to file
    fileout = open(path, 'w')
    for line in html:
        fileout.write(line)

    print 'Data written to:', path, '\n'

    if sql_path:
        os.startfile(path)
    else:
        v_open = raw_input("Open file (Y/N):").upper()
        if v_open == 'Y':
            os.startfile(path)

headings_on is a global that I have set to True in my script, you will also need to import traceback for the error handling to work as it is currently specified.